Development of a predictive model of venous thromboembolism recurrence in anticoagulated cancer patients using machine learning

Risk of Recurrent Venous Thromboembolism in Patients with Cancer: An Individual Patient Data Meta-analysis and Development of a Prediction Model

About 7% of patients with cancer-associated venous thromboembolism (CAT) develop a recurrence during anticoagulant treatment. Identification of high-risk patients may help guide treatment decisions.To identify clinical predictors and develop a prediction model for on-treatment recurrent CAT.For this individual patient data meta-analysis, we used data from four randomized controlled trials evaluating low-molecular-weight heparin or direct oral anticoagulants (DOACs) for CAT (Hokusai VTE Cancer, SELECT-D, CLOT, and CATCH). The primary outcome was adjudicated on-treatment recurrent CAT during a 6-month follow-up. A clinical prediction model was developed using multivariable logistic regression analysis with backward selection. This model was validated using internal-external cross-validation. Performance was assessed by the c-statistic and a calibration plot.After excluding patients using vitamin K antagonists, the combined dataset comprised 2,245 patients with cancer and acute CAT who were treated with edoxaban (23%), rivaroxaban (9%), dalteparin (47%), or tinzaparin (20%). Recurrent on-treatment CAT during the 6-month follow-up occurred in 150 (6.7%) patients. Predictors included in the final model were age (restricted cubic spline), breast cancer (odds ratio [OR]: 0.42; 95% confidence interval [CI]: 0.20-0.87), metastatic disease (OR: 1.44; 95% CI: 1.01-2.05), treatment with DOAC (OR: 0.66; 95% CI: 0.44-0.98), and deep vein thrombosis only as an index event (OR: 1.72; 95% CI: 1.31-2.27). The c-statistic of the model was 0.63 (95% CI: 0.54-0.72) after internal-external cross-validation. Calibration varied across studies.The prediction model for recurrent CAT included five clinical predictors and has only modest discrimination. Prediction of recurrent CAT at the initiation of anticoagulation remains challenging.

NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review

This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. It addresses gaps in existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. A comprehensive literature search in the Scopus database identified 94 relevant studies published between 2019 and 2024. The analysis revealed a growing trend in NLP applications for cancer research, with information extraction (47 studies) and text classification (40 studies) emerging as predominant NLP tasks, followed by named entity recognition (7 studies). Among cancer types, breast, lung, and colorectal cancers were found to be the most studied. A significant shift from rule-based and traditional machine learning approaches to advanced deep learning techniques and transformer-based models was observed. It was found that dataset sizes used in existing studies varied widely, ranging from small, manually annotated datasets to large-scale EHRs. The review highlighted key challenges, including the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. While NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. The integration of NLP tools into palliative medicine and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes. This review provides valuable insights into the current state and future directions of NLP applications in cancer research.

Derivation and validation of the Caravaggio score for the risk stratification for recurrence in patients with cancer-associated venous thromboembolism

BACKGROUND

In patients with cancer associated venous thromboembolism (CAT), risk factor-based scores for recurrence could drive clinical management. The aim of this study in patients with CAT was to develop and validate a risk score for recurrent venous thromboembolism (VTE) during anticoagulation: the Caravaggio score.

METHODS

The Caravaggio score was developed in patients included in the Caravaggio trial and then externally validated in patients included in the TESEO registry. Potential predictors (univariate p-value ≤ 0.1) for recurrence were evaluated in a multivariable Cox regression model with death unrelated to VTE as competing event. Candidate predictors were identified and scored based on clinical relevance and β-coefficient. Patients were then categorized in three risk classes. The performance of the Caravaggio score was assessed by discrimination (c-statistics), sensitivity, specificity, positive and negative predictive value (NPV).

RESULTS

Symptomatic VTE, ovarian and/or uterine cancer, pancreatic cancer, metastatic cancer, adenocarcinoma histological subtype, and pharmacological anticancer treatment were included in the score. In the derivation cohort, the incidence of recurrent VTE in the high, intermediate and low-risk groups was 11.6, 7.7 and 2.5 %, respectively. Incidences in the validation cohort were 8.0, 3.5 and 1.7 %, respectively. c-statistics in derivation and validation cohorts were 0.641 (95 % CI 0.584-0.698) and 0.606, (95 % CI 0.557-0.653), respectively. The NPV for low vs. intermediate/high-risk group was 98 % (95 % CI 95-99) in the derivation and 98 % (95 % CI 97-99) in the validation cohort.

