{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/clinical-outcome-prediction-from-admission","title":"Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration","arxiv_id":"2102.04110","date":"2021-02-08","proceeding":"EACL 2021 2","authors":["Betty van Aken","Jens-Michalis Papaioannou","Manuel Mayrdