ArticleBiomedical engineering online2018
Machine learning approaches for predicting high cost high need patient expenditures in health care.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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Who cites it
98 citing papers in PubMed.
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- Identification of Cellular Senescence-Related Hub Genes in Rheumatoid Arthritis from Bioinformatics Analysis Through Machine Learning up to Verifications in Mouse Macrophages and Tests in Patients.International journal of molecular sciences · 2026Article
- A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Identification of potential key efferocytosis-related genes in rheumatoid arthritis using machine learning.Clinical rheumatology · 2026Article
- Novel Insights into the Pathogenic Role of CSF1R in Endometriosis.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- Identification of mitochondria-related genes in calcific aortic valve disease by integrated analysis of single-cell and bulk transcriptomic atlases.Journal of physiology and biochemistry · 2026Article
- HIV testing factors among Men Who Have Sex with Men in China: an analysis based on Lasso regression and Bayesian networks.BMC public health · 2026Article
- Integrative bioinformatics-machine learning-experimental validation identifies shared immunomodulatory targets in psoriasis and psoriatic arthritis and deciphers allicin's therapeutic mechanism.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Development of Machine Learning Models to Predict Health Insurance Claim Costs Among Older Indonesians: A Retrospective Predictive Modeling Study.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026Article
- Machine learning framework for early identification of high-spending Crohn's disease patients using administrative claims.BMJ health & care informatics · 2026Article
- Insights into the interplay between stroke and depression through lipid metabolism-related diagnostic genes.Molecular brain · 2026Article
- Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury.Molecular medicine reports · 2026Article
- Identification and Validation of Ferroptosis-Related Biomarkers and Therapeutic Targets in ARDS: A Bioinformatics and Experimental Study.Journal of inflammation research · 2026Article
- Study on the pathogenesis of PLXNB1 gene in olfactory dysfunction of allergic rhinitis.PloS one · 2026Article
- Neutrophil Extracellular Trap-Related Gene Signatures and Molecular Clusters in Severe Influenza: Identification Through Integrative Transcriptome Analysis.Journal of immunology research · 2026Article
- Interpretable machine learning enables early identification of financial risk and resource optimization in colorectal cancer surgery.Frontiers in public health · 2026Article
- Impact of multimorbidity on healthcare costs in patients with type 2 diabetes in China: a longitudinal analysis of health insurance claims data.Archives of public health = Archives belges de sante publique · 2025Article
- Prediction of financial deficits of postoperative patients in the intensive care unit using machine learning.JA clinical reports · 2025Article
38 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
backgroundThis paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.
resultsWe systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance.
conclusionsThis study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently.
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