ArticleNPJ digital medicine2019
Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.Frontiers in medicine · 2024Pooled it
- Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Artificial intelligence for detecting fall events, assessing fall risks, and preventing fall occurrences in the elderly: A narrative review.Clinical medicine (London, England) · 2026Review
- Explainable SHAP-space fall risk profiling from electronic health records for inpatient prevention planning.npj health systems · 2026Article
- Observational
- Article
- Investigation of High Efficacy Groups for Hospital-Wide Standardized Hypnotic Bundles on Insomnia: A Subgroup Analysis of the COBATON Study.Journal of general and family medicine · 2026Article
- Association between atherogenic index and risk of falls: a decade-long prospective cohort study.BMC public health · 2025Article
- Artificial intelligence in nursing practice: a qualitative study of nurses' perspectives on opportunities, challenges, and ethical implications.BMC nursing · 2025Article
- Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: a landmarking approach.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Fall prediction in a quiet standing balance test via machine learning: Is it possible?PloS one · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.BMC medical informatics and decision making · 2023Article
- Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient.Methodist DeBakey cardiovascular journal · 2023Review
- Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.Journal of healthcare engineering · 2022Article
- Prediction of fall risk among community-dwelling older adults using a wearable system.Scientific reports · 2021Article
- Development of an algorithm for assessing fall risk in a Japanese inpatient population.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi-view ensemble learning and model-based missing data imputation method. As input, over two thousand inpatient fall patients' demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi-view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross-validated AUC of 0.713 (95% CI, 0.701-0.725), an AUC of 0.808 (95% CI, 0.740-0.876) on the separate testing set. Our studies show the efficacy of integrative machine-learning based classifier model in dealing with multi-source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients.
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.