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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knowledge Learning Symbiosis for Developing Risk Prediction Models from Regional EHR Repositories.

Jing Mei1, Eryu Xia1

  • 1IBM Research, Beijing, China.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces knowledge learning symbiosis (KLS) to improve electronic health record (EHR) data analysis. KLS enhances cardiovascular disease risk prediction models, especially with incomplete or biased EHR data.

Keywords:
Electronic Health RecordsMachine LearningRisk Assessment

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Area of Science:

  • Health Informatics
  • Machine Learning
  • Biomedical Data Science

Background:

  • Secondary use of regional electronic health record (EHR) data is challenged by data selection bias and incompleteness, limiting data size and utility.
  • Existing methods struggle to effectively leverage domain knowledge for improving EHR data analysis.

Purpose of the Study:

  • To propose a novel framework, knowledge learning symbiosis (KLS), for incorporating domain knowledge to enhance secondary use of EHR data.
  • To address data selection bias and incompleteness issues in EHR datasets.
  • To improve the performance of predictive models built on EHR data.

Main Methods:

  • Developed the knowledge learning symbiosis (KLS) framework, integrating domain knowledge into EHR data analysis.
  • Introduced three categories of methods: knowledge injection into input features, objective functions, and output labels.
  • Pioneered the knowledge-enhanced neural network (KENN) for injecting knowledge into objective functions.
  • Conducted a case study using a type 2 diabetes patient cohort from regional EHR repositories to predict cardiovascular disease risk.

Main Results:

  • The KLS framework successfully improved cardiovascular disease risk prediction performance on small and biased EHR data.
  • Incorporating a well-established knowledge risk model as domain knowledge within the KLS framework yielded significant performance gains.
  • The knowledge-enhanced neural network (KENN) demonstrated superior performance compared to other methods evaluated.

Conclusions:

  • Knowledge learning symbiosis (KLS) provides an effective approach to overcome limitations in secondary EHR data use.
  • KENN offers a powerful method for integrating domain knowledge into predictive models, particularly for cardiovascular disease risk.
  • The proposed framework enhances the reliability and accuracy of predictive models derived from real-world, often imperfect, EHR data.