Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study

Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning

Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Based on machine learning technology and ensemble learning theory, this study integrates four neural network models to construct an optimal prediction model for daily outpatient visits related to skin diseases. To address the issue of local optima entrapment in sand cat swarm optimization (SCSO), an enhanced SCSO is proposed by incorporating the chaotic mapping, the spiral search strategy, and the sparrow warning mechanism. The enhanced SCSO is then utilized to optimize two critical parameters of variational mode decomposition, enabling the extraction of periodic patterns from the skin disease time series. Finally, the enhanced SCSO is applied again to determine the optimal weights for the ensemble model, thereby achieving optimal fusion predictions. We utilized ten years of outpatient data from the dermatology department of a hospital in China, and selected acne, the most prevalent skin condition in the region, as a case study. Experimental results demonstrate that the proposed model effectively combines the strengths of each module, achieving an root mean squared error (RMSE) of 4.43 and an R-squared (R2) of 0.98. Compared to individual models, the RMSE and R2 are improved by 79.69% and 36.97%, respectively, effectively overcoming the limitations of single-model approaches. This research provides valuable insights for leveraging medical time series data and optimizing healthcare resource allocation.

Understanding sexual homicide in Korea using machine learning algorithms

The current study was conducted to confirm the characteristics in sexual homicide and to explore variables that effectively differentiate sexual homicide and nonsexual homicide. Further, newer methods that have received attention in criminology, such as the machine learning method, were used to explore the ideal algorithm for classifying sexual homicide and patterns for sexual homicide in Korea. To do this, 542 homicide cases were analyzed utilizing eight algorithms, and the classification performance of each algorithm was analyzed along with the importance of variables. The results of the analysis revealed that the Naive Bayes, K-Nearest Neighbors, and RF algorithms demonstrate good classification accuracy, and generally, factors such as relationships, marriage, planning, personal weapons, and overkill were identified as crucial variables that distinguish sexual homicide in Korea. In addition, the crime scene information of the crime occurring in the dark (at night) and body disposal were found to have high importance. The current study proposes ways to enhance the efficacy of crime investigation and advance the research on sexual homicides in Korea through a more scientific understanding of sexual homicide that has not been thoroughly explored domestically.

Epidemiology of lipid disturbances in psoriasis: An analysis of trends from 2006 to 2023

INTRODUCTION

A strong link has been established between psoriasis and lipid disturbances; however, no study has systematically examined their global epidemiology.

METHODS

We searched six databases from their inception up to October 1, 2023. Data analysis was conducted using Stata SE 15.1. We performed subgroup, meta-regression, and sensitivity analyses to assess the heterogeneity of the pooled studies.

RESULTS

Our review included 239 studies comprising 15,519,570 participants. The pooled prevalence rate of dyslipidemia among individuals with psoriasis was 38%.

CONCLUSION

Patients with severe psoriasis should undergo screening for lipid abnormalities. This can facilitate the early detection of lipid dysfunction and associated cardiovascular comorbidities.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Use of coronarycomputed tomography for cardiovascular risk assessment in immune-mediated inflammatory diseases

Immune-mediated inflammatory diseases (IMIDs) are recognised risk factors for accelerated atherosclerotic cardiovascular disease (CVD), particularly in younger individuals and women who lack traditional CVD risk factors. Reflective of the critical role that inflammation plays in the formation, progression and rupture of atherosclerotic plaques, research into immune mechanisms of CVD has led to the identification of a range of therapeutic targets that are the subject of ongoing clinical trials. Several key inflammatory pathways implicated in the pathogenesis of atherosclerosis are targeted in people with IMIDs. However, cardiovascular risk continues to be systematically underestimated by conventional risk assessment tools in the IMID population, resulting in considerable excess CVD burden and mortality. Hence, there is a pressing need to improve methods for CVD risk-stratification among patients with IMIDs, to better guide the use of statins and other prognostic interventions. CT coronary angiography (CTCA) is the current first-line investigation for diagnosing and assessing the severity of coronary atherosclerosis in many individuals with suspected angina. Whether CTCA is also useful in the general population for reclassifying asymptomatic individuals and improving long-term prognosis remains unknown. However, in the context of IMIDs, it is conceivable that the information provided by CTCA, including state-of-the-art assessments of coronary plaque, could be an important clinical adjunct in this high-risk patient population. This narrative review discusses the current literature about the use of coronary CT for CVD risk-stratification in three of the most common IMIDs including rheumatoid arthritis, psoriasis and systemic lupus erythematosus.

