ArticleScientific reports2019
Predictive Modeling of the Hospital Readmission Risk from Patients' Claims Data Using Machine Learning: A Case Study on COPD.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07349901 (Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data), which is not on this map. Cited by 74 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A Narrative Review of the Correlation Between Comorbidity and Acute Exacerbation of COPD Patients.International journal of chronic obstructive pulmonary disease · 2026Review
- Predicting 30-day unplanned readmissions in patients with cancer in a Brazilian hospital: a Machine Learning approach.Einstein (Sao Paulo, Brazil) · 2026Article
- Development and External Validation of a Machine Learning Model for 90-Day Readmission in Hospitalized Older Patients with AECOPD: A Two-Center Study.International journal of chronic obstructive pulmonary disease · 2026Article
- Development and Validation of an Electronic Health Record Algorithm to Predict the Presence of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Advanced machine learning models for prediction of readmission and mortality risks in patients with chronic obstructive pulmonary disease using routine clinical data.Fujita medical journal · 2025Article
- Predicting outcomes after hospitalisation for COPD exacerbation using machine learning.ERJ open research · 2025Article
- Article
- Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.PloS one · 2025Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- Artificial Intelligence Applied to Forced Spirometry in Primary Care.Open respiratory archives · 2024Article
- Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach.Journal of clinical medicine · 2024Article
- Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data.Healthcare (Basel, Switzerland) · 2024Article
- Predicting Hospital Readmission in Medicaid Patients With COPD Using Administrative and Claims Data.Respiratory care · 2024Article
- Research on Multimodal Fusion of Temporal Electronic Medical Records.Bioengineering (Basel, Switzerland) · 2024Article
- Implementing federated learning over VPN-based wireless backhaul networks for healthcare systems.PeerJ. Computer science · 2024Article
- Accurately assessing congenital heart disease using artificial intelligence.PeerJ. Computer science · 2024Article
- Federated Offline Reinforcement Learning.Journal of the American Statistical Association · 2024Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a prevalent chronic pulmonary condition that affects hundreds of millions of people all over the world. Many COPD patients got readmitted to hospital within 30 days after discharge due to various reasons. Such readmission can usually be avoided if additional attention is paid to patients with high readmission risk and appropriate actions are taken. This makes early prediction of the hospital readmission risk an important problem. The goal of this paper is to conduct a systematic study on developing different types of machine learning models, including both deep and non-deep ones, for predicting the readmission risk of COPD patients. We evaluate those different approaches on a real world database containing the medical claims of 111,992 patients from the Geisinger Health System from January 2004 to September 2015. The patient features we build the machine learning models upon include both knowledge-driven ones, which are the features extracted according to clinical knowledge potentially related to COPD readmission, and data-driven features, which are extracted from the patient data themselves. Our analysis showed that the prediction performance in terms of Area Under the receiver operating characteristic (ROC) Curve (AUC) can be improved from around 0.60 using knowledge-driven features, to 0.653 by combining both knowledge-driven and data-driven features, based on the one-year claims history before discharge. Moreover, we also demonstrate that the complex deep learning models in this case cannot really improve the prediction performance, with the best AUC around 0.65.
Indexed as
Identifiers
What OpenQuestion holds
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.