CONCLUSIONS

The Caravaggio score is simple and able to stratify patients with CAT for the risk for VTE recurrence.

Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing

BACKGROUND

Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (RWE) generated by artificial intelligence techniques could potentially expedite the validation of new treatments in future health crises. We aimed to use natural language processing (NLP) and machine learning (ML) to assess the impact of RDV on COVID19-associated outcomes including time to discharge and in-hospital mortality.

METHODS

Using EHRead®, an NLP technology including SNOMED-CT terminology that extracts unstructured clinical information from electronic health records (EHR), we retrospectively examined hospitalized COVID-19 patients with moderate-to-severe pneumonia in three Spanish hospitals between January 2021 and March 2022. Among RDV eligible patients, treated (RDV+) vs untreated (RDV‒) patients were compared after propensity score matching (PSM; 1:3.3 ratio) based on age, sex, Charlson comorbidity index, COVID-19 vaccination status, other COVID-19 treatment, hospital, and variant period. Cox proportional hazards models and Kaplan-Meier plots were used to assess statistical differences between groups.

RESULTS

Among 7,651,773 EHRs from 84,408 patients, 6,756 patients were detected with moderate-to-severe COVID-19 pneumonia during the study period. The study population was defined with 4,882 (72.3%) RDV eligible patients. The median age was 72 years and 57.3% were male. A total of 812 (16.6%) patients were classified as RDV+ and were matched to 2,703 RDV‒ patients (from a total of 4,070 RDV‒). After PSM, all covariates had an absolute mean standardized difference of less than 10%. The hazard ratio for in-hospital mortality at 28 days was 0.73 (95% confidence interval, CI, 0.56 to 0.96, p = 0.022) with RDV‒ as the reference group. Risk difference and risk ratio at 28 days was 2.7% and 0.76, respectively, both favoring the RDV+ group. No differences were found in length of hospital stay since RDV eligibility between groups.

CONCLUSIONS

Using NLP and ML we were able to generate RWE on the effectiveness of RDV in COVID-19 patients, confirming the potential of using this methodology to measure the effectiveness of treatments in pandemics. Our results show that using RDV in hospitalized patients with moderate-to-severe pneumonia is associated with significantly reduced inpatient mortality. Adherence to clinical guideline recommendations has prognostic implications and emerging technologies in identifying eligible patients for treatment and avoiding missed opportunities during public health crises are needed.

The impact of KRAS mutations on risk of venous thromboembolism recurrence in patients with metastatic colorectal cancer

BACKGROUND

The relationship between KRAS mutations and the risk of venous thromboembolism (VTE) recurrence in metastatic colorectal cancer (mCRC) patients with established cancer-associated thrombosis (CAT) remains uncertain. This study aims to (1) evaluate the predictive value of seven KRAS mutation subtypes for VTE recurrence and (2) assess the impact of incorporating these mutations into two existing VTE risk scores: the Khorana score and the Ottawa score.

METHODS

Between 2019 and 2023, we identified patients with histologically confirmed mCRC who had symptomatic or incidental index VTE and received anticoagulation therapy. Regression analyses were conducted to calculate hazard ratios (HRs) for recurrent VTE associated with the seven KRAS mutation subtypes. We used receiver operating characteristic (ROC) curves to assess the performance of both the original scores and the modified scores that included KRAS mutations. To quantify the improvements of the modified scores, we calculated the net reclassification improvement (NRI).

RESULTS

A total of 2,195 patients were enrolled. KRAS G12C, KRAS G12A, and KRAS G13D mutations were significantly associated with a higher risk of recurrent VTE compared to other subtypes, with HRs of 1.84 (95% CI: 1.09-2.97), 2.02 (95% CI: 1.07-3.79), and 1.55 (95% CI: 1.02-2.27), respectively. The original Khorana and Ottawa scores demonstrated moderate predictive ability for VTE recurrence, each with an area under the ROC curve (ROC-AUC) of 0.56 (95% CI: 0.52-0.60). Incorporating the KRAS G12C, KRAS G12A, and KRAS G13D mutations improved the AUCs to 0.70 (95% CI: 0.67-0.74) for the modified Khorana score and 0.71 (95% CI: 0.67-0.74) for the modified Ottawa score. After dichotomizing risk using thresholds from ROC analysis, the NRI values were 0.54 (95% CI: 0.43-0.65) for the modified Khorana score and 0.48 (95% CI: 0.37-0.60) for the modified Ottawa score.

