S2-E20.3 – How Can We Best Use NASHmap in Clinical Trials and Patient Treatment?

The Surfers explore the impact that NASHmap, the first Machine Learning model that can identify patients likely to have NASH, can have on clinical trials and patient treatment.

Prof. Schattenberg and colleagues built the NASHmap model from a NIDDK database and validated it using the Optum de-identified EHR dataset. The model includes 14 variables, some obvious, others less so, that produce an AUC of 0.82 in the NIDDK database and 0.79 in the Optum database. This conversation explores NASHmap’s powerful implications for future clinical trial recruitment and large-scale patient screening.

TOPICS: Diabetes, Diagnostic Tests, Elastography, FibroScan, NAFLD, NASH, population screening, Optum, Machine Learning, Artificial Intelligence, XGBoost, EHR, Electronic Health Records, Clinical Trial Recruitment

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