ArticlePatterns (New York, N.Y.)2022
Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed, 61 citations in OpenAlex.
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Interpretable SHAP-based machine learning framework for patient satisfaction prediction: a case study in Thammasat University Hospital.BMC medical informatics and decision making · 2026Article
- Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Use of Conventional Artificial Intelligence Methods in the Identification of Frailty: A Scoping Review.Journal of the American Geriatrics Society · 2026Article
- Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study.JMIR medical informatics · 2026Article
- Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians.Clinical medicine (London, England) · 2026Review
- Interpretable artificial intelligence for predicting blood transfusion after surgery for femoral shaft fractures: A retrospective analysis.Indian journal of anaesthesia · 2026Article
- Predicting long-term hospital outcomes in older adults with the hospital frailty risk score: a nationwide study of repeated readmissions.BMC geriatrics · 2026Article
- Digital therapeutics into geriatric cardiovascular emergency care.Frontiers in digital health · 2026Review
- Clinical clusters during acute illness predict long-term mortality in older patients.BMC medicine · 2025Article
- Clinical and environmental factors, functional status, and multimorbidity-stratifying progression and prognosis of multimorbidity, frailty, and disability: the Age-It Research Program.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- A Machine Learning Tool to Predict Survival After First Surgery in Peripheral Artery Disease Patients.Journal of cardiovascular translational research · 2025Article
- Integrating multiple data sources to predict all-cause readmission or mortality in patients with substance misuse.PLOS digital health · 2025Article
- Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study.JMIR aging · 2025Article
- Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study.JMIR medical informatics · 2025Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Integrating real-world data and machine learning: A framework to assess covariate importance in real-world use of alternative intravenous dosing regimens for atezolizumab.Clinical and translational science · 2024Article
- An explainable artificial intelligence framework for risk prediction of COPD in smokers.BMC public health · 2023Article
- Article
- Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation.Patterns (New York, N.Y.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare costs due to unplanned readmissions are high and negatively affect health and wellness of patients. Hospital readmission is an undesirable outcome for elderly patients. Here, we present readmission risk prediction using five machine learning approaches for predicting 30-day unplanned readmission for elderly patients (age ≥ 50 years). We use a comprehensive and curated set of variables that include frailty, comorbidities, high-risk medications, demographics, hospital, and insurance utilization to build these models. We conduct a large-scale study with electronic health record (her) data with over 145,000 observations from 76,000 patients. Findings indicate that the category boost (CatBoost) model outperforms other models with a mean area under the curve (AUC) of 0.79. We find that prior readmissions, discharge to a rehabilitation facility, length of stay, comorbidities, and frailty indicators were all strong predictors of 30-day readmission. We present in-depth insights using Shapley additive explanations (SHAP), the state of the art in machine learning explainability.
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.