ArticleInternational journal of machine learning and computing2015
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data.
Article in International journal of machine learning and computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.Frontiers in oncology · 2024Pooled it
- Improving palliative and end-of-life care with machine learning and routine data: a rapid review.HRB open research · 2019Pooled it
- Hospice Care in the Era of AI: Hospices' Views on Data-Driven Tools to Support Live Discharge Decisions.Journal of the American Geriatrics Society · 2026Article
- The Technologies, Applicability, and Trade-Offs of AI in Palliative Care for Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
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- Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65.Scientific reports · 2023Article
- Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours.Clinical epidemiology · 2023Article
- Predicting mortality in the very old: a machine learning analysis on claims data.Scientific reports · 2022Article
- Identification of Digital Health Priorities for Palliative Care Research: Modified Delphi Study.JMIR aging · 2022Article
- Development of a Machine Learning Model Using Limited Features to Predict 6-Month Mortality at Treatment Decision Points for Patients With Advanced Solid Tumors.JCO clinical cancer informatics · 2022Article
- Revisiting performance metrics for prediction with rare outcomes.Statistical methods in medical research · 2021Article
- Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites.BMC medical informatics and decision making · 2020Article
- Prediction model for short-term mortality after palliative radiotherapy for patients having advanced cancer: a cohort study from routine electronic medical data.Scientific reports · 2020Article
- Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study.JMIR cancer · 2019Article
- Article
- Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions.JAMA network open · 2019Article
- Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.Journal of the National Cancer Institute · 2019Article
- Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.Journal of biomedical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning models often seek to optimize predictions by incorporating features that are not present in the databases readily available to providers and policy makers, limiting generalizability and implementation. Here we tested a number of machine learning classifiers for prediction of six-month mortality in a population of elderly Medicare beneficiaries, using an administrative claims database of the kind available to the majority of health care payers and providers. We show that machine learning classifiers substantially outperform current widely-used methods of risk prediction-but only when used with an improved feature set incorporating insights from clinical medicine, developed for this study. Our work has applications to supporting patient and provider decision making at the end of life, as well as population health-oriented efforts to identify patients at high risk of poor outcomes.
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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.