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

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

ARIANA: Adaptive Robust and Integrative Analysis for finding Novel Associations

The effective mining of biological literature can provide a range of services such as hypothesis-generation, semantic-sensitive information retrieval, and knowledge discovery, which can be important to understand the confluence of different diseases, genes, and risk factors. Furthermore, integration of different tools at specific levels could be valuable. The main focus of the dissertation is developing and integrating tools in finding network of semantically related entities. The key contribution is the design and implementation of an Adaptive Robust and Integrative Analysis for finding Novel Associations. ARIANA is a software architecture and a web-based system for efficient and scalable knowledge discovery. It integrates semantic-sensitive analysis of text-data through ontology-mapping with database search technology to ensure the required specificity. ARIANA was prototyped using the Medical Subject Headings ontology and PubMed database and has demonstrated great success as a dynamic-data-driven system. ARIANA has five main components: (i) Data Stratification, (ii) Ontology-Mapping, (iii) Parameter Optimized Latent Semantic Analysis, (iv) Relevance Model and (v) Interface and Visualization. The other contribution is integration of ARIANA with Online Mendelian Inheritance in Man database, and Medical Subject Headings ontology to provide gene-disease associations. Empirical studies produced some exciting knowledge discovery instances. Among them was the connection between the hexamethonium and pulmonary inflammation and fibrosis. In 2001, a research study at John Hopkins used the drug hexamethonium on a healthy volunteer that ended in a tragic death due to pulmonary inflammation and fibrosis. This accident might have been prevented if the researcher knew of published case report. Since the original case report in 1955, there has not been any publications regarding that association. ARIANA extracted this knowledge even though its database contains publications from 1960 to 2012. Out of 2,545 concepts, ARIANA ranked “Scleroderma, Systemic”, “Neoplasms, Fibrous Tissue”, “Pneumonia”, “Fibroma”, and “Pulmonary Fibrosis” as the 13th, 16th, 38th, 174th and 257th ranked concept respectively. The researcher had access to such knowledge this drug would likely not have been used on healthy subjects.In today\u27s world where data and knowledge are moving away from each other, semantic-sensitive tools such as ARIANA can bridge that gap and advance dissemination of knowledge

Knowledge Discovery Through Large-Scale Literature-Mining of Biological Text-Data

The aim of this study is to develop scalable and efficient literature-mining framework for knowledge discovery in the field of medical and biological sciences. Using this scalable framework, customized disease-disease interaction network can be constructed. Features of the proposed network that differentiate it from existing networks are its 1) flexibility in the level of abstraction, 2) broad coverage, and 3) domain specificity. Empirical results for two neurological diseases have shown the utility of the proposed framework. The second goal of this study is to design and implement a bottom-up information retrieval approach to facilitate literature-mining in the specialized field of medical genetics. Experimental results are being corroborated at the moment

Hepatitis C virus molecular evolution: Transmission, disease progression and antiviral therapy

Hepatitis C virus (HCV) infection represents an important public health problem worldwide. Reduction of HCV morbidity and mortality is a current challenge owned to several viral and host factors. Virus molecular evolution plays an important role in HCV transmission, disease progression and therapy outcome. The high degree of genetic heterogeneity characteristic of HCV is a key element for the rapid adaptation of the intrahost viral population to different selection pressures (e.g., host immune responses and antiviral therapy). HCV molecular evolution is shaped by different mechanisms including a high mutation rate, genetic bottlenecks, genetic drift, recombination, temporal variations and compartmentalization. These evolutionary processes constantly rearrange the composition of the HCV intrahost population in a staging manner. Remarkable advances in the understanding of the molecular mechanism controlling HCV replication have facilitated the development of a plethora of direct-acting antiviral agents against HCV. As a result, superior sustained viral responses have been attained. The rapidly evolving field of anti-HCV therapy is expected to broad its landscape even further with newer, more potent antivirals, bringing us one step closer to the interferon-free era.Fil: Preciado, María Victoria. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Valva, Pamela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños ; ArgentinaFil: Escobar Gutierrez, Alejandro. Instituto de Diagnóstico y Referencia Epidemiológicos; MéxicoFil: Rahal, Paula. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Ruiz Tovar, Karina. Instituto de Diagnóstico y Referencia Epidemiológicos; MéxicoFil: Yamasaki, Lilian. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Vazquez Chacon, Carlos. Instituto de Diagnóstico y Referencia Epidemiológicos; MéxicoFil: Martinez Guarneros, Armando. Instituto de Diagnóstico y Referencia Epidemiológicos; MéxicoFil: Carpio Pedroza, Juan Carlos. Instituto de Diagnóstico y Referencia Epidemiológicos; MéxicoFil: Fonseca Coronado, Salvador. Universidad Nacional Autónoma de México; MéxicoFil: Cruz Rivera, Mayra. Universidad Nacional Autónoma de México; Méxic

A Novel Soft Computing Based Model For Symptom Analysis & Disease Classification

In countries like India, many mortality occurs every year because of improper pronouncement of disease on time. Many people remain deprived of medication as the people per doctor ratio are nearly 1:1700. Every human body and its physiological processes show some symptoms of a diseased condition. The proposed model in this paper would analyze those symptoms for identification of the disease and its type. In this proposed model, few selected attributes would be considered which are shown as symptoms by a person suspected with a particular disease. Those attributes can be taken as input for the proposed symptom analysis and classification model, which is a soft computing model for classifying a sample first to be diseased or disease free and then, if diseased, predicting its type (if any). Number of diseased and disease free samples are to be collected. Each of these samples is a collection of attributes shown / expressed by a human body. With respect to a specific disease, those collected samples form two primary clusters, one is diseased and the other one is disease free. The disease free cluster may be discarded for further analysis. Depending on the symptoms shown by the diseased samples, every disease has some types based on the symptoms it shows. The diseased cluster of samples can reform clusters among themselves depending on the types of the disease. Those clusters then become the classes of the multiclass classifier for analysis of a new incoming sample

Med Decis Making

Purpose:Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes.Methods:We combine two algorithm adaptation methods \u2013 multi-target stacking and regression chain with maximum correlation \u2013 with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings.Results:Output variables from the simulation model were correlated (average \u3c1=0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving interpretability of other models.Conclusions:In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had modest impact. While advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.R37 DA015612/DA/NIDA NIH HHSUnited States/U38 PS004644/PS/NCHHSTP CDC HHSUnited States/2022-10-01T00:00:00Z35735216PMC945245411962vault:4324

Predicting healthcare high-cost users using data mining methods

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe increase in healthcare costs is, perhaps, one of the most important issues that governments and organizations face nowadays. An ageing population and technological advancements are the key reasons for this phenomenon. In this scenario, proactive measures are very important. This work aimed to improve the effectiveness of the prevention by helping the identification of the most probable high-cost users of health services in future years. Data from 2015 to 2019 of approximately 30,000 Central Bank of Brazil’s Health Program’s enrollees were used to train, validate and test four types of models, considering the kind of high-cost users (simple or cost-bloomers, i.e., non-high-cost in previous periods) and the time-span between predictors and the dependent variable (none or one year), an innovation suggested by other authors. Different percentual cut-off points to define highcost were used, and up to 67% of high-risk users’ expenses could be correctly captured. Results confirmed the importance of previous costs data for this kind of prediction and showed that costbloomers and one-year time-span approaches reach good performance, creating opportunities to improve users’ health outcomes while contributing to the fiscal sustainability of private and public health systems