ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2018
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 10 citations in OpenAlex.
- Article
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- Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.Scientific reports · 2025Article
- Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.Journal of cosmetic dermatology · 2025Article
- Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.Scientific reports · 2024Article
- FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data.Scientific reports · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Impact of medical-nursing combined policy pilot on hospitalization frequency of middle-aged and older patients with chronic diseases: a quasi-experimental study based on China Health and Retirement Longitudinal Study.Frontiers in public health · 2024Article
- Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.Frontiers in endocrinology · 2024Article
- Article
- Prediction of Prednisolone Dose Correction Using Machine Learning.Journal of healthcare informatics research · 2023Article
- Predictive models of pregnancy based on data from a preconception cohort study.Human reproduction (Oxford, England) · 2022Article
- Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey.Archives of computational methods in engineering : state of the art reviews · 2022Article
- The impact of payer status on hospital admissions: evidence from an academic medical center.BMC health services research · 2021Observational
- Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator.International journal of medical informatics · 2020Article
- Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.JMIR medical informatics · 2020Article
- Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator.medRxiv : the preprint server for health sciences · 2020Article
- Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study.Scientific reports · 2020Article
- Prescriptive analytics for reducing 30-day hospital readmissions after general surgery.PloS one · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.