TY - GEN

T1 - Interpretable Clinical Trial Search using Pubmed Citation Network

AU - Roy, Soumyadeep

AU - Ganguly, Niloy

AU - Sural, Shamik

AU - Rudra, Koustav

N1 - Funding Information: ACKNOWLEDGMENT Soumyadeep Roy is supported by the Institute Ph.D. Fellowship at the Indian Institute of Technology Kharagpur. This research was funded in part by the Federal Ministry of Education and Research (BMBF), Germany, under the project LeibnizKILabor with grant No. 01DD20003, and the IMPRINT-funded project titled “Development of a Remote Healthcare Delivery System: Early Diagnosis, Therapy, Follow-up and Preventive Care for Non-communicable Diseases (Cardio-pulmonary)”. Furthermore, this work is supported in part by the Science and Engineering Research Board, Department of Science and Technology, Government of India, under Project SRG/2022/001548. Koustav Rudra is a recipient of the DST-INSPIRE Faculty Fellowship [DST/INSPIRE/04/2021/003055] in the year 2021 under Engineering Sciences.

PY - 2023

Y1 - 2023

N2 - Clinical trials are an essential source of information for practicing Evidence-Based Medicine because they help to determine the efficacy of newly developed treatments and drugs. However, most of the existing trial search systems focus on a specific disease (e.g., cancer) and utilize disease-specific knowledge bases that hinder the adaptation of such methods to new diseases. In this work, we overcome both limitations and propose a graph-based model that explores both clinical trials and the Pubmed databases to alleviate the shortage of relevant clinical trials for a query. We construct a large heterogeneous graph (750K nodes and 1.2 Million edges) made of clinical trials and Pubmed articles linked to clinical trials. As both the graph edges and nodes are labeled, we develop a novel metapath-based similarity search (MPSS) method to retrieve and rank clinical trials across multiple disease classes. We primarily focus on consumers and users that do not have any prior medical knowledge. As there are no multiple disease-wide trial search evaluation datasets, we contribute a high-quality, well-annotated query-relevant trial set comprising around 25 queries and, on average, approximately 95 annotated trials per query. We also perform a detailed evaluation of MPSS on the TREC Precision Medicine Benchmark Dataset, a disease-specific clinical trial search setting. We make all the codes and data publicly available at https://github.com/roysoumya/MPSS-clinical-trial-search.

AB - Clinical trials are an essential source of information for practicing Evidence-Based Medicine because they help to determine the efficacy of newly developed treatments and drugs. However, most of the existing trial search systems focus on a specific disease (e.g., cancer) and utilize disease-specific knowledge bases that hinder the adaptation of such methods to new diseases. In this work, we overcome both limitations and propose a graph-based model that explores both clinical trials and the Pubmed databases to alleviate the shortage of relevant clinical trials for a query. We construct a large heterogeneous graph (750K nodes and 1.2 Million edges) made of clinical trials and Pubmed articles linked to clinical trials. As both the graph edges and nodes are labeled, we develop a novel metapath-based similarity search (MPSS) method to retrieve and rank clinical trials across multiple disease classes. We primarily focus on consumers and users that do not have any prior medical knowledge. As there are no multiple disease-wide trial search evaluation datasets, we contribute a high-quality, well-annotated query-relevant trial set comprising around 25 queries and, on average, approximately 95 annotated trials per query. We also perform a detailed evaluation of MPSS on the TREC Precision Medicine Benchmark Dataset, a disease-specific clinical trial search setting. We make all the codes and data publicly available at https://github.com/roysoumya/MPSS-clinical-trial-search.

KW - citation network

KW - clinical trial

KW - interpretability

KW - metapath-based similarity search

UR - http://www.scopus.com/inward/record.url?scp=85172388666&partnerID=8YFLogxK

U2 - 10.1109/ICDH60066.2023.00056

DO - 10.1109/ICDH60066.2023.00056

M3 - Conference contribution

AN - SCOPUS:85172388666

SN - 979-8-3503-4104-1

SP - 328

EP - 338

BT - 2023 IEEE International Conference on Digital Health

A2 - Chang, Carl K.

A2 - Chang, Rong N.

A2 - Fan, Jing

A2 - Fox, Geoffrey C.

A2 - Jin, Zhi

A2 - Pravadelli, Graziano

A2 - Shahriar, Hossain

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Digital Health

Y2 - 2 July 2023 through 8 July 2023

ER -