Dataset Information

A Data-Driven Pipeline to Discover Treatment Variations and the Associated Contributing Factors Balanced with Optimal Granularity.

ABSTRACT: Evidence-based medicine utilizes research evidence from clinical trials to support treatment decisions. To leverage the advantage of electronic health records and big data analysis methods, we developed a data-driven analytic pipeline that uses 1) agglomerative hierarchical clustering to define different granularity of treatment variation, 2) feature selection and multinomial multivariate logistic regression analysis to identify variables (factors) associated with treatment variation, and 3) prognosis analysis to compare patient outcome across top treatment groups. We tested our approach on the diffuse large B-cell lymphoma patient population from the MIMIC-IV dataset and found that our approach helps determine the optimal granularity of treatment variation and identify factors associated with treatment variation but not realized in randomized controlled trials due to unbalanced patient cohorts. We also found some patient cohorts' characteristics that could serve to inspire hypothesis generation, such as the influence of ethnicity on the treatment plans and subsequent prognoses.

SUBMITTER: Fan H

PROVIDER: S-EPMC10148325 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A Data-Driven Pipeline to Discover Treatment Variations and the Associated Contributing Factors Balanced with Optimal Granularity.

Fan Hao H Lim Kian-Huat KH Yen Po-Yin PY

AMIA ... Annual Symposium proceedings. AMIA Symposium 20220101

Evidence-based medicine utilizes research evidence from clinical trials to support treatment decisions. To leverage the advantage of electronic health records and big data analysis methods, we developed a data-driven analytic pipeline that uses 1) agglomerative hierarchical clustering to define different granularity of treatment variation, 2) feature selection and multinomial multivariate logistic regression analysis to identify variables (factors) associated with treatment variation, and 3) pro ...[more]

PMID: 37128379

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Project description:ObjectiveIn applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR.Materials and methodsLargely data-driven, FIDDLE systematically transforms structured EHR data into feature vectors, limiting the number of decisions a user must make while incorporating good practices from the literature. To demonstrate its utility and flexibility, we conducted a proof-of-concept experiment in which we applied FIDDLE to 2 publicly available EHR data sets collected from intensive care units: MIMIC-III and the eICU Collaborative Research Database. We trained different ML models to predict 3 clinically important outcomes: in-hospital mortality, acute respiratory failure, and shock. We evaluated models using the area under the receiver operating characteristics curve (AUROC), and compared it to several baselines.ResultsAcross tasks, FIDDLE extracted 2,528 to 7,403 features from MIMIC-III and eICU, respectively. On all tasks, FIDDLE-based models achieved good discriminative performance, with AUROCs of 0.757-0.886, comparable to the performance of MIMIC-Extract, a preprocessing pipeline designed specifically for MIMIC-III. Furthermore, our results showed that FIDDLE is generalizable across different prediction times, ML algorithms, and data sets, while being relatively robust to different settings of user-defined arguments.ConclusionsFIDDLE, an open-source preprocessing pipeline, facilitates applying ML to structured EHR data. By accelerating and standardizing labor-intensive preprocessing, FIDDLE can help stimulate progress in building clinically useful ML tools for EHR data.

Dataset Information

A Data-Driven Pipeline to Discover Treatment Variations and the Associated Contributing Factors Balanced with Optimal Granularity.

Publications

A Data-Driven Pipeline to Discover Treatment Variations and the Associated Contributing Factors Balanced with Optimal Granularity.

Similar Datasets

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