Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE International Conference on Digital Health |
| Subtitle of host publication | ICDH |
| Editors | Carl K. Chang, Rong N. Chang, Jing Fan, Geoffrey C. Fox, Zhi Jin, Graziano Pravadelli, Hossain Shahriar |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 328-338 |
| Number of pages | 11 |
| ISBN (electronic) | 9798350341034 |
| ISBN (print) | 979-8-3503-4104-1 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE International Conference on Digital Health - Hybrid, Chicago, United States Duration: 2 Jul 2023 → 8 Jul 2023 |
Abstract
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.
Keywords
- citation network, clinical trial, interpretability, metapath-based similarity search
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Medicine(all)
- Health Informatics
Sustainable Development Goals
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2023 IEEE International Conference on Digital Health: ICDH. ed. / Carl K. Chang; Rong N. Chang; Jing Fan; Geoffrey C. Fox; Zhi Jin; Graziano Pravadelli; Hossain Shahriar. Institute of Electrical and Electronics Engineers Inc., 2023. p. 328-338.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -