The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data
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, w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847900/?tool=EBI |
| _version_ | 1828054979718414336 |
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| author | Benedikt Langenberger Timo Schulte Oliver Groene |
| author_facet | Benedikt Langenberger Timo Schulte Oliver Groene |
| author_sort | Benedikt Langenberger |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T20:31:20Z |
| format | Article |
| id | doaj.art-026e3fb06272447599da43d0ed7ebcd8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T20:31:20Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-026e3fb06272447599da43d0ed7ebcd82023-01-25T05:34:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01181The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims dataBenedikt LangenbergerTimo SchulteOliver GroeneOur 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847900/?tool=EBI |
| spellingShingle | Benedikt Langenberger Timo Schulte Oliver Groene The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data PLoS ONE |
| title | The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data |
| title_full | The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data |
| title_fullStr | The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data |
| title_full_unstemmed | The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data |
| title_short | The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data |
| title_sort | application of machine learning to predict high cost patients a performance comparison of different models using healthcare claims data |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847900/?tool=EBI |
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