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Impact of Diverse Data Sources on Computational Phenotyping
Liwei Wang1, Janet E Olson2,3, Suzette J Bielinski2
1Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
Frontiers in Genetics
|June 26, 2020
Summary
Integrating diverse data sources significantly improves computational phenotyping accuracy for rheumatoid arthritis and type 2 diabetes mellitus, overcoming limitations of data fragmentation in electronic health records.
Area of Science:
- Biomedical Informatics
- Health Informatics
- Computational Biology
Background:
- Electronic health records (EHRs) offer extensive data for computational phenotyping.
- Data fragmentation within single EHR sources limits the accuracy of automated phenotype extraction.
- Existing computational phenotyping algorithms face challenges due to incomplete data.
Purpose of the Study:
- To investigate the impact of data fragmentation on computational phenotyping algorithms.
- To evaluate the performance of rheumatoid arthritis (RA) and type 2 diabetes mellitus (T2DM) phenotyping algorithms using diverse data sources.
- To compare algorithm performance between a single health system (Mayo EHRs) and a multi-system linked dataset (Rochester Epidemiology Project - REP).
Main Methods:
- Utilized two established computational phenotyping algorithms for RA and T2DM.
- Applied algorithms to single-source Mayo EHR data and multi-source REP data.
- Assessed algorithm performance using metrics such as positive predictive value (PPV) and false-negative rate (FNR).
Main Results:
- Data fragmentation markedly impacted both RA and T2DM case selection accuracy.
- REP data demonstrated superior performance with higher PPV (97.2% for RA, 98.3% for T2DM) and lower FNR (5.2% for RA, 3.3% for T2DM) compared to Mayo EHRs.
- Mayo EHRs showed lower PPV (91.4% for RA, 92.4% for T2DM) and higher FNR (26.6% for RA, 14% for T2DM).
- Biases were also observed in T2DM control selection from Mayo data (PPV 91.2%, FNR 1.2%).
Conclusions:
- Diverse data sources, like the REP, significantly enhance the accuracy of computational phenotyping by mitigating data fragmentation.
- Utilizing multi-system linked EHR data is crucial for reliable phenotype extraction.
- Addressing data fragmentation is essential for advancing phenotype-driven research and improving healthcare delivery through EHR data.
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