Key result
A generalized linear model net (GLMN) predicted hospitalization in heart failure patients with 81.2% accuracy, 87.5% positive predictive value, and 75% negative predictive value.
Why the study?
The study aimed to evaluate and compare the performance of eight machine learning techniques for predicting hospitalization in patients with heart failure.
Can machine learning techniques accurately predict hospitalization in patients with heart failure using standard healthcare data?
Observational (n=380)
Yes
Can machine learning techniques accurately predict hospitalization in patients with heart failure using standard healthcare data?
Machine learning techniques, particularly generalized linear model net, demonstrate high accuracy in predicting hospital admissions for heart failure patients using routinely collected clinical data.
ML models for HF hospitalization prediction warrant prospective validation; leaves open clinical integration.
The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.
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Lorenzoni et al. (2019) conducted an observational in Heart failure (n=380). Machine Learning Techniques (specifically GLMN) vs. Other Machine Learning Techniques was evaluated on Prediction of hospitalization. A generalized linear model net (GLMN) predicted hospitalization in heart failure patients with 81.2% accuracy, 87.5% positive predictive value, and 75% negative predictive value.
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