A novel method for Causal Structure Discovery from EHR data: A demonstration on type-2 diabetes mellitus

Autor:	Pedro J. Caraballo, György J. Simon, M. Regina Castro, Sisi Ma, Prashanthi Vemuri, Xinpeng Shen
Rok vydání:	2021
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer science Disease mechanisms Causal structure computer.software_genre Causality Machine Learning (cs.LG) 3. Good health Graph (abstract data type) Generalizability theory Data mining Completeness (statistics) computer
Popis:	Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive, sometimes infeasible or unethical. Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data. We propose a new data transformation method and a novel CSD algorithm to overcome the challenges posed by these characteristics. Materials and methods: We demonstrated the proposed methods on an application to type-2 diabetes mellitus. We used a large EHR data set from Mayo Clinic to internally evaluate the proposed transformation and CSD methods and used another large data set from an independent health system, Fairview Health Services, as external validation. We compared the performance of our proposed method to Fast Greedy Equivalence Search (FGES), a state-of-the-art CSD method in terms of correctness, stability and completeness. We tested the generalizability of the proposed algorithm through external validation. Results and conclusions: The proposed method improved over the existing methods by successfully incorporating study design considerations, was robust in face of unreliable EHR timestamps and inferred causal effect directions more correctly and reliably. The proposed data transformation successfully improved the clinical correctness of the discovered graph and the consistency of edge orientation across bootstrap samples. It resulted in superior accuracy, stability, and completeness. Comment: 20 pages, 2 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32e38e0a91c9f5883edd1dcfdfd74ab6 https://doi.org/10.21203/rs.3.rs-486814/v1 Zobrazit plný text záznamu