التفاصيل البيبلوغرافية
| العنوان: |
Machine learning approaches for predicting high cost high need patient expenditures in health care |
| المؤلفون: |
Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka |
| المصدر: |
BioMedical Engineering OnLine, Vol 17, Iss S1, Pp 1-20 (2018) |
| بيانات النشر: |
BMC, 2018. |
| سنة النشر: |
2018 |
| المجموعة: |
LCC:Medical technology |
| مصطلحات موضوعية: |
High-cost, High need patients, Machine learning, Predictive modeling, Medical technology, R855-855.5 |
| الوصف: |
Abstract Background This 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. Results We 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. Conclusions This 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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1475-925X |
| Relation: |
http://link.springer.com/article/10.1186/s12938-018-0568-3; https://doaj.org/toc/1475-925X |
| DOI: |
10.1186/s12938-018-0568-3 |
| URL الوصول: |
https://doaj.org/article/0eda411cf6df430e9f2bf0ee1e148d7e |
| رقم الانضمام: |
edsdoj.0eda411cf6df430e9f2bf0ee1e148d7e |
| قاعدة البيانات: |
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