Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Objective: In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies. Materials and Methods: In this study, we propose a clinical text representation infused with medical knowledge (MK). First, we isolate the noise from the relevant data using a medically relevant description extractor; then we utilize log-likelihood ratio based weights from selected sentences to highlight "met" and "not-met" knowledge-infused representations in bichannel setting for each instance. The combined medical knowledge-infused representation (MK) from these modules helps identify significant clinical criteria semantics, which in turn renders effective learning when used with a convolutional neural network architecture. Results: MKCNN outperforms other Medical Knowledge (MK) relevant learning architectures by approximately 3%; notably SVM and XGBoost implementations developed in this study. MKCNN scored 86.1% on F1metric, a gain of 6% above the average performance assessed from the submissions for n2c2 task. Although pattern/rule-based methods show a higher average performance for the n2c2 clinical data set, MKCNN significantly improves performance of machine learning implementations for clinical datasets. Conclusion: MKCNN scored 86.1% on the F1 score metric. In contrast to many of the rule-based systems introduced during the n2c2 challenge workshop, our system presents a model that heavily draws on machine-based learning. In addition, the MK representations add more value to clinical comprehension and interpretation of natural texts.