CONCLUSIONS

The KRAS G12C, KRAS G12A, and KRAS G13D mutations are significantly associated with an increased risk of recurrent VTE. Incorporating these specific KRAS mutations into existing risk scores may enhance their predictive accuracy for recurrent VTE in patients with mCRC.

Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing

PURPOSE

We developed a predictive model to assess the risk of major bleeding (MB) within 6 months of primary venous thromboembolism (VTE) in cancer patients receiving anticoagulant treatment. We also sought to describe the prevalence and incidence of VTE in cancer patients, and to describe clinical characteristics at baseline and bleeding events during follow-up in patients receiving anticoagulants.

METHODS

This observational, retrospective, and multicenter study used natural language processing and machine learning (ML), to analyze unstructured clinical data from electronic health records from nine Spanish hospitals between 2014 and 2018. All adult cancer patients with VTE receiving anticoagulants were included. Both clinically- and ML-driven feature selection was performed to identify MB predictors. Logistic regression (LR), decision tree (DT), and random forest (RF) algorithms were used to train predictive models, which were validated in a hold-out dataset and compared to the previously developed CAT-BLEED score.

RESULTS

Of the 2,893,108 cancer patients screened, in-hospital VTE prevalence was 5.8% and the annual incidence ranged from 2.7 to 3.9%. We identified 21,227 patients with active cancer and VTE receiving anticoagulants (53.9% men, median age of 70 years). MB events after VTE diagnosis occurred in 10.9% of patients within the first six months. MB predictors included: hemoglobin, metastasis, age, platelets, leukocytes, and serum creatinine. The LR, DT, and RF models had AUC-ROC (95% confidence interval) values of 0.60 (0.55, 0.65), 0.60 (0.55, 0.65), and 0.61 (0.56, 0.66), respectively. These models outperformed the CAT-BLEED score with values of 0.53 (0.48, 0.59).

CONCLUSIONS

Our study shows encouraging results in identifying anticoagulated patients with cancer-associated VTE who are at high risk of MB.

Cancer-associated thrombosis: what is new?

PURPOSE OF REVIEW

The life expectancy of patients suffering from thrombosis associated with cancer has improved significantly, making them a chronic disease. Patients with thrombosis and cancer are fragile. Treated with anticoagulants, they remain at risk of complications.

RECENT FINDINGS

Consequently, news issues emerge for clinical practice: anticoagulation therapy personalization is required to optimize the benefit ratio, involving patient characteristics and cancer characteristics. During follow-up, prediction score are designed and investigated to help identify and discriminate patients at risk of venous thromboembolism recurrences and major bleedings. Considering the improved prognosis of patients with cancer and cancer-associated thrombosis, the question of extended treatment arises, representing a major unmet need to date. Finally, new strategies, in particular anti-XI agents that appear attractive options, are currently being evaluated in the treatment of thrombosis associated with cancer.

SUMMARY

The improved prognosis of patients with cancer-associated thrombosis is accompanied by new therapeutic strategies to improve the benefit-risk ratio of anticoagulant treatment in these fragile patients, at risk of both venous thromboembolic recurrence and haemorrhagic complication.

Random forest algorithm for predicting postoperative hypotension in oral cancer resection and free flap reconstruction surgery

This study aimed to investigate the risk factors for low postoperative blood pressure and construct a machine learning (ML) model based on these features for real-time prediction in patients with oral cancer following reconstruction surgery. The retrospective cohort analysis included adults who had undergone oral cancer resection and free flap reconstruction surgery between December 2022 and December 2023. Patient clinical characteristics were obtained from the electronic medical records. Seven ML techniques were attempted with postoperative hypotension (POH) (mean arterial pressure ˂ 55 mmHg) as the primary outcome. The best-performing ML model was tuned, and the final performance was evaluated using split-set validation, followed by risk factor identification and model interpretability. Of the 727 patients, 412 were finally included, with 66 (16.2%) experiencing POH, resulting in higher inpatient costs and prolonged hospitalization. With an area under the receiver operating characteristic curve of 0.805 (95% confidence interval [CI]: 0.674-0.935), the random forest model demonstrated excellent performance. Shapley additive explanation and feature importance analysis revealed that systolic pressure, heart rate, tumor size, lactic acid level, diastolic pressure, surgical time, total liquid infusion volume, and body mass index were significant risk factors for POH, indicating the robustness of the random forest model.

