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

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group

A Comparative Study on Hepatitis C Predictions Using Machine Learning Algorithms

Hepatitis C virus (HCV) is known to be the major cause of chronic liver disease. Based on research, HCV has caused more than 100.000 cases of liver cancer per year. This virus has become the cause of at least 280.000 deaths. To diagnose HCV, it takes at least two different tests, namely serological assays and molecular tests, which are quite costly and complex. With Machine Learning technology, the diagnosis of any disease or virus can be made by detecting different patterns or relationships. Therefore, this study aims to predict the Hepatitis C virus using different machine learning algorithms and find out the best model for the classification of Hepatitis C disease. Furthermore, this study shows some visualizations to find out the relationships between attributes. We used different machine learning algorithms, namely K-Nearest Neighbour, Support Vector Machine, Random Forest, Neural Network, Naïve Bayes, and Logistic Regression. The performance of those different machine learning algorithms was evaluated using four different metrics, which are classification accuracy, precision, recall, and F-1 score. The classification accuracy results are 96.5%, 96.7%, 97.3%, 97.1%, 96%, 97.9% each for k-NN, SVM, RandomFores, Neural Network, Naïve Bayes and Logistic Regression. Based on the results, each model showed high performance, but Logistic Regression performs the best result. With the results conducted by this study, it is hoped that it can help the diagnosis process of HCV based on laboratory data. However, it is important to communicate the shortcomings and some possible improvements for each model. Keywords: Machine Learning, Predictions, Hepatitis C Viru

Integrated Study of Liver Fibrosis: Modeling and Clinical Detection

The liver is a vital organ that carries out over 500 essential tasks, including fat metabolism, blood filtering, bile production, and some protein production. Although the structure of the liver and the role of each type of cells in the liver are well known, the biomedical and mechanical interplays within liver tissues remain unclear. Chronic liver diseases are a significant public health challenge. All chronic liver diseases lead to liver fibrosis due to excessive fiber accumulation, resulting in cirrhosis and loss of liver function. Only early stage liver fibrosis is reversible. However, early-stage liver fibrosis is difficult to diagnose. How the progression of fibrosis changes the mechanical properties of the liver tissue and altering the dynamics of blood flow is still not well understood. The objective of this dissertation is to integrate the understanding of liver diseases and mechanical modeling to develop several models relating liver fibrosis to blood flow. In collaboration with clinicians specialized in hepatic fibrosis, we integrated computational modeling and clinicopathologic image analysis and proposed a new technology for early stage fibrosis detection. The key results of this research include: (1) A mathematical model of liver fibrosis progression connecting the cellular and molecular mechanisms of fibrosis to tissue rigidity; (2) A novel machine learning-based algorithm to automatically stage liver fibrosis based on pathology images; (3) A physics model to illustrate how the liver stiffness affects the blood flow pattern, predicting a direct relationship between fibrosis stage and ultrasound Doppler measurement of liver blood flow; (4) Statistical analysis of clinical ultrasound Doppler data from fibrosis patients confirming our model prediction. These results lead to a novel noninvasive technology for detecting early stages of liver fibrosis with high accuracy

A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES

Liver cirrhosis is the most common type of chronic liver disease in the globe. The ability to forecast the onset of liver cirrhosis sickness is critical for successful treatment and the prevention of catastrophic health implications. As a result, the researchers created a prediction model using machine learning techniques. This study was based on a dataset from the Federal Medical Centre, Yola, which included 583 patient instances and 11 attributes. The proposed model for the prediction of liver cirrhosis sickness employed Nave Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, and F1 Score were used to evaluate the model's performance. Among all the strategies used in this study, the Support Vector Machine (SVM) technique produces the best results, with accuracy of 73%, precision of 73%, recall of 100%, and F1 Score of 84%. Based on medical data from FMC, Yola, this study shows that machine learning methods, specifically the Support Vector Machine, provide a more accurate prediction for liver cirrhosis sickness. This approach can be used to help doctors make better clinical decisions

Analyzing Non-Alcoholic Fatty Liver Disease Risk Using Time-Series Model

Non-alcoholic fatty liver disease (NAFLD) is the most global frequent liver disease, with a prevalence of almost 20% in the overall population. NAFLD may progress to fibrosis and later into cirrhosis in addition to other diseases. Our objective is to stratify patients\u27 risks for NAFLD and advanced fibrosis over time and suggest preventive medical decisions. We used a cohort of individuals from the Tel-Aviv medical center. Time-series clustering machine learning model (Hidden Markov Models (HMM)) was used to profile fibrosis risk by modeling patients’ latent medical status and trajectories over time. The best-fitting model had three latent HMM states. Initial results show that tracking individuals over time and their relative risk for fibrosis at each point of time provides significant clinical insights regarding each state (and its group of individuals). Thus, longitudinal risk stratification can enable the early identification of specific individual groups following distinct medical trajectories based on their routine visits

Diagnostic and interventional circulating biomarkers in nonalcoholic steatohepatitis

IntroductionIn the setting of the obesity epidemic, nonalcoholic fatty liver disease (NAFLD) has become one of the most prevalent forms of chronic liver disease worldwide. Approximately 25% of adults globally have NAFLD which includes those with NAFL, or simple steatosis, and individuals with nonalcoholic steatohepatitis (NASH) where inflammation, hepatocyte injury and potentially hepatic fibrosis are found in conjunction with steatosis. Individuals with NASH, particularly those with hepatic fibrosis, have higher rates of liver‐related and overall mortality, making this distinction of significant clinical importance. One of the core challenges in current clinical practice is identifying this subset of individuals with NASH without the use of liver biopsy, the gold standard for both diagnostics and staging disease severity. Identifying noninvasive biomarkers, an accurately measured and reproducible parameter, would aide in identifying patients eligible for NASH pharmacotherapy clinical trials and to help tailor intensity of monitoring required.Methods, Results and ConclusionsIn this review, we highlight both the currently available and novel diagnostic and interventional circulating biomarkers under investigation for NASH, underscoring their accuracy and limitations relevant to our patient population and current clinical practice.One of the core challenges in NASH is the ability to accurately diagnose and stage individuals using non‐invasive methods. In this review, we highlight both the currently available and novel diagnostic and interventional circulating biomarkers under investigation for NASH, underscoring their accuracy and limitations relevant to our patient population and current clinical practice.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163493/2/edm2177.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163493/1/edm2177_am.pd

Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts

Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Vari