Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare
Accurately predicting patient expenditure in healthcare is an important task with many applications such as provider profiling, accountable care management, and capitated medical payment adjustment. Existing approaches mainly rely on manually designed features and linear regression-based models, whi...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/9328319/ |
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| author | Xianlong Zeng Simon Lin Chang Liu |
| author_facet | Xianlong Zeng Simon Lin Chang Liu |
| author_sort | Xianlong Zeng |
| collection | DOAJ |
| description | Accurately predicting patient expenditure in healthcare is an important task with many applications such as provider profiling, accountable care management, and capitated medical payment adjustment. Existing approaches mainly rely on manually designed features and linear regression-based models, which require massive medical domain knowledge and show limited predictive performance. This paper proposes a multi-view deep learning framework to predict future healthcare expenditure at the individual level based on historical claims data. Our multi-view approach can effectively model the heterogeneous information, including patient demographic features, medical codes, drug usages, and facility utilization. We conducted expenditure forecasting tasks on a real-world pediatric dataset that contains more than 450,000 patients. The empirical results show that our proposed method outperforms all baselines for predicting medical expenditure. These findings help toward better preventive care and accountable care in the healthcare domain. |
| format | Article |
| id | doaj-art-90623c851deb4157a434793e20ea68b9 |
| institution | Kabale University |
| issn | 2644-1268 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-90623c851deb4157a434793e20ea68b92024-11-20T00:01:38ZengIEEEIEEE Open Journal of the Computer Society2644-12682021-01-012627110.1109/OJCS.2021.30525189328319Multi-View Deep Learning Framework for Predicting Patient Expenditure in HealthcareXianlong Zeng0https://orcid.org/0000-0001-8110-903XSimon Lin1Chang Liu2https://orcid.org/0000-0002-6721-1959Electrical Engineering and Computer Science, Ohio University, Athens, OH, USARISI, Nationwide Children's Hospital, Columbus, OH, USASchool of EECS, Ohio University, Athens, OH, USAAccurately predicting patient expenditure in healthcare is an important task with many applications such as provider profiling, accountable care management, and capitated medical payment adjustment. Existing approaches mainly rely on manually designed features and linear regression-based models, which require massive medical domain knowledge and show limited predictive performance. This paper proposes a multi-view deep learning framework to predict future healthcare expenditure at the individual level based on historical claims data. Our multi-view approach can effectively model the heterogeneous information, including patient demographic features, medical codes, drug usages, and facility utilization. We conducted expenditure forecasting tasks on a real-world pediatric dataset that contains more than 450,000 patients. The empirical results show that our proposed method outperforms all baselines for predicting medical expenditure. These findings help toward better preventive care and accountable care in the healthcare domain.https://ieeexplore.ieee.org/document/9328319/Administrative claims datadeep learningelectronic health recordexpenditure predictionmachine learning |
| spellingShingle | Xianlong Zeng Simon Lin Chang Liu Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare IEEE Open Journal of the Computer Society Administrative claims data deep learning electronic health record expenditure prediction machine learning |
| title | Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare |
| title_full | Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare |
| title_fullStr | Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare |
| title_full_unstemmed | Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare |
| title_short | Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare |
| title_sort | multi view deep learning framework for predicting patient expenditure in healthcare |
| topic | Administrative claims data deep learning electronic health record expenditure prediction machine learning |
| url | https://ieeexplore.ieee.org/document/9328319/ |
| work_keys_str_mv | AT xianlongzeng multiviewdeeplearningframeworkforpredictingpatientexpenditureinhealthcare AT simonlin multiviewdeeplearningframeworkforpredictingpatientexpenditureinhealthcare AT changliu multiviewdeeplearningframeworkforpredictingpatientexpenditureinhealthcare |