Project: SCH: INT: Collaborative Research: High-throughput Phenotyping on Electronic Health Records using Multi-Tensor Factorization
Funding Source: NSF Smart Connect Health Integrated Grant: Award Number 1418511
As the adoption of electronic health records (EHRs) has grown, EHRs are now composed of a diverse array of data, including structured information (e.g., diagnoses, medications, and lab results), molecular sequences, unstructured clinical progress notes, and social network information. There is mounting evidence that EHRs are a rich resource for clinical research, but they are notoriously difficult to leverage because of their orientation to healthcare business operations, heterogeneity across commercial systems, and high levels of missing or erroneous entries. Moreover, the interactions among different data sources within an EHR are challenging to model, hampering our ability to leverage traditional analytic frameworks. In recognition of this problem, various efforts have been undertaken to transform EHR data into concise and meaningful concepts, or phenotypes. Yet, to date, these efforts have been ad hoc and labor intensive, resulting in specific phenotypes for specific environments; e.g., type 2 diabetes in the EHR system at Vanderbilt University Medical Center (VUMC). There is an urgent need for scalable phenotyping methods, but several major challenges must be addressed, including: a) patient representation, b) high-throughput phenotype generation from EHRs, c) expert-guided phenotype refinement, and d) phenotype adaptation across institutions. The goal of this project is to address these challenges by developing a general computational framework for transforming EHR data into meaningful phenotypes with only modest levels of expert guidance. The PIs will develop novel courses on Healthcare Analytics as a Massive Open Online Course (MOOC) that covers cross-disciplinary topics at the confluence of computer science and medical informatics, while embellishing existing graduate courses on biomedical informatics. The PIs plan to deliver tutorials and organize workshops at relevant computer science and medical informatics conferences with the goal of sharing research results and developing a community. The PIs will develop outreach modules that focus on freshmen and under-represented students, as well as educational sessions for clinical researchers who are currently performing phenotyping in academic medical centers. Thus, the project has a significant component the integrates research and education as well as providing for new scientific insights.
In support of this goal, the team plans to represent and analyze EHR data as inter-connected high-order relations i.e. tensors (e.g. tuples of patient-medication-diagnosis, patient-lab, and patient-symptoms). The proposed analytic framework generalizes several existing data mining methodologies, including dimensionality reduction, topic modeling and co-clustering, which all arise as limited special cases of analyzing second order tensors. It will also enable flexible refinement of candidates to adapt phenotypes from one healthcare institution to another, and will incorporate feedback from domain experts. The accompanying suite of algorithms and methods will enable the automation of high-throughput phenotype generation, refinement, adaptation and applications, in a broad range of health informatics settings and across multiple institutions. This project will integrate biomedical informaticists, computer scientists, and clinical experts. The significance of the resulting phenotypes in diverse clinical applications, including: a) cohort construction, where case and control patients are identified with respect to specific phenotype combinations; b) genome wide association studies (GWAS), where target phenotypes of patients are tested against DNA sequence variation for significant statistical associations; and c) clinical predictive modeling, where a model is developed to predict target phenotypes or diseases will be demonstrated. The framework will be developed with public accessible data from MIMIC-II and CMS and validate in real clinical environments at Northwestern Memorial Hospital and VUMC through several high-impact disease targets (including hypertension, type 2 diabetes, hypothyroidism, atrial fibrillation, rheumatoid arthritis, and multiple sclerosis). Additionally, the methodologies developed through this project will be integrated into existing software platforms that support the representation of EHR-derived phenotypes, but lack a data-driven component for the generation and refinement of candidates. Overall, the proposed framework is expected to have a major impact on translational clinical research including clinical trial design, predictive modeling, epidemiology studies and clinical decision support.
- Task 1: Phenotyping Generation: How to turn EHR data into meaningful clinical concepts (Phenotypes)?
- Task 2: Phenotyping Refinement: How to incoporate feedback to ensure the generated phenotypes clinically meaningful?
- Task 3: Phenotyping Adaptation: How to port phenotypes from one institution to another?
- App 1: Cohort Construction: Validate the generated phenotypes recover some existing phenotypes (from PheKB)
- App 2: GWAS: Develop genomic-wide association studies using the generated phenotypes (as target or control variables)
- App 3:Predictive modeling: Use generated phenotypes as features to faciliate predictive modeling
Ho, Joyce C., Joydeep Ghosh, Steve R. Steinhubl, Walter F. Stewart, Joshua C. Denny, Bradley A. Malin, and Jimeng Sun. “Limestone: High-Throughput Candidate Phenotype Generation via Tensor Factorization.” Journal of Biomedical Informatics. Accessed August 8, 2014. doi:10.1016/j.jbi.2014.07.001
- Joyce Ho, Joydeep Ghosh, Jimeng Sun. Marble: High-throughput Phenotyping from Electronic Health Records via Sparse Nonnegative Tensor Factorization. ACM SIGKDD, 2014