Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support. (1st December 2020)

• Record Type: Journal Article
• Authors: Baron, Jason M; Paranjape, Ketan; Love, Tara; Sharma, Vishakha; Heaney, Denise; Prime, Matthew
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 3(2021)
• Page Start: 605
• Page End: 615
• Publication Date: 2020-12-01
• Subjects: missing data -- imputation -- clinical decision support -- meta-model -- machine learning -- survival; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa254
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Like most real-world data, electronic health record (EHR)–derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the "meta-model" and apply the meta-model to patient-specific cancer prognosis. Materials and Methods: Using real-world data, we developed a suite of individual random survival forest models to predict survival in patients with advanced lung cancer, colorectal cancer, and breast cancer. Individual models varied by the predictor data used. We combined models for each cancer type into a meta-model that predicted survival for each patient using a weighted mean of the individual models for which the patient had all requisite predictors. Results: The meta-model significantly outperformed many of the individual models and performed similarly to the best performing individual models. Comparisons of the meta-model to a more traditional imputation-based method of addressing missing data supported the meta-model's utility. Conclusions: We developed a novel machine learning–based strategy to underlie clinical decision support and predict survival in cancer patients, despite missing data. The meta-model may more generally provide a tool for addressing missing data across a variety of clinical prediction problems. Moreover, the meta-model may address other challenges in clinical predictive modeling including model extensibility and integration of predictive algorithms trained across different institutions and datasets.