Machine learning approaches for predicting high cost high need patient expenditures in health care. Yang, C., Delcher, C., Shenkman, E., & Ranka, S. Biomed Eng Online, 17(Suppl 1):131, Nov, 2018.
Machine learning approaches for predicting high cost high need patient expenditures in health care [link]Paper  abstract   bibtex   
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.\\ 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.\\ 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.
@Article{pmid30458798,
   Author="Yang, C.  and Delcher, C.  and Shenkman, E.  and Ranka, S. ",
   Title="{{M}achine learning approaches for predicting high cost high need patient expenditures in health care}",
   Journal="Biomed Eng Online",
   Year="2018",
   Volume="17",
   Number="Suppl 1",
   Pages="131",
   Month="Nov",
   Abstract={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.\\ 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.\\ 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.},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245495}
}

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