ArticleJournal of the American Medical Informatics Association : JAMIA
Mining high-dimensional administrative claims data to predict early hospital readmissions.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.BMJ open · 2016Pooled it
- Nationwide hospital admission data statistics and disease-specific 30-day readmission prediction.Health information science and systems · 2022Article
- Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach.BMC medical informatics and decision making · 2021Article
- Article
- Predicting all-cause 90-day hospital readmission for dental patients using machine learning methods.BDJ open · 2021Article
- What Are They Worth? Six 30-Day Readmission Risk Scores for Medical Inpatients Externally Validated in a Swiss Cohort.Journal of general internal medicine · 2020Article
- Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk.Scientific reports · 2020Article
- Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.JAMA network open · 2019Article
- On the interpretability of machine learning-based model for predicting hypertension.BMC medical informatics and decision making · 2019Article
- Claims data-driven modeling of hospital time-to-readmission risk with latent heterogeneity.Health care management science · 2019Article
- Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency.Journal of health economics and outcomes research · 2019Article
- Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory.PloS one · 2019Article
- Bayesian hierarchical vector autoregressive models for patient-level predictive modeling.PloS one · 2018Article
- Scalable and accurate deep learning with electronic health records.NPJ digital medicine · 2018Article
- Mortality Risk Factors Among Patients With Cirrhosis and a Low Model for End-Stage Liver Disease Sodium Score (≤15): An Analysis of Liver Transplant Allocation Policy Using Aggregated Electronic Health Record Data.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2017Article
- Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2017Article
- Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients.PeerJ · 2017Article
- Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.PloS one · 2017Article
- A numerical similarity approach for using retired Current Procedural Terminology (CPT) codes for electronic phenotyping in the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS).BMC medical informatics and decision making · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundCurrent readmission models use administrative data supplemented with clinical information. However, the majority of these result in poor predictive performance (area under the curve (AUC)<0.70).
objectiveTo develop an administrative claim-based algorithm to predict 30-day readmission using standardized billing codes and basic admission characteristics available before discharge. MATERIALS AND
methodsThe algorithm works by exploiting high-dimensional information in administrative claims data and automatically selecting empirical risk factors. We applied the algorithm to index admissions in two types of hospitalized patient: (1) medical patients and (2) patients with chronic pancreatitis (CP). We trained the models on 26,091 medical admissions and 3218 CP admissions from The Johns Hopkins Hospital (a tertiary research medical center) and tested them on 16,194 medical admissions and 706 CP admissions from Johns Hopkins Bayview Medical Center (a hospital that serves a more general patient population), and vice versa. Performance metrics included AUC, sensitivity, specificity, positive predictive values, negative predictive values, and F-measure.
resultsFrom a pool of up to 5665 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnoses, 599 ICD-9-CM procedures, and 1815 Current Procedural Terminology codes observed, the algorithm learned a model consisting of 18 attributes from the medical patient cohort and five attributes from the CP cohort. Within-site and across-site validations had an AUC≥0.75 for the medical patient cohort and an AUC≥0.65 for the CP cohort.
conclusionsWe have created an algorithm that is widely applicable to various patient cohorts and portable across institutions. The algorithm performed similarly to state-of-the-art readmission models that require clinical data.
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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.