Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research

• URL: http://arxiv.org/abs/2001.09765v1
• Date: Mon, 13 Jan 2020 22:58:48 GMT
• Title: Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research
• Authors: Benjamin Birnbaum, Nathan Nussbaum, Katharina Seidl-Rathkopf, Monica Agrawal, Melissa Estevez, Evan Estola, Joshua Haimson, Lucy He, Peter Larson, Paul Richardson
• Abstract summary: We introduce a technique called Model-Assisted Cohort Selection (MACS) with Bias Analysis. We trained a model on 17,263 patients using term-frequency inverse-document-frequency (TF-IDF) and logistic regression. We used a test set of 17,292 patients to measure algorithm performance and perform Bias Analysis.
• Score: 1.25957368859589
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Objective Electronic health records (EHRs) are a promising source of data for health outcomes research in oncology. A challenge in using EHR data is that selecting cohorts of patients often requires information in unstructured parts of the record. Machine learning has been used to address this, but even high-performing algorithms may select patients in a non-random manner and bias the resulting cohort. To improve the efficiency of cohort selection while measuring potential bias, we introduce a technique called Model-Assisted Cohort Selection (MACS) with Bias Analysis and apply it to the selection of metastatic breast cancer (mBC) patients. Materials and Methods We trained a model on 17,263 patients using term-frequency inverse-document-frequency (TF-IDF) and logistic regression. We used a test set of 17,292 patients to measure algorithm performance and perform Bias Analysis. We compared the cohort generated by MACS to the cohort that would have been generated without MACS as reference standard, first by comparing distributions of an extensive set of clinical and demographic variables and then by comparing the results of two analyses addressing existing example research questions. Results Our algorithm had an area under the curve (AUC) of 0.976, a sensitivity of 96.0%, and an abstraction efficiency gain of 77.9%. During Bias Analysis, we found no large differences in baseline characteristics and no differences in the example analyses. Conclusion MACS with bias analysis can significantly improve the efficiency of cohort selection on EHR data while instilling confidence that outcomes research performed on the resulting cohort will not be biased.

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