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study

Background and Objectives: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine learning (ML). Materials and Methods: We designed a case-control study nested in a cohort of patients with VTE included in a prospective registry from two Spanish hospitals between 2005 and 2021. Both clinically and ML-driven feature selection were performed to identify predictors for occult cancer. XGBoost, LightGBM, and CatBoost algorithms were used to train different prediction models, which were subsequently validated in a hold-out dataset. Results: A total of 815 patients with VTE were included (51.5% male and median age of 59). During follow-up, 56 patients (6.9%) were diagnosed with cancer. One hundred and twenty-one variables were explored for the predictive analysis. CatBoost obtained better performance metrics among the ML models analyzed. The final CatBoost model included, among the top 15 variables to predict hidden malignancy, age, gender, systolic blood pressure, heart rate, weight, chronic lung disease, D-dimer, alanine aminotransferase, hemoglobin, serum creatinine, cholesterol, platelets, triglycerides, leukocyte count and previous VTE. The model had an ROC-AUC of 0.86 (95% CI, 0.83-0.87) in the test set. Sensitivity, specificity, and negative and positive predictive values were 62%, 94%, 93% and 75%, respectively. Conclusions: This is the first risk score developed for identifying patients with VTE who are at increased risk of occult cancer using ML tools, obtaining a remarkably high diagnostic accuracy. This study's limitations include potential information bias from electronic health records and a small cancer sample size. In addition, variability in detection protocols and evolving clinical practices may affect model accuracy. Our score needs external validation.

SEOM clinical guidelines on venous thromboembolism (VTE) and cancer (2023)

The Spanish Society of Medical Oncology (SEOM) last published clinical guidelines on venous thromboembolism (VTE) and cancer in 2019, with a partial update in 2020. In this new update to the guidelines, SEOM seeks to incorporate recent evidence, based on a critical review of the literature, to provide practical current recommendations for the prophylactic and therapeutic management of VTE in patients with cancer. Special clinical situations whose management and/or choice of currently recommended therapeutic options (low-molecular-weight heparins [LMWHs] or direct-acting oral anticoagulants [DOACs]) is controversial are included.

External validation of the RIETE and SOME scores for occult cancer in patients with venous thromboembolism: a multicentre cohort study

INTRODUCTION

Venous thromboembolism (VTE) may be the first sign of an undiagnosed cancer. The RIETE and SOME scores aim to identify patients with acute VTE at high risk of occult cancer. In the present study, we evaluated the performance of both scores.

METHODS

The scores were evaluated in a retrospective cohort from two centers. The area under the receiver-operating characteristics curve (AUC) evaluated the discriminatory performance.

RESULTS

The RIETE score was applied to 815 patients with provoked and unprovoked VTE, of whom 56 (6.9%) were diagnosed with cancer. Of the 203 patients classified as high-risk, 18 were diagnosed with cancer, representing 32.1% (18/56) of the total cancer diagnoses. In the group of 612 low-risk patients, 67.9% of the cancer cases were diagnosed (38/56). Sensitivity, specificity, negative and positive predictive values, and AUC were 32%, 76%, 94%, 9%, and 0.430 (95% confidence interval [CI], 0.38‒0.47), respectively. The SOME score could be calculated in 418 patients with unprovoked VTE, of whom 33 (7.9%) were diagnosed with cancer. Of the 45 patients classified as high-risk, three were diagnosed with cancer, representing 9.1% (3/33) of the total cancer diagnoses. In the group of 373 low-risk patients, 90.9% of the cancer cases were diagnosed (30/33). Sensitivity, specificity, negative and positive predictive values, and AUC were 33%, 88%, 94%, 20%, and 0.351 (95% CI, 0.27‒0.43), respectively.

CONCLUSIONS

The performance of both scores was poor. Our results highlight the need to develop new models to identify high-risk patients who may benefit from an extensive cancer screening strategy.

