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

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 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.

What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

“…In recent years, most approaches are based on deep neural networks due to their ability to outperform earlier methods in most settings. Most recently, Transformer-based models have been applied for prediction of patient outcomes with reported increases in performance Zhang et al, 2020a;Tuzhilin, 2020;Zhao et al, 2021;van Aken et al, 2021;Rasmy et al, 2021). In this work we analyse three Transformer-based models due to their upcoming prevalence in the application of NLP in health care.…”

“…We conduct our analysis on data from the MIMIC-III database (Johnson et al, 2016). In particular we use the outcome prediction task setup by van Aken et al (2021). The classification task includes 48,745 English admission notes annotated with the patients' clinical outcomes at discharge.…”

Clinical Prompt Learning with Frozen Language Models

“…Regardless of the size issues of the PLMs, there is still a real benefit in their application to new domains and downstream tasks through traditional fine-tuning, including the biomedical domain Huang et al [2019], Alsentzer et al [2019], van Aken et al [2021. The persistent concern is the need to fine-tune both the entire PLM and task specific head to produce viable performance on many tasks.…”

Literature-Augmented Clinical Outcome Prediction

“…With the advent of electronic health records (EHRs), more clinical data has become available to train AI models for outcome prediction (Rajkomar et al, 2018;Hashir and Sawhney, 2020). In particular, language models pretrained on biomedical and/or clinical text are demonstrating increasing proficiency when fine-tuned for the task of predicting outcomes such as in-hospital mortality or length of stay (van Aken et al, 2021).…”

“…only clinical notes (Boag et al, 2018;Hashir and Sawhney, 2020). Recent LM-based approaches van Aken et al (2021) have designed pretraining schemes over corpora of clinical notes and general biomedical literature. This is in contrast to our work, where we directly incorporate a literature retrieval mechanism into our outcome prediction model, by finding papers relevant to specific patient cases.…”