Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (2021)

Abstract: 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 pre-training* 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.

## introduction clinical professionals make decisions about patients under strong time constraints. the patient information at hand is often unstructured, e.g. in the form of clinical notes written by other medical personnel in limited time. clinical decision support (cds) systems can help in these scenarios by pointing towards related cases or certain risks. clinical outcome prediction is a fundamental task of cds systems, in which the patient's development is predicted based on data from their electronic health record (ehr). in this work we focus on textual ehr data available at admission time. figure 1 shows a sample admission note with highlighted parts that -according to medical doctorsmust be considered when evaluating a patient. encoding clinical notes with pre-trained language models. neural models need to extract relevant facts from such notes and learn complex relations between them in order to associate certain clinical outcomes. pre-trained language models such as bert @xcite have shown to be able to both extract information from noisy text and to capture task-specific relations in an end-to-end fashion @xcite . we thus base our work on these models and pose the following questions: simulating patients at admission time. existing work on text-based outcome prediction focuses on progress notes after a certain time of a patient's hospitalisation @xcite . this is mostly due to a lack of publicly available admission notes and poses some problems: 1) doctors might miss specific outcome risks early in admission and 2) progress notes already contain information about clinical decisions made on admission time @xcite . we propose to simulate newly arrived patients by extracting admission notes from mimic iii discharge summaries. we are thus able in-hospital mortality: not deceased length of stay: > 14 days symptoms & vitals general risk factors medications pre-conditions ## clinical admission to discharge task clinical outcome prediction can be defined in different ways. we approach the task from a doctor's perspective and predict the outcome of a current admission from the time of the patient's arrival to the hospital unit. we describe our setup as follows. ## integrating clinical knowledge into language models we propose clinical outcome pre-training, a way to integrate knowledge about clinical outcomes into pre-trained language models. we further introduce an additional step to incorporate icd code hierarchy into our multi-label classification tasks. foot_0 ## discussion and findings clinical outcome prediction is a sensitive task. we therefore conduct an extensive analysis on the core all model including a manual error analysis by medical doctors on 20 randomly chosen samples to understand how the model would perform in clinical practice. ## conclusion we reframe the task of clinical outcome prediction to consider the admission state of a patient and support doctors in their initial decision process. we show that current state-of-the-art language models outperform selected baselines on this task and present methods for further improvement: outcome pre-training enables our models to learn from unlabelled sources and icd+ incorporates hierarchical and textual icd representations into our models. for future work, we suggest considering pre-trained language models with larger context sizes @xcite and languages other than english @xcite . we further encourage work on semantic encoding of negated terms and numerical data from clinical text.