Diagnosing a patient requires consideration of a wide variety of factors: past medical history and comorbidities, physical exam findings, lab results, imaging, ECG findings, and in some cases, genomic testing. Clinical diagnosis and prognostic assessment currently relies on expert knowledge of the treating physician. Recent developments in machine learning make it possible to build automated clinical diagnostic and risk assessment tools using data from the electronic medical record.
This talk walks through the steps involved in building a clinical risk assessment model, using sepsis as a case study. A large part of the talk will focus on the tools and techniques involved in pre-processing complex medical data, and strategies for evaluating model results.