ArticlePloS one2023
The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.JMIR medical informatics · 2026Article
- A machine learning model for post-operative sepsis prediction in acute surgical patients: a multi-centre, prospective study.Scientific reports · 2026Article
- Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.Prostate cancer and prostatic diseases · 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
- Enhancing Medicaid Social Risk Identification: Comparing Linear and Non-Linear Machine Learning Models with Expert Heuristics.IEEE International Conference on Digital Health. IEEE International Conference on Digital Health · 2026Article
- Analysis of medical costs and two-model prediction for patients with severe mental disorders in Gansu Province, China.Frontiers in public health · 2026Article
- Multiple sclerosis risk stratification and healthcare cost prediction using machine learning.Communications medicine · 2025Article
- Enhancing predictions of health insurance overspending risk through hospital departmental performance indicators.Biomolecules & biomedicine · 2025Article
- Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods.Scientific reports · 2025Article
- Development and validation of a machine learning-based model for perioperative stroke prediction in noncardiac, nonvascular, and nonneurosurgical patients.Frontiers in physiology · 2025Article
- Article
- Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis.Brain and behavior · 2024Article
- Machine learning models for predicting extended length of stay and hospital charges in nontraumatic subarachnoid hemorrhage.Frontiers in neurologyArticle
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications of each algorithm were selected based on validation dataset performance. For performance comparison, selected models were applied to unforeseen data with features of the year 2017 and outcomes of the year 2018 (n = 21,146). The RF was the best performing algorithm measured by the area under the receiver operating curve (AUC) with a value of 0.883 (95% confidence interval (CI): 0.872-0.893) on test data, followed by the GBM (AUC = 0.878; 95% CI: 0.867-0.889). The ANN (AUC = 0.846; 95% CI: 0.834-0.857) and LR (AUC = 0.839; 95% CI: 0.826-0.852) were significantly outperformed by the GBM and the RF. All ML algorithms and the LR performed ´good´ (i.e. 0.9 > AUC ≥ 0.8). We were able to develop machine learning models that predict high-cost patients with 'good' performance facilitating routinely collected sickness fund claims and cost data. We found that tree-based models performed best and outperformed the ANN and LR.
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