Prospective evaluation of cardiovascular risk and mortality in patients with psoriasis: An American population-based study

The association between psoriasis and cardiovascular disease (CVD) has long been discussed and continually refined. However, there is currently a lack of prospective studies on the cardiovascular risk attributed to psoriasis in the United States general population. Representative adult participants were selected from the National Health and Nutrition Examination Survey (NHANES). Risks of cardiovascular symptoms and diseases prevalence were evaluated between participants with and without psoriasis. The hazards for all-cause mortality and CVD mortality were stratified by psoriasis status. Mediation analysis was then conducted to identify potential mediators between psoriasis and cardiac death. Overall, 19 741 participants were included in the current study, 542 (2.7%) had psoriasis and 19 199 (97.3%) did not have psoriasis. After adjusting for known CVD risk factors, odds for hypertension (OR = 1.37, 95% CI: 1.13-1.66, p = 0.001), hypercholesterolemia (OR = 1.37, 95% CI: 1.13-1.64, p < 0.001) and angina pectoris (OR = 1.74, 95% CI: 1.11-2.60, p = 0.011) were higher in psoriasis patients. Compared with participants without psoriasis, moderate/severe but not mild patients showed significantly higher CVD mortality (HR = 2.55, 95% CI: 1.27-5.15, p = 0.009). This result was supported by subgroup analyses. Mediation analysis further suggested that the direct effect of moderate/severe psoriasis on CVD mortality accounted for 81.4% (65.8%-97.1%). Besides, the indirect effect might derive from disturbance of serum albumin, urea nitrogen and uric acid. Moderate-to-severe psoriasis is an independent risk factor for cardiovascular disease, making it necessary to regularly conduct cardiovascular disease-related examinations for patients with higher severity of psoriasis in clinical settings.

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

Coronary Microvascular Dysfunction in Asymptomatic Patients with Severe Psoriasis

Severe psoriasis is associated with an increased cardiovascular risk, which may be independent of the traditional risk factors. Coronary microvascular dysfunction (CMD) has been shown to predict a poor cardiovascular prognosis in the general population and in patients with psoriasis. In this study, we assessed the prevalence and predictors of CMD in a large cohort of patients with psoriasis without clinical cardiovascular disease. A total of 503 patients with psoriasis were enrolled and underwent transthoracic Doppler echocardiography to evaluate coronary microcirculation. Of these, 55 patients were excluded from the analyses because of missing data. Of the 448 patients in this study, 31.5% showed CMD. Higher PASI, longer disease duration, the presence of psoriatic arthritis, and hypertension were independently associated with CMD. An increase of 1 point of PASI and 1 year of psoriasis duration were associated with a 5.8% and 4.6% increased risk of CMD, respectively. In our study, CMD was associated with the severity and duration of psoriasis. This supports the role of systemic inflammation in CMD and suggests that the coronary microcirculation may represent an extracutaneous site involved in the immune-mediated injury of psoriasis. We should diagnose and actively search for CMD in patients with severe psoriasis.

Differential diagnosis of bone marrow failure syndromes guided by machine learning

The choice to postpone treatment while awaiting genetic testing can result in significant delay in definitive therapies in patients with severe pancytopenia. Conversely, the misdiagnosis of inherited bone marrow failure (BMF) can expose patients to ineffectual and expensive therapies, toxic transplant conditioning regimens, and inappropriate use of an affected family member as a stem cell donor. To predict the likelihood of patients having acquired or inherited BMF, we developed a 2-step data-driven machine-learning model using 25 clinical and laboratory variables typically recorded at the initial clinical encounter. For model development, patients were labeled as having acquired or inherited BMF depending on their genomic data. Data sets were unbiasedly clustered, and an ensemble model was trained with cases from the largest cluster of a training cohort (n = 359) and validated with an independent cohort (n = 127). Cluster A, the largest group, was mostly immune or inherited aplastic anemia, whereas cluster B comprised underrepresented BMF phenotypes and was not included in the next step of data modeling because of a small sample size. The ensemble cluster A-specific model was accurate (89%) to predict BMF etiology, correctly predicting inherited and likely immune BMF in 79% and 92% of cases, respectively. Our model represents a practical guide for BMF diagnosis and highlights the importance of clinical and laboratory variables in the initial evaluation, particularly telomere length. Our tool can be potentially used by general hematologists and health care providers not specialized in BMF, and in under-resourced centers, to prioritize patients for genetic testing or for expeditious treatment.

Therapeutic application of machine learning in psoriasis: A Prisma systematic review

Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.

Heart failure-type symptom scores in chronic kidney disease: The importance of body mass index

OBJECTIVES

This analysis sought to determine factors (including adiposity-related factors) most associated with HF-type symptoms (fatigue, shortness of breath, and edema) in adults with chronic kidney disease (CKD).

BACKGROUND

Symptom burden impairs quality of life in CKD, especially symptoms that overlap with HF. These symptoms are common regardless of clinical HF diagnosis, and may be affected by subtle cardiac dysfunction, kidney dysfunction, and other factors. We used machine learning to investigate cross-sectional relationships of clinical variables with symptom scores in a CKD cohort.

METHODS

Participants in the Chronic Renal Insufficiency Cohort (CRIC) with a baseline modified Kansas City Cardiomyopathy Questionnaire (KCCQ) score were included, regardless of prior HF diagnosis. The primary outcome was Overall Summary Score as a continuous measure. Predictors were 99 clinical variables representing demographic, cardiac, kidney and other health dimensions. A correlation filter was applied. Random forest regression models were fitted. Variable importance scores and adjusted predicted outcomes are presented.

RESULTS

The cohort included 3426 individuals, 10.3% with prior HF diagnosis. BMI was the most important factor, with BMI 24.3 kg/m2 associated with the least symptoms. Symptoms worsened with higher or lower BMIs, with a potentially clinically relevant 5 point score decline at 35.7 kg/m2 and a 1-point decline at the threshold for low BMI, 18.5 kg/m2. The most important cardiac and kidney factors were heart rate and eGFR, the 4th and 5th most important variables, respectively. Results were similar for secondary analyses.

CONCLUSIONS

In a CKD cohort, BMI was the most important feature for explaining HF-type symptoms regardless of clinical HF diagnosis, identifying an important focus for symptom directed investigations.

Epidemiology of Psoriasis and Comorbid Diseases: A Narrative Review

Psoriasis is a chronic autoimmune inflammatory disease that remains active for a long period, even for life in most patients. The impact of psoriasis on health is not only limited to the skin, but also influences multiple systems of the body, even mental health. With the increasing of literature on the association between psoriasis and extracutaneous systems, a better understanding of psoriasis as an autoimmune disease with systemic inflammation is created. Except for cardiometabolic diseases, gastrointestinal diseases, chronic kidney diseases, malignancy, and infections that have received much attention, the association between psoriasis and more systemic diseases, including the skin system, reproductive system, and oral and ocular systems has also been revealed, and mental health diseases draw more attention not just because of the negative mental and mood influence caused by skin lesions, but a common immune-inflammatory mechanism identified of the two systemic diseases. This review summarizes the epidemiological evidence supporting the association between psoriasis and important and/or newly reported systemic diseases in the past 5 years, and may help to comprehensively recognize the comorbidity burden related to psoriasis, further to improve the management of people with psoriasis.

Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field

The atherosclerotic vascular disease is a cardiovascular continuum in which the main role is attributed to atherosclerosis, from its appearance to its associated complications. The increasing prevalence of cardiovascular risk factors, population ageing, and burden on both the economy and the healthcare system have led to the development of new diagnostic and therapeutic strategies in the field. The better understanding or discovery of new pathophysiological mechanisms and molecules modulating various signaling pathways involved in atherosclerosis have led to the development of potential new biomarkers, with key role in early, subclinical diagnosis. The evolution of technological processes in medicine has shifted the attention of researchers from the profiling of classical risk factors to the identification of new biomarkers such as midregional pro-adrenomedullin, midkine, stromelysin-2, pentraxin 3, inflammasomes, or endothelial cell-derived extracellular vesicles. These molecules are seen as future therapeutic targets associated with decreased morbidity and mortality through early diagnosis of atherosclerotic lesions and future research directions.

Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review

Background

Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction.

Objective

To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances.

Methods

We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and "psoriasis" in the title and/or abstract.

Results

Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment.

Conclusion

Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.

Application of machine learning in understanding atherosclerosis: Emerging insights

Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

Metabolic syndrome and its factors are associated with noncalcified coronary burden in psoriasis: An observational cohort study

BACKGROUND

Psoriasis is associated with a heightened risk of cardiovascular disease and higher prevalence of metabolic syndrome.

OBJECTIVE

Investigate the effect of metabolic syndrome and its factors on early coronary artery disease assessed as noncalcified coronary burden by coronary computed tomography angiography in psoriasis.

METHODS

This cross-sectional study consisted of 260 participants with psoriasis and coronary computed tomography angiography characterization. Metabolic syndrome was defined according to the harmonized International Diabetes Federation criteria.

RESULTS

Of the 260 participants, 80 had metabolic syndrome (31%). The metabolic syndrome group had a higher burden of cardiometabolic disease, systemic inflammation, noncalcified coronary burden, and high-risk coronary plaque. After adjusting for Framingham risk score, lipid-lowering therapy, and biologic use, metabolic syndrome (β = .31; P < .001) and its individual factors of waist circumference (β = .33; P < .001), triglyceride levels (β = .17; P = .005), blood pressure (β = .18; P = .005), and fasting glucose (β = .17; P = .009) were significantly associated with noncalcified coronary burden. After adjusting for all other metabolic syndrome factors, blood pressure and waist circumference remained significantly associated with noncalcified coronary burden.

LIMITATIONS

Observational nature with limited ability to control for confounders.

CONCLUSIONS

In psoriasis, individuals with metabolic syndrome had more cardiovascular disease risk factors, systemic inflammation, and noncalcified coronary burden. Efforts to increase metabolic syndrome awareness in psoriasis should be undertaken to reduce the heightened cardiovascular disease risk.

Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6–9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.