ArticleAdvances in data mining. Industrial Conference on Data Mining2017
Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients.
Article in Advances in data mining. Industrial Conference on Data Mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Secondary use of standardized nursing care data for advancing nursing science and practice: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Evidence of Progress in Making Nursing Practice Visible Using Standardized Nursing Data: a Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Pooled it
- Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study.JMIR medical informatics · 2025Article
- Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network.International journal of medical informatics · 2024Article
- Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.JCO clinical cancer informatics · 2024Article
- Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.Frontiers in digital health · 2024Article
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.Journal of medical Internet research · 2021Review
- Application of machine learning in predicting hospital readmissions: a scoping review of the literature.BMC medical research methodology · 2021Article
- Time-varying Readmission Diagnoses During 30 Days After Hospitalization for COPD Exacerbation.Medical care · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.However, most of the EHRs are high dimensional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.
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.