Venous thromboembolism in the era of machine learning and artificial intelligence in medicine

In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present utility and envisioning its transformative roles within VTE management, from prevention to screening and beyond. Central to our discourse is a forward-looking perspective on the integration of AI within VTE in medicine, advocating for rigorous study design, robust validation processes, and meticulous statistical analysis to gauge the efficacy of AI applications. We further illuminate the potential of large language models and generative AI in revolutionizing VTE care, while acknowledging their inherent limitations and proposing innovative solutions to overcome challenges related to data availability and integrity, including the strategic use of synthetic data. The critical importance of navigating ethical, legal, and privacy concerns associated with AI is underscored, alongside the imperative for comprehensive governance and policy frameworks to regulate its deployment in VTE treatment. We conclude on a note of cautious optimism, where we highlight the significance of proactively addressing the myriad challenges that accompany the advent of AI in healthcare. Through diligent design, stringent validation, extensive education, and prudent regulation, we can harness AI's potential to significantly enhance our understanding and management of VTE. As we stand on the cusp of a new era, our commitment to these principles will be instrumental in ensuring that the promise of AI is fully realized within the realm of VTE care.

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating the literature on the use of ML for the prediction of cancer-associated thrombosis is necessary to understand its diagnostic and prognostic abilities relative to KS. This systematic review aims to evaluate the current use and performance of ML algorithms to predict thrombosis in cancer patients. This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Databases Medline, EMBASE, Cochrane, and ClinicalTrials.gov, were searched from inception to September 15, 2023, for studies evaluating the use of ML models for the prediction of thrombosis in cancer patients. Search terms "machine learning," "artificial intelligence," "thrombosis," and "cancer" were used. Studies that examined adult cancer patients using any ML model were included. Two independent reviewers conducted study selection and data extraction. Three hundred citations were screened, of which 29 studies underwent a full-text review, and ultimately, 8 studies with 22,893 patients were included. Sample sizes ranged from 348 to 16,407 patients. Thrombosis was characterized as venous thromboembolism (n = 6) or peripherally inserted central catheter thrombosis (n = 2). The types of cancer included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, head-neck, and sarcoma. All studies reported outcomes on the ML's predictive capacity. The extreme gradient boosting appears to be the best-performing model, and several models outperform KS in their respective datasets.

GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience

BACKGROUND

There is currently a shortage of accurate, efficient, and precise predictive instruments for rectal neuroendocrine neoplasms (NENs).

AIM

To develop a predictive model for individuals with rectal NENs (R-NENs) using data from a large cohort.

METHODS

Data from patients with primary R-NENs were retrospectively collected from 17 large-scale referral medical centers in China. Random forest and Cox proportional hazard models were used to identify the risk factors for overall survival and progression-free survival, and two nomograms were constructed.

RESULTS

A total of 1408 patients with R-NENs were included. Tumor grade, T stage, tumor size, age, and a prognostic nutritional index were important risk factors for prognosis. The GATIS score was calculated based on these five indicators. For overall survival prediction, the respective C-indexes in the training set were 0.915 (95% confidence interval: 0.866-0.964) for overall survival prediction and 0.908 (95% confidence interval: 0.872-0.944) for progression-free survival prediction. According to decision curve analysis, net benefit of the GATIS score was higher than that of a single factor. The time-dependent area under the receiver operating characteristic curve showed that the predictive power of the GATIS score was higher than that of the TNM stage and pathological grade at all time periods.

CONCLUSION

The GATIS score had a good predictive effect on the prognosis of patients with R-NENs, with efficacy superior to that of the World Health Organization grade and TNM stage.

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients

AIM

To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurrence prediction.

METHODS

A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single-center retrospective study and were randomly divided into the training dataset (n = 870) and the test dataset (n = 217). Univariate logistic regression analysis was used to identify risk factors that associated with the occurrence of postoperative VTE in the training dataset. Machine learning models (including decision tree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated.

RESULTS

The incidence of developing 30-day postoperative VTE was 6.0% (65/1087). Age, previous VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lymphadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G-Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p < 0.05). The AUCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respectively.

CONCLUSION

The ML models, especially the DT model, constructed in our study had excellent prediction value and shed light upon its further application in clinic practice.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups.

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.