Leo Anthony Celi


Learning to Ask Like a Physician
Eric Lehman | Vladislav Lialin | Katelyn Edelwina Legaspi | Anne Janelle Sy | Patricia Therese Pile | Nicole Rose Alberto | Richard Raymund Ragasa | Corinna Victoria Puyat | Marianne Katharina Taliño | Isabelle Rose Alberto | Pia Gabrielle Alfonso | Dana Moukheiber | Byron Wallace | Anna Rumshisky | Jennifer Liang | Preethi Raghavan | Leo Anthony Celi | Peter Szolovits
Proceedings of the 4th Clinical Natural Language Processing Workshop

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG: https://github.com/elehman16/discq.


A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Edward T. Moseley | Joy T. Wu | Jonathan Welt | John Foote | Patrick D. Tyler | David W. Grant | Eric T. Carlson | Sebastian Gehrmann | Franck Dernoncourt | Leo Anthony Celi
Proceedings of the Twelfth Language Resources and Evaluation Conference

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it is critical to develop robust algorithms to infer patients’ conditions and treatments from their written notes. In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note. Nursing Progress Notes and Discharge Summaries from the Intensive Care Unit of a large tertiary care hospital were manually annotated for the presence of several high-context phenotypes relevant to treatment and risk of re-hospitalization. This dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. Each Discharge Summary and Progress Note has been annotated by at least two expert human annotators (one clinical researcher and one resident physician). Annotated phenotypes include treatment non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes. This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing.