Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis

OBJECTIVE

To assess the acknowledgement and mitigation of sex bias within studies using supervised machine learning (ML) for improving clinical outcomes in rheumatoid arthritis (RA).

DESIGN

A systematic review of original studies published in English between 2018 and November 2023.

DATA SOURCES

PUBMED and EMBASE databases.

STUDY SELECTION

Studies were selected based on their use of supervised ML in RA and their publication within the specified date range.

DATA EXTRACTION AND SYNTHESIS

Papers were scored on whether they reported, attempted to mitigate or successfully mitigated various types of bias: training data bias, test data bias, input variable bias, output variable bias and analysis bias. The quality of ML research in all papers was also assessed.

RESULTS

Out of 52 papers included in the review, 51 had a female skew in their study participants. However, 42 papers did not acknowledge any potential sex bias. Only three papers assessed bias in model performance by sex disaggregating their results. Potential sex bias in input variables was acknowledged in one paper, while six papers commented on sex bias in their output variables, predominantly disease activity scores. No paper attempted to mitigate any type of sex bias.

CONCLUSIONS

The findings demonstrate the need for increased promotion of inclusive and equitable ML practices in healthcare to address unchecked sex bias in ML algorithms.

PROSPERO REGISTRATION NUMBER

CRD42023431754.

Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence

Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, and immunology data. This comprehensive review explores the clinical applications of AI-driven analytics in unlocking personalized insights for patients with autoimmune rheumatic diseases. Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. AI-enabled immunology data analysis, including dimensionality reduction, cell population identification, and sample classification, provides unprecedented insights into complex immune responses. The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. The future of precision medicine lies in the continued research, development, and clinical integration of AI-driven strategies to unlock personalized patient care and drive innovation in rheumatology.

Identification of new hub- ferroptosis-related genes in Lupus Nephritis

Background: Lupus Nephritis (LN) is the primary causation of kidney injury in systemic lupus erythematosus (SLE). Ferroptosis is a programmed cell death. Therefore, understanding the crosstalk between LN and ferroptosis is still a significant challenge. Methods: We obtained the expression profile of LN kidney biopsy samples from the Gene Expression Omnibus database and utilised the R-project software to identify differentially expressed genes (DEGs). Then, we conducted a functional correlation analysis. Ferroptosis-related genes (FRGs) and differentially expressed genes (DEGs) crossover to select FRGs with LN. Afterwards, we used CIBERSORT to assess the infiltration of immune cells in both LN tissues and healthy control samples. Finally, we performed immunohistochemistry on LN human renal tissue. Results: 10619 DEGs screened from the LN biopsy tissue were identified. 22 hub-ferroptosis-related genes with LN (FRGs-LN) were screened out. The CIBERSORT findings revealed that there were significant statistical differences in immune cells between healthy control samples and LN tissues. Immunohistochemistry further demonstrated a significant difference in HRAS, TFRC, ATM, and SRC expression in renal tissue between normal and control groups. Conclusion: We developed a signature that allowed us to identify 22 new biomarkers associated with FRGs-LN. These findings suggest new insights into the pathology and therapeutic potential of LN ferroptosis inhibitors and iron chelators.

Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry

Background

Rheumatoid arthritis (RA) is a common condition treated with biological disease-modifying anti-rheumatic medicines (bDMARDs). However, many patients exhibit resistance, necessitating the use of machine learning models to predict remissions in patients treated with bDMARDs, thereby reducing healthcare costs and minimizing negative effects.

Objective

The study aims to develop machine learning models using data from the Kuwait Registry for Rheumatic Diseases (KRRD) to identify clinical characteristics predictive of remission in RA patients treated with biologics.

Methods

The study collected follow-up data from 1,968 patients treated with bDMARDs from four public hospitals in Kuwait from 2013 to 2022. Machine learning techniques like lasso, ridge, support vector machine, random forest, XGBoost, and Shapley additive explanation were used to predict remission at a 1-year follow-up.

Results

The study used the Shapley plot in explainable Artificial Intelligence (XAI) to analyze the effects of predictors on remission prognosis across different types of bDMARDs. Top clinical features were identified for patients treated with bDMARDs, each associated with specific mean SHAP values. The findings highlight the importance of clinical assessments and specific treatments in shaping treatment outcomes.

Conclusion

The proposed machine learning model system effectively identifies clinical features predicting remission in bDMARDs, potentially improving treatment efficacy in rheumatoid arthritis patients.

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective

INTRODUCTION

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

AREAS COVERED

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

EXPERT OPINION

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

Meniscus injury prediction model based on metric learning

A meniscus injury is a prevalent condition affecting the knee joint. The construction of a subjective prediction model for meniscus injury represents a potentially invaluable diagnostic tool for physicians. Nevertheless, given the variability of pathological manifestations among individual patients, machine learning-based models may produce errors when attempting to predict specific medical records. In order to mitigate this issue, the present study suggests the incorporation of metric learning within the machine learning (ML) modelling process, with the aim of reducing the intra-class spacing of comparable samples and thereby enhancing the classification accuracy of individual medical records. This work has not yet been attempted in the field of knee joint prediction. The findings demonstrate that the adoption of metric learning produces better optimal outcomes. Compared to machine learning baseline models, F1 was increased by 2%.

Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis

Introduction

This study conducts a retrospective analysis on patients with BCLC stage A/B hepatocellular carcinoma (HCC) accompanied by Child-Pugh B cirrhosis, who underwent transarterial chemoembolization (TACE) in combination with local ablation therapy. Our goal was to uncover risk factors contributing to post-treatment recurrence and to develop and validate an innovative 1-, 3-, and 5-year recurrence free survival (RFS) nomogram.

Methods

Data from 255 BCLC A/B HCC patients with Child-Pugh B cirrhosis treated at Beijing You'an Hospital (January 2014 - January 2020) were analyzed using random survival forest (RSF), LASSO regression, and multivariate Cox regression to identify independent risk factors for RFS. The prognostic nomogram was then constructed and validated, categorizing patients into low, intermediate, and high-risk groups, with RFS assessed using Kaplan-Meier curves.

Results

The nomogram, integrating the albumin/globulin ratio, gender, tumor number, and size, showcased robust predictive performance. Harrell's concordance index (C-index) values for the training and validation cohorts were 0.744 (95% CI: 0.703-0.785) and 0.724 (95% CI: 0.644-0.804), respectively. The area under the curve (AUC) values for 1-, 3-, and 5-year RFS in the two cohorts were also promising. Calibration curves highlighted the nomogram's reliability and decision curve analysis (DCA) confirmed its practical clinical benefits. Through meticulous patient stratification, we also revealed the nomogram's efficacy in distinguishing varying recurrence risks.

Conclusion

This study advances recurrence prediction in BCLC A/B HCC patients with Child-Pugh B cirrhosis following TACE combined with ablation. The established nomogram accurately predicts 1-, 3-, and 5-year RFS, facilitating timely identification of high-risk populations.

Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases

OBJECTIVE

Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators.

METHODS

A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model.

RESULTS

Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles.

CONCLUSION

This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs)

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

The Digital Way to Intercept Psoriatic Arthritis

Psoriasis (PsO) and Psoriatic Arthritis (PsA) are chronic, immune-mediated diseases that share common etiopathogenetic pathways. Up to 30% of PsO patient may later develop PsA. In nearly 75% of cases, skin psoriatic lesions precede arthritic symptoms, typically 10 years prior to the onset of joint symptoms, while PsO diagnosis occurring after the onset of arthritis is described only in 15% of cases. Therefore, skin involvement offers to the rheumatologist a unique opportunity to study PsA in a very early phase, having a cohort of psoriatic “risk patients” that may develop the disease and may benefit from preventive treatment. Progression from PsO to PsA is often characterized by non-specific musculoskeletal symptoms, subclinical synovio-entheseal inflammation, and occasionally asymptomatic digital swelling such as painless toe dactylitis, that frequently go unnoticed, leading to diagnostic delay. The early diagnosis of PsA is crucial for initiating a treatment prior the development of significant and permanent joint damage. With the ongoing development of pharmacological treatments, early interception of PsA has become a priority, but many obstacles have been reported in daily routine. The introduction of digital technology in rheumatology may fill the gap in the physician-patient relationship, allowing more targeted monitoring of PsO patients. Digital technology includes telemedicine, virtual visits, electronic health record, wearable technology, mobile health, artificial intelligence, and machine learning. Overall, this digital revolution could lead to earlier PsA diagnosis, improved follow-up and disease control as well as maximizing the referral capacity of rheumatic centers.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

The Challenges of Telemedicine in Rheumatology

During the past 20 years, the development of telemedicine has accelerated due to the rapid advancement and implementation of more sophisticated connected technologies. In rheumatology, e-health interventions in the diagnosis, monitoring and mentoring of rheumatic diseases are applied in different forms: teleconsultation and telecommunications, mobile applications, mobile devices, digital therapy, and artificial intelligence or machine learning. Telemedicine offers several advantages, in particular by facilitating access to healthcare and providing personalized and continuous patient monitoring. However, some limitations remain to be solved, such as data security, legal problems, reimbursement method, accessibility, as well as the application of recommendations in the development of the tools.

Machine Learning in Rheumatic Diseases

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus

OBJECTIVES

Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.

METHODS

From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).

RESULTS

A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.

CONCLUSIONS

We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus

Objectives

Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.

Methods

From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).

Results

A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.

Conclusions

We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients

BACKGROUND

At the patient level, the prognostic value of several features that are known to be associated with an increased risk of converting from relapsing-remitting (RR) to secondary phase (SP) multiple sclerosis (MS) remains limited.

METHODS

Among 262 RRMS patients followed up for 10 years, we assessed the probability of developing the SP course based on clinical and conventional and non-conventional magnetic resonance imaging (MRI) parameters at diagnosis and after 2 years. We used a machine learning method, the random survival forests, to identify, according to their minimal depth (MD), the most predictive factors associated with the risk of SP conversion, which were then combined to compute the secondary progressive risk score (SP-RiSc).

RESULTS

During the observation period, 69 (26%) patients converted to SPMS. The number of cortical lesions (MD = 2.47) and age (MD = 3.30) at diagnosis, the global cortical thinning (MD = 1.65), the cerebellar cortical volume loss (MD = 2.15) and the cortical lesion load increase (MD = 3.15) over the first 2 years exerted the greatest predictive effect. Three patients' risk groups were identified; in the high-risk group, 85% (46/55) of patients entered the SP phase in 7 median years. The SP-RiSc optimal cut-off estimated was 17.7 showing specificity and sensitivity of 87% and 92%, respectively, and overall accuracy of 88%.

CONCLUSIONS

The SP-RiSc yielded a high performance in identifying MS patients with high probability to develop SPMS, which can help improve management strategies. These findings are the premise of further larger prospective studies to assess its use in clinical settings.

Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model

Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called "MobileNetV2," which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.

Identifying chronic disease patients using predictive algorithms in pharmacy administrative claims: an application in rheumatoid arthritis

OBJECTIVE

To evaluate the predictive performance of logistic and linear regression versus machine learning (ML) algorithms to identify patients with rheumatoid arthritis (RA) treated with target immunomodulators (TIMs) using only pharmacy administrative claims.

METHODS

Adults aged 18-64 years with ≥1 TIM claim in the IBM MarketScan commercial database were included in this retrospective analysis. The predictive ability of logistic regression to identify RA patients was compared with supervised ML classification algorithms including random forest (RF), decision trees, linear support vector machines (SVMs), neural networks, naïve Bayes classifier, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbors (k-NN). Model performance was evaluated using F1 score, accuracy, precision, sensitivity, area under the receiver operating characteristic curve (AUROC), and Matthews correlation coefficient (MCC). Analyses were conducted in all-patient and etanercept-only samples.

RESULTS

In the all-patients sample, ML approaches did not outperform logistic regression. RF showed small improvements versus logistic regression that were not considered remarkable, respectively: F1 score (84.55% vs 83.96%), accuracy (84.05% vs 83.79%), sensitivity (84.53% vs 82.20%), AUROC (84.04% vs 83.85%), and MCC (68.07% vs 67.66%). Findings were similar in the etanercept samples.

CONCLUSION

Logistic regression and ML approaches successfully identified patients with RA in a large pharmacy administrative claims database. The ML algorithms were no better than logistic regression at prediction. RF, SVMs, LDA, and ridge classifier showed comparable performance, while neural networks, decision trees, naïve Bayes classifier, and QDA underperformed compared with logistic regression in identifying patients with RA.

A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue

Purpose: Nutritional intervention was always implemented based on "one-size-fits-all" recommendation instead of personalized strategy. We aimed to develop a machine learning based model to predict the optimal dose of a botanical combination of lutein ester, zeaxanthin, extracts of black currant, chrysanthemum, and goji berry for individuals with eye fatigue. Methods: 504 features, including demographic, anthropometrics, eye-related indexes, blood biomarkers, and dietary habits, were collected at baseline from 303 subjects in a randomized controlled trial. An aggregated score of visual health (VHS) was developed from total score of eye fatigue symptoms, visuognosis persistence, macular pigment optical density, and Schirmer test to represent an overall eye fatigue level. VHS at 45 days after intervention was predicted by XGBoost algorithm using all features at baseline to show the eye fatigue improvement. Optimal dose of the combination was chosen based on the predicted VHS. Results: After feature selection and parameter optimization, a model was trained and optimized with a Pearson's correlation coefficient of 0.649, 0.638, and 0.685 in training, test and validation set, respectively. After removing the features collected by invasive blood test and costly optical coherence tomography, the model remained good performance. Among 58 subjects in test and validation sets, 39 should take the highest dose as the optimal option, 17 might take a lower dose, while 2 could not benefit from the combination. Conclusion: We applied XGBoost algorithm to develop a model which could predict optimized dose of the combination to provide personalized nutrition solution for individuals with eye fatigue.

Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis

We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early-TNF and non-early-TNF users. Machine learning models were formulated to predict the early-TNF users using the baseline data. Feature importance analysis was performed to delineate significant baseline characteristics. The numbers of early-TNF and non-early-TNF users were 90 and 505, respectively. The performance of the ANN model, based on the area under curve (AUC) for a receiver operating characteristic curve (ROC) of 0.783, was superior to logistic regression, support vector machine, random forest, and XGBoost models (for an ROC curve of 0.719, 0.699, 0.761, and 0.713, respectively) in predicting early-TNF users. Feature importance analysis revealed CRP and ESR as the top significant baseline characteristics for predicting early-TNF users. Our model displayed superior performance in predicting early-TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.

Quantitative Imaging in Inflammatory Arthritis: Between Tradition and Innovation

Radiologic imaging is crucial for diagnosing and monitoring rheumatic inflammatory diseases. Particularly the emerging approach of precision medicine has increased the interest in quantitative imaging. Extensive research has shown that ultrasound allows a quantification of direct signs such as bone erosions and synovial thickness. Dual-energy X-ray absorptiometry and high-resolution peripheral quantitative computed tomography (CT) contribute to the quantitative assessment of secondary signs such as osteoporosis or lean mass loss. Magnetic resonance imaging (MRI), using different techniques and sequences, permits in-depth evaluations. For instance, the perfusion of the inflamed synovium can be quantified by dynamic contrast-enhanced imaging or diffusion-weighted imaging, and cartilage injury can be assessed by mapping (T1ρ, T2). Furthermore, the increased metabolic activity characterizing the inflammatory response can be reliably assessed by hybrid imaging (positron emission tomography [PET]/CT, PET/MRI). Finally, advances in intelligent systems are pushing forward quantitative imaging. Complex mathematical algorithms of lesions' segmentation and advanced pattern recognition are showing promising results.

Can the adherence to quality of care indicators for early rheumatoid arthritis in clinical practice reduce risk of hospitalisation? Retrospective cohort study based on the Record Linkage of Rheumatic Disease study of the Italian Society for Rheumatology

OBJECTIVE

To describe the adherence to quality of care indicators in early rheumatoid arthritis (RA) and to evaluate its impact on the risk of hospitalisation in a real-world setting.

DESIGN

Retrospective cohort study.

SETTING

Patients with early-onset RA identified from healthcare regional administrative databases by means of a validated algorithm between 2006 and 2012 in the Lombardy region (Italy).

PARTICIPANTS

The study cohort included 14 203 early-onset RA (71% female, mean age 60 years).

OUTCOME MEASURES

For each patient, a summary adherence score was calculated starting from the compliance to six quality indicators: (1-2) methotrexate or sulfasalazine or leflunomide with/without glucocorticoids, (3-4) other disease-modifying antirheumatic drugs (DMARDs) with/without glucocorticoids, (5) early interruption of glucocorticoids, (6) early clinical assessment.The relationship between low, intermediate and high categories of the summary score and the 12-month risk of hospitalisation for all causes and for RA was assessed.

RESULTS

During a follow-up of 1 year, 2609 hospitalisations occurred, of which 704 were for RA (main or secondary diagnosis) and 252 primarily for RA. In a 7-year period (2006-2012), early DMARDs and timely clinical monitoring treatment increased (from 52% to 62% p trend <0.001 and from 25% to 30% p trend 0.009, respectively).Intermediate and high summary adherence score categories (compared with the low category) were related significantly with a lower risk of hospitalisation (adjusted HR 0.85 (95% CI 0.77 to 0.93), p<0.001 and HR 0.76 (95% CI 0.69 to 0.84), p<0.001, respectively). Among the indicators of the adherence score, early DMARD prescription showed the strongest positive impact, while long-term use of glucocorticoids was the worst negative one.

CONCLUSION

In early RA, adherence to quality standards of care is associated with a lower risk of hospitalisation. Future interventions to improve the adherence to quality standards of care in this setting should decrease the risk of hospitalisation with a significant impact on individual and population health.

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Digital health technologies: opportunities and challenges in rheumatology

The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care. This Review discusses key barriers and opportunities to improve application of digital health technologies in rheumatology. Key topics include smart design, voice enablement and the integration of electronic patient-reported outcomes. Smart design involves active engagement with the end users of the technologies, including patients and clinicians through focus groups, user testing sessions and prototype review. Voice enablement using voice assistants could be critical for enabling patients with hand arthritis to effectively use smartphone apps and might facilitate patient engagement with many technologies. Tracking many rheumatic diseases requires frequent monitoring of patient-reported outcomes. Current practice only collects this information sporadically, and rarely between visits. Digital health technology could enable patient-reported outcomes to inform appropriate timing of face-to-face visits and enable improved application of treat-to-target strategies. However, best practice standards for digital health technologies do not yet exist. To achieve the potential of digital health technology in rheumatology, rheumatology professionals will need to be more engaged upstream in the technology design process and provide leadership to effectively incorporate the new tools into clinical care.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Applied machine learning and artificial intelligence in rheumatology

Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient’s opinion and the rheumatologist’s empirical and evidence-based experience, but it will also be influenced by machine-learned evidence.

Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.

Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model

OBJECTIVE

Dyslipidemia has been recognized as a major risk factor of several diseases, and early prevention and management of dyslipidemia is effective in the primary prevention of cardiovascular events. The present study aims to develop risk models for predicting dyslipidemia using Random Survival Forest (RSF), which take the complex relationship between the variables into account.

METHODS

We used data from 6328 participants aged between 19 and 90 years free of dyslipidemia at baseline with a maximum follow-up of 5 years. RSF was applied to develop gender-specific risk model for predicting dyslipidemia using variables from anthropometric and laboratory test in the cohort. Cox regression was also adopted in comparison with the RSF model, and Harrell's concordance statistic with 10-fold cross-validation was used to validate the models.

RESULTS

The incidence density of dyslipidemia was 101/1000 in total and subgroup incidence densities were 121/1000 for men and 69/1000 for women. Twenty-four predictors were identified in the prediction model of males and 23 in females. The C-statistics of the prediction models for males and females were 0.731 and 0.801, respectively. The RSF model shows better discriminative performance than CPH model (0.719 for males and 0.787 for females). Moreover, some predictors were observed to have a nonlinear effect on dyslipidemia.

CONCLUSION

The RSF model is a promising method in identifying high-risk individuals for the prevention of dyslipidemia and related diseases.

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative

OBJECTIVE

To find autoantibodies (AAbs) in serum that could be useful to predict incidence of radiographic knee osteoarthritis (KOA).

DESIGN

A Nucleic-acid Programmable Protein Arrays (NAPPA) platform was used to screen AAbs against 2125 human proteins in sera at baseline from participants free of radiographic KOA belonging to the incidence and non-exposed subcohorts of the Osteoarthritis Initiative (OAI) who developed or not, radiographic KOA during a follow-up period of 96 months. NAPPA-ELISA were performed to analyse reactivity against methionine adenosyltransferase two beta (MAT2β) and verify the results in 327 participants from the same subcohorts. The association of MAT2β-AAb levels with KOA incidence was assessed by combining several robust biostatistics analysis (logistic regression, Receiver Operating Characteristic and Kaplan-Meier curves). The proposed prognostic model was replicated in samples from the progression subcohort of the OAI.

