COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching

• URL: http://arxiv.org/abs/2006.08765v1
• Date: Mon, 15 Jun 2020 21:01:33 GMT
• Title: COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching
• Authors: Junyi Gao, Cao Xiao, Lucas M. Glass, Jimeng Sun
• Abstract summary: We propose CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching.
• Score: 70.08786840301435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks.

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