Year: 2024
Type: conference-paper
Abstract: Heart failure (HF) has the highest 30-day rehospitalization rate among all medical and surgical conditions.This study proposes a method for predicting HF recurrence using longitudinal chart data and laboratory measurements from patients in the critical care unit (CCU).We analyzed data from 8,720 HF patients in the MIMIC-III database, extracting time series of five physiological measurements and deriving 11 statistical features from each, along with demographic and laboratory data, totaling 67 features.We employed a Random Forest (RF) classifier to predict HF recurrence over four time intervals (week, month, half a year, and a year) and conducted survival analysis using HF-recurrence probabilities from the trained model.For the test set, we achieved a mean F1 score of 0.57±0.09,sensitivity of 0.77±0.03,and specificity of 0.77±0.04.Our findings suggest that machine learning algorithms applied to chart data and laboratory measurements could potentionally help guide patient discharge decisions and reduce HF recurrence.
Source: Computing in cardiology
Authors: Zuzana Koscova, Eniko Vargova, Jan Pavlus, Radovan Smisek, Veronika Bulkova, Pavel Jurak, Filip Plesinger
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
Award: G7384073382
OpenAlex ID: W4405434982