Ethan Steinberg

TL;DR: Data from tests for common respiratory viruses and SARS-CoV-2 in the health system was used to measure the ability to correctly classify virus test results based on presenting symptoms and found symptom-based screening may not be an effective strategy to identify individuals who should be tested for SARS -CoV2 infection or to obtain a leading indicator of new COVID-19 cases.

TL;DR: AMELIE could help clinicians narrow the field of possible causative genes, shortening the time required for expert diagnosis of Mendelian diseases, and was 3 to 19 times more efficient than hand-curated database–based approaches.

TL;DR: It is demonstrated that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant.

TL;DR: In this article, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules is presented, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data.

TL;DR: Trove, a framework for weakly supervised entity classification using medical ontologies, demonstrates how a wide range of medical entity classifiers can be quickly constructed using weak supervision and without requiring manually-labeled training data.