ArticleJournal of the American Medical Directors Association2021
Machine Learning Algorithms to Predict Mortality and Allocate Palliative Care for Older Patients With Hip Fracture.
Article in Journal of the American Medical Directors Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 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.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Artificial Intelligence and Machine Learning for Outcome Prediction After Osteoporotic Hip Fracture: A Systematic Review and Meta-analysis of Prediction Model Performance.Current osteoporosis reports · 2026Pooled it
- Models for predicting short- to long-term mortality in older adults with hip fractures in clinical practice: a systematic review and meta-analysis.BMC geriatrics · 2026Pooled it
- Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Evaluation of short- and long-term mortality prediction in patients undergoing hip fracture surgery using a biomarker-based interpretable predictive modeling approach: a retrospective cohort analysis.BMC medical informatics and decision making · 2026Article
- The Technologies, Applicability, and Trade-Offs of AI in Palliative Care for Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Review
- Development and internal validation of prediction model for 90-day mortality in older patients with hip fracture using admission blood parameters.BMC geriatrics · 2026Article
- Screening Tools for the Early Identification of Palliative Care Needs in Patients with Advanced Chronic Conditions: An Updated Systematic Review.Journal of clinical medicine · 2026Review
- Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Comparing manual vs. automated machine learning and deep learning models for predicting one-year mortality in elderly hip fracture patients.Frontiers in medicine · 2026Article
- Interpretable machine learning approach for optimizing hospice care predictions using health assessment data.BMC medical informatics and decision making · 2025Article
- Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Review
- A nomogram for one-year risk of death after hip fracture.Frontiers in medicine · 2025Article
- Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Development and Internal Validation of a Multivariable Prediction Model for Mortality After Hip Fracture with Machine Learning Techniques.Calcified tissue international · 2024Article
- Palliative Non-Operative Management in Geriatric Hip Fracture Patients: When Would Surgeons Abstain from Surgery?Journal of clinical medicine · 2024Article
- Predicting Prolonged Wound Drainage after Hemiarthroplasty for Hip Fractures: A Stacked Machine Learning Study.Clinics in orthopedic surgery · 2023Article
- A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.Journal of medical Internet research · 2023Article
- Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo evaluate a machine learning model designed to predict mortality for Medicare beneficiaries aged >65 years treated for hip fracture in Inpatient Rehabilitation Facilities (IRFs).
designRetrospective design/cohort analysis of Centers for Medicare & Medicaid Services Inpatient Rehabilitation Facility-Patient Assessment Instrument data. SETTING AND
participantsA total of 17,140 persons admitted to Medicare-certified IRFs in 2015 following hospitalization for hip fracture. MEASURES: Patient characteristics include sociodemographic (age, gender, race, and social support) and clinical factors (functional status at admission, chronic conditions) and IRF length of stay. Outcomes were 30-day and 1-year all-cause mortality. We trained and evaluated 2 classification models, logistic regression and a multilayer perceptron (MLP), to predict the probability of 30-day and 1-year mortality and evaluated the calibration, discrimination, and precision of the models.
resultsFor 30-day mortality, MLP performed well [acc = 0.74, area under the receiver operating characteristic curve (AUROC) = 0.76, avg prec = 0.10, slope = 1.14] as did logistic regression (acc = 0.78, AUROC = 0.76, avg prec = 0.09, slope = 1.20). For 1-year mortality, the performances were similar for both MLP (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.96) and logistic regression (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.95). CONCLUSION AND IMPLICATIONS: A scoring system based on logistic regression may be more feasible to run in current electronic medical records. But MLP models may reduce cognitive burden and increase ability to calibrate to local data, yielding clinical specificity in mortality prediction so that palliative care resources may be allocated more effectively.
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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.