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Predicting Medical Event Occurrence Using Medical Insurance Claims Big Data.
- Source :
- Medinfo; 2023, Vol. 310, p654-658, 5p
- Publication Year :
- 2023
-
Abstract
- Medical events are often infrequent, thus becomes hard to predict. In this paper, we focus on predictor that forecasts whether a medical event would occur in the next year, and analyzes the impact of event's frequency and data size via predictor's performance. In the experiment, we made 1572 predictors for medical events using Medical Insurance Claims (MICs) data from 800,000 participants and 205.8 million claims over 8 years. The result revealed that (a) forecasting error will be increased when predicting low-frequency events, and (b) increasing the number of training dataset reduces errors. This result suggests that increasing data size is a key to solve low frequency problems. However, we still need additional methods to cope with sparse and imbalanced data. [ABSTRACT FROM AUTHOR]
- Subjects :
- RESEARCH evaluation
CONFERENCES & conventions
MEDICAL care
HEALTH insurance reimbursement
DATABASE management
HEALTH insurance
FORECASTING
DESCRIPTIVE statistics
SOFTWARE analytics
ADVERSE health care events
DIAGNOSTIC errors
RECEIVER operating characteristic curves
PREDICTION models
DATA analysis software
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 15696332
- Volume :
- 310
- Database :
- Complementary Index
- Journal :
- Medinfo
- Publication Type :
- Conference
- Accession number :
- 175124539
- Full Text :
- https://doi.org/10.3233/SHTI231046