Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery

May 1, 2019, 00:00
10.1016/j.jval.2019.01.011
https://www.valueinhealthjournal.com/article/S1098-3015(19)30073-7/fulltext
Title : Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery
Citation : https://www.valueinhealthjournal.com/action/showCitFormats?pii=S1098-3015(19)30073-7&doi=10.1016/j.jval.2019.01.011
First page : 580
Section Title : DECISION-ANALYTIC MODELING: PAST, PRESENT, AND FUTURE
Open access? : No
Section Order : 580

Objectives

Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS.

Methods

We selected patients meeting the following criteria in 2 large US healthcare claims databases (Truven Health MarketScan Commercial [CCAE]; Optum Clinformatics [Optum]): underwent MxS between January 1, 2007, to October 1, 2013 (first = index); aged ≥18 years; continuous enrollment 180 days pre-index (baseline) to 730 days postindex; baseline T2D diagnosis and treatment. The outcome was no antihyperglycemic medication treatment from 365 to 730 days after MxS. A regularized logistic regression model was trained using the following candidate predictor categories measured at baseline: demographics, conditions, medications, measurements, and procedures. A 75% to 25% split of the CCAE group was used for model training and testing; the Optum group was used for external validation.

Results

13 050 (CCAE) and 3477 (Optum) patients met the study inclusion criteria. Antihyperglycemic medication cessation rates were 72.9% (CCAE) and 70.8% (Optum). The model possessed good internal discriminative accuracy (area under the curve [AUC] = 0.778 [95% CI = 0.761-0.795] in CCAE test set N = 3527) and transportability (external AUC = 0.759 [95% CI = 0.741-0.777] in Optum N = 3477).

Conclusion

The application of machine learning techniques to real-world healthcare data can yield useful predictive models to assist patient selection. In future practice, establishment of prerequisite technological infrastructure will be needed to implement such models for real-world decision support.

Categories :
  • Administrative Claims (Insurance and Billing) Data
  • Artificial Intelligence, Machine Learning, Predictive Analytics
  • Diabetes/Endocrine/Metabolic Disorders
  • Health & Insurance Records Systems
  • Surgery
Tags :
  • antihyperglycemic medication
  • machine learning
  • metabolic surgery
  • prediction
  • type 2 diabetes
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