Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose clinical outcome pretraining to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.PRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin and NIDDM presented to ED with the worst headache of his life. He had a syncopal episode and was intubated by EMS. Medication on admission: 1mg IV ativan x 1.

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Passage retrieval is the task of retrieving only the portions of a document that are relevant to a particular information need. One application medical doctors and researchers face is the challenge of reading a large amount of novel literature. For example, since the outbreak of Coronavirus disease 2019 (COVID-19), tens of thousands of papers have been published each month about the disease. We demonstrate how we can support healthcare professionals in this exploratory research task with our neural passage retrieval system based on Contextualized Discourse Vectors (CDV). CDV captures the discourse of long documents on sentence level and allows to query a large corpus with medical entities and aspects. Our demonstration covers over 27,000 diseases and 14,000 clinical aspects including symptoms, diagnostics, treatments and medications. It returns passages and highlights sentences to effectively answer clinical queries with up to 65% Recall@1. We showcase our system on the COVID-19 Open Research Dataset (CORD-19), Orphanet and Wikipedia diseases corpora.1 https://allenai.org/. 2 https://covid19-research-explorer.appspot.com/.3 https://spike.covid-19.apps.allenai.org/search/covid19.

Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose clinical outcome pretraining to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.