Predicting Future Elective Colon Resection for Diverticulitis Using Patterns of Health Care Utilization.
Lucas W ThornbladeDavid R FlumAbraham D FlaxmanPublished in: EGEMS (Washington, DC) (2018)
By applying Machine Learning to HCU data from the time around a diagnosis of diverticulitis, we predicted elective surgery weeks to months in advance, with moderate accuracy. Identifying patients who are most likely to elect surgery for diverticulitis provides an opportunity for effective shared decision making initiatives aimed at reducing the use of costly low-value care.
Keyphrases
- healthcare
- minimally invasive
- machine learning
- coronary artery bypass
- end stage renal disease
- patients undergoing
- ejection fraction
- quality improvement
- newly diagnosed
- chronic kidney disease
- big data
- prognostic factors
- artificial intelligence
- patient reported outcomes
- surgical site infection
- electronic health record
- high intensity
- percutaneous coronary intervention
- acute coronary syndrome
- social media