Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study

Background

Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individuals fail to achieve adequate immune reconstitution, known as immunological nonresponse (INR), which is linked to poor prognosis and higher mortality. However, the early prediction and intervention of INR remains challenging in South China.

Methods

This study included 1,577 PLWH who underwent at least two years of HAART and clinical follow-up between 2017 and 2022 at two major tertiary hospitals in South China. We utilized logistic multivariate regression to identify independent predictors of INR and employed restricted cubic splines (RCS) for nonlinear analysis. We also developed several machine-learning models, assessing their performance using internal and external datasets to generate receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The best-performing model was further interpreted using Shapley additive explanations (SHAP) values.

Results

Independent predictors of INR included baseline, 6-month and 12-month CD4+ T cell counts, baseline hemoglobin, and 6-month hemoglobin levels. RCS analysis highlighted significant nonlinear relationships between baseline CD4+ T cells, 12-month CD4+ T cells and baseline hemoglobin with INR. The Random Forest model demonstrated superior predictive accuracy, with ROC areas of 0.866, 0.943, and 0.897 across the datasets. Calibration was robust, with Brier scores of 0.136, 0.102, and 0.126. SHAP values indicated that early CD4+T cell counts and CD4/CD8 ratio were crucial in predicting INR.

Conclusions

This study introduces the random forest model to predict incomplete immune reconstitution in PLWH, which can significantly assist clinicians in the early prediction and intervention of INR among PLWH.

Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging

Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR.

Application of artificial intelligence in liver diseases: From diagnosis to treatment

Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden. There were an estimated 2.14 million liver-related deaths in 2017, representing an 11.4% increase since 2012. Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease. As a hot research topic in recent years, the application of artificial intelligence (AI) in different fields has attracted extensive attention, and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases. Machine learning (ML) is the core of AI and the basic way to make a computer intelligent. ML technology has many potential uses in hepatology, ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis. The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly, accurately and scientifically, which is of importance for reducing the incidence and mortality of liver diseases, reducing medical errors, and promoting the development of medicine. This paper reviews the application and prospects of AI in liver diseases, and aims to improve clinicians’ awareness of the importance of AI in the diagnosis and treatment of liver diseases.