Clinical Aligned Interpretable Graph-Based Modeling for Intelligent System in Predictive Medicine
The preponderance of Electronic Health Records (EHRs) motivates and enables the exploitation of predictive measures, e.g., Artificial Intelligence (AI), in support of personalized medicine. These measures aid physicians in their daily clinical practice by predicting possible disease diagnoses and outcomes of drug prescriptions. With well studied data mining techniques and machine learning models, e.g., deep neural networks, building such systems seems relatively straightforward. However, in practice vast difficulties exists. Real-world EHRs are noisy and are abundant with temporal relationships, hindering deep neural networks performance, preventing clinical applicability. Equally important for clinical acceptance is interpretability; clinicians must understand the reasoning behind a predicted outcome. Unfortunately, existing approaches fail to achieve both high accuracy and model interpretability.I develop graphical representations of EHRs for better model interpretability. Additionally, the developed graph kernels achieve noise resistance and obtain superior accuracy. Distance-based kernel learning with rigorous empirical evaluation concludes a guideline of distance selection for variety of data distribution. Thus, using the proposed approach, I address both aforementioned issues.
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