RESULTS

In the screening phase, six AAbs were found significantly different at baseline in samples from incident compared with non-incident participants. In the verification phase, high levels of MAT2β-AAb were significantly associated with the future incidence of KOA and with an earlier development of the disease. The incorporation of this AAb in a clinical model for the prognosis of incident radiographic KOA significantly improved the identification/classification of patients who will develop the disorder. The usefulness of the model to predict radiographic KOA was confirmed on a different OAI subcohort.

CONCLUSIONS

The measurement of AAbs against MAT2β in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence.

Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis

INTRODUCTION

Machine learning is applied to characterize the risk and predict outcomes in multi-dimensional data. The prediction of radiographic progression in axial spondyloarthritis (axSpA) remains limited. Hence, we tested the feasibility of supervised machine learning algorithms to predict radiographic progression in axSpA.

METHODS

This is a retrospective and hospital-based study. Clinical and laboratory data obtained from two independent axSpA groups were used as training and testing datasets. Radiographic progression over 2 years was assessed using the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) and mSASSS worsening by ≥ two units was defined as progression. Seven machine learning models with different algorithms were fitted, and their performance for the testing dataset was assessed using receiver-operating characteristic (ROC) and precision-recall (PR) curve.

RESULTS

The training and testing groups had equivalent characteristics, and radiographic progression was identified in 25.3% and 23.7%, respectively. The generalized linear model (GLM) and support vector machine (SVM) were the top two best-performing models with an average area-under-curve (AUC) of ROC of over 0.78. SVM had the higher AUC of PR compared with GLM (0.56 versus 0.51). Balanced accuracy was over 65% in all models. mSASSS was the most informative variable, followed by the presence of syndesmophyte(s) at the baseline and sacroiliac joint grades.

CONCLUSIONS

Clinical and radiographic data-driven predictive models showed reasonable performance in the prediction of radiographic progression in axSpA. Further modelling with larger and more detailed data could provide an excellent opportunity for the clinical translation of the predictive models to the management of high-risk patients.Key Points• Clinical and radiographic data-driven predictive models showed reasonable performance in the prediction of radiographic progression in axSpA.• Further modelling with larger and more detailed data could provide an excellent opportunity for the clinical translation of the predictive models to the management of high-risk patients.

Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning

Background and Aims: Accurately predicting the response to methotrexate (MTX) in juvenile idiopathic arthritis (JIA) patients before administration is the key point to improve the treatment outcome. However, no simple and reliable prediction model has been identified. Here, we aimed to develop and validate predictive models for the MTX response to JIA using machine learning based on electronic medical record (EMR) before and after administering MTX.

Materials and Methods: Data of 362 JIA patients with MTX mono-therapy were retrospectively collected from EMR between January 2008 and October 2018. DAS44/ESR-3 simplified standard was used to evaluate the MTX response. Extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and logistic regression (LR) algorithms were applied to develop and validate models with 5-fold cross-validation on the randomly split training and test set. Data of 13 patients additionally collected were used for external validation.

Results: The XGBoost screened out the optimal 10 pre-administration features and 6 mix-variables. The XGBoost established the best model based on the 10 pre-administration variables. The performances were accuracy 91.78%, sensitivity 90.70%, specificity 93.33%, AUC 97.00%, respectively. Similarly, the XGBoost developed a better model based on the 6 mix-variables, whose performances were accuracy 94.52%, sensitivity 95.35%, specificity 93.33%, AUC 99.00%, respectively.

Conclusion: Based on common EMR data, we developed two MTX response predictive models with excellent performance in JIA using machine learning. These models can predict the MTX efficacy early and accurately, which provides powerful decision support for doctors to make or adjust therapeutic scheme before or after treatment.

Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort

BACKGROUND AND OBJECTIVES

The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. In this study, we developed and validated few computational models to predict anticancer drug efficacy based on molecular, cellular and clinical features of 31 oral squamous cell carcinoma (OSCC) cohort using computational methods.

METHODS

We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms.

RESULTS

The train-test process for the real tumor samples of MLR-WLS method revealed the drug efficacy prediction enhancement and we observed that there was very less priming difference between actual and predicted. Furthermore, we found there was a less difference between actual apoptotic priming and predicted apoptotic priming for the tumors 6, 8, 21 and 30 whereas, for the remaining tumors there were no differences between predicted and actual priming data. The error terms (Actual Vs Predicted) also revealed the reliability of enhanced MLR-WLS model for drug efficacy prediction.

CONCLUSION

We developed effective computational prediction models using MLR analysis for anticancer drug efficacy which will be useful in the field of precision medicine to choose the choice of drug in a personalized manner. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications.

A portfolio of biologic self-injection devices in rheumatology: how patient involvement in device design can improve treatment experience

Biologic drugs (e.g. anti-tumor necrosis factors) are effective treatments for multiple chronic inflammatory diseases including rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis. Administration of biologic drugs is usually via subcutaneous self-injection, which provides many patient benefits compared to infusions including increased flexibility, reduced costs, and reduced caregiver burden. However, it is also associated with challenges such as needle phobia, patient treatment misconceptions and incorrect drug administration, and can be impacted by dexterity problems. Evidence suggests these problems, along with other drug administration challenges (e.g. patient forgetfulness, busy lifestyles, and polypharmacy), can reduce patient adherence to treatment. To combat these challenges, patient feedback has been used to develop a range of self-injection devices, including pre-filled syringes, pre-filled pens, and electronic injection devices. Providing different devices for drug administration gives patients the opportunity to choose a device that addresses the challenges they face as an individual. Research suggests involving patients in medical device development, providing patients with a choice of devices and enrolling individuals in patient support programs can empower patients to take control of their treatment journey. By providing a portfolio of self-injection devices, designed based on patient needs, patient experience will improve, potentially improving adherence and hence, long-term treatment outcomes.

Application of machine learning in rheumatic disease research

Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.

Application of machine learning in the diagnosis of axial spondyloarthritis

PURPOSE OF REVIEW

In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA).

RECENT FINDINGS

In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6-91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing.

SUMMARY

We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.

A Convenient Non-harm Cervical Spondylosis Intelligent Identity method based on Machine Learning

Cervical spondylosis (CS), a most common orthopedic diseases, is mainly identified by the doctor's judgment from the clinical symptoms and cervical change provided by expensive instruments in hospital. Owing to the development of the surface electromyography (sEMG) technique and artificial intelligence, we proposed a convenient non-harm CS intelligent identify method EasiCNCSII, including the sEMG data acquisition and the CS identification. Faced with the limit testable muscles, the data acquisition method are proposed to conveniently and effectively collect data based on the tendons theory and CS etiology. Faced with high-dimension and the weak availability of the data, the 3-tier model EasiAI is developed to intelligently identify CS. The common features and new features are extracted from raw sEMG data in first tier. The EasiRF is proposed in second tier to further reduce the data dimension, improving the performance. A classification model based on gradient boosted regression tree is developed in third tier to identify CS. Compared with 4 common machine learning classification models, the EasiCNCSII achieves best performance of 91.02% in mean accuracy, 97.14% in mean sensitivity, 81.43% in mean specificity, 0.95 in mean AUC.

Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches

OBJECTIVE

The objective of this study was to compare the performance of several commonly used machine learning methods to traditional statistical methods for predicting emergency department and hospital utilization among patients receiving publicly-funded home care services.

STUDY DESIGN AND SETTING

We conducted a population-based retrospective cohort study of publicly-funded home care recipients in the Hamilton-Niagara-Haldimand-Brant region of southern Ontario, Canada between 2014 and 2016. Gradient boosted trees, neural networks, and random forests were tested against two variations of logistic regression for predicting three outcomes related to emergency department and hospital utilization within six months of a comprehensive home care clinical assessment. Models were trained on data from years 2014 and 2015 and tested on data from 2016. Performance was compared using logarithmic score, Brier score, AUC, and diagnostic accuracy measures.

RESULTS

Gradient boosted trees achieved the best performance on all three outcomes. Gradient boosted trees provided small but statistically significant performance gains over both traditional methods on all three outcomes, while neural networks significantly outperformed logistic regression on two of three outcomes. However, sensitivity and specificity gains from using gradient boosted trees over logistic regression were only in the range of 1%-2% at several classification thresholds.

CONCLUSION

Gradient boosted trees and simple neural networks yielded small performance benefits over logistic regression for predicting emergency department and hospital utilization among patients receiving publicly-funded home care. However, the performance benefits were of negligible clinical importance.