ArticleNPJ digital medicine2020
Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review.Journal of general internal medicine · 2022Pooled it
- Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.JMIR medical informatics · 2026Article
- Nonlinear modeling of health checkup factors and future claims-based healthcare costs in Japan: a retrospective cohort study.Health economics review · 2026Article
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- Predicting high-cost healthcare users: development and external validation of multivariable models using the HUNT and Tromsø studies linked to Norwegian health registries.BMC health services research · 2026Article
- Risk stratification for long-term inpatient costs in mental disorders: a dual-track machine learning approach using baseline EHRs and hospitalization trajectories.BMC health services research · 2026Article
- Risk stratification and determinant identification of high-need, high-cost ICU patients using machine learning: a large-scale retrospective study from a multi-specialty ICU in a tertiary hospital.Frontiers in public health · 2026Article
- Multiple sclerosis risk stratification and healthcare cost prediction using machine learning.Communications medicine · 2025Article
- Can patient-reported data improve predictions about who will be a high-need, high-cost patient in British Columbia?Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Plasma SVEP1 Levels Predict Cardiovascular Events in Hypertrophic Cardiomyopathy Beyond Conventional Clinical Risk Models Including NT-proBNP.Circulation. Heart failure · 2025Article
- Machine-learning-based cost prediction models for inpatients with mental disorders in China.BMC psychiatry · 2025Article
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- Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning.BMC infectious diseases · 2024Article
- Early-Stage Chronic Kidney Disease and Related Health Care Spending.JAMA network open · 2024Article
- Machine Learning to Predict Interstage Mortality Following Single Ventricle Palliation: A NPC-QIC Database Analysis.Pediatric cardiology · 2023Article
- Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare.Biomaterials · 2023Review
- A prediction model for asthma exacerbations after stopping asthma biologics.Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology · 2023Article
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- Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-need, high-cost (HNHC) patients-usually defined as those who account for the top 5% of annual healthcare costs-use as much as half of the total healthcare costs. Accurately predicting future HNHC patients and designing targeted interventions for them has the potential to effectively control rapidly growing healthcare expenditures. To achieve this goal, we used a nationally representative random sample of the working-age population who underwent a screening program in Japan in 2013-2016, and developed five machine-learning-based prediction models for HNHC patients in the subsequent year. Predictors include demographics, blood pressure, laboratory tests (e.g., HbA1c, LDL-C, and AST), survey responses (e.g., smoking status, medications, and past medical history), and annual healthcare cost in the prior year. Our prediction models for HNHC patients combining clinical data from the national screening program with claims data showed a c-statistics of 0.84 (95%CI, 0.83-0.86), and overperformed traditional prediction models relying only on claims data.
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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.