Postdoctoral Research Associate
Washington University School of Medicine in St Louis
February 2020 –
September 2024
- Clinical Model Development and Deployment
- Developed solutions for the problem of merging patient medical records from different hospitals or health recording systems using state-of-the-art deep learning techniques.
- Queried large open-source medical dataset MIMIC in PostgreSQL for solution’s performance demonstration.
- Validated these techniques by implementing self-designed PyTorch models on high-performance computing platforms in portable Docker containers to demonstrate improvement over existing methods.
- Developed tailor-made code in R using statistical techniques to process intra-operative medication data for manual entry or drug unit mismatch errors.
- Designed supervised prediction models for post-surgical complications (such as in-hospital mortality, cardiac arrest etc) in Python to inform clinicians of individualized patient risk. Utilized LSTM and attention-based deep networks to process intra-operatively recorded medications and vitals for model deployment using torch serve.
- Evaluation of clinically deployed models
- Designed risk prediction explanation methods using machine learning interpretability techniques while collaborating with domain expert colleagues on GitHub.
- Evaluated the deployed machine learning models that were being used in a telemedicine centre as part of a large randomized control trial for racial, sex-based or age-based discrimination.
- Conducted interviews with the clinicians to understand the impact of AI-generated risk predictions along with explanations on their decisions.
- Impact of social vulnerability on surgery outcomes
- Quantified zipcode based social economic status for patients from St Louis area using GIS tools.
- Conducted statistical analysis to study the effect of social vulnerability on the post surgical complications.