Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.
Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
2/3/2025
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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=183076984&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183076984 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2/3/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183076984 183076984 183076984 10.1007/s10916-025-02140-z 183076984 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases. aug: au: Hernández-Arango, Alejandro Arias, María Isabel Pérez, Viviana Chavarría, Luis Daniel Jaimes, Fabian affil: https://ror.org/03bp5hc83 Department of Internal Medicine, University of Antioquia, Medellín, Colombia sug: subj: Noncommunicable Diseases Prognosis Machine Learning Risk Assessment Prediction Models Decision Support Systems, Clinical Human Colombia Male Female Middle Age Aged Aged, 80 and Over Descriptive Statistics Confidence Intervals Prospective Studies Mortality Hospitalization Emergency Service Office Visits Retrospective Design Record Review Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms—XGBoost, Elastic Net logistic regression, and an Artificial Neural Network—to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848–0.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865–0.927), and the Neural Network achieved 0.886 (95% CI: 0.853–0.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937–0.965), the XGBoost model achieved 0.963 (95% CI: 0.952–0.974), and the Neural Network scored 0.932 (95% CI: 0.915–0.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971–0.987) for Elastic Net, 0.977 (95% CI: 0.967–0.986) for XGBoost, and 0.976 (95% CI: 0.968–0.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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