Publications by authors named "M Ripperger"
Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) with genetic data to understand which decisions may affect performance.
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- Post-marketing safety surveillance can be improved by detecting clinical events through spontaneous reporting, but it requires healthcare professionals to be well-informed and aware of the reporting process.
- The study introduces a new method for identifying incidents using unstructured clinical data and natural language processing, validated against traditional methods for two specific health concerns: suicide attempts and sleep-related behaviors.
- Results showed that while the new approach effectively identified suicide attempts with decent precision, it struggled more with sleep-related behaviors; additionally, performance varied by race, highlighting the need for careful monitoring and bias reduction in healthcare AI applications.
Article Synopsis
- This study focuses on treatment-resistant depression (TRD), which affects about one-third of major depressive disorder (MDD) patients, and aims to clarify its genetic basis since previous research hasn't pinpointed specific genetic markers.* -
- Researchers used electroconvulsive therapy (ECT) as an indicator of TRD and applied machine learning to analyze health records, performing a genome-wide association study involving over 154,000 patients in four large biobanks.* -
- The findings revealed low heritability estimates and identified two significant genetic loci associated with TRD, suggesting links to other traits like cognition and metabolism, which could have implications for future treatments.*