Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine.
Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20%–30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with time. The purpose of this study was to predict the risks...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 2; pp. 1 - 13 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148904080&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148904080 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2021 vid: 45 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148904080 148904080 148904080 10.1007/s10916-020-01693-5 148904080 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine. aug: au: Goldman, Orit Ben-Assuli, Ofir Rogowski, Ori Zeltser, David Shapira, Itzhak Berliner, Shlomo Zelber-Sagi, Shira Shenhar-Tsarfaty, Shani affil: Faculty of Business Administration, Ono Academic College, 104 Zahal Street, 55000, Kiryat Ono, Israel sug: subj: Nonalcoholic Fatty Liver Disease Complications Fibrosis Complications Liver Pathology Risk Assessment Machine Learning Methods Preventive Health Care Quality Improvement Decision Making, Clinical Physician Attitudes Human Prospective Studies Surveys Clinical Assessment Tools Inflammation Patient Admission Life Style Changes Chronic Disease Prevention and Control Health Screening Economic Aspects of Illness Questionnaires Chi Square Test Descriptive Statistics Adult Middle Age Male Female Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20%–30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with time. The purpose of this study was to predict the risks associated with NAFLD and advanced fibrosis on the Fatty Liver Index (FLI) and the 'NAFLD fibrosis 4' calculator (FIB-4), to enable physicians to make more optimal preventive medical decisions. A prospective cohort of apparently healthy volunteers from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), admitted for their routine annual health check-up. Data from the TAMCIS database were subjected to machine learning classification models to predict individual risk after extensive data preparation that included the computation of independent variables over several time points. After incorporating the time covariates and other key variables, this technique outperformed the predictive power of current popular methods (an improvement in AUC above 0.82). New powerful factors were identified during the predictive process. The findings can be used for risk stratification and in planning future preventive strategies based on lifestyle modifications and medical treatment to reduce the disease burden. Interventions to prevent chronic disease can substantially reduce medical complications and the costs of the disease. The findings highlight the value of predictive analytic tools in health care environments. NAFLD constitutes a growing burden on the health system; thus, identification of the factors related to its incidence can make a strong contribution to preventive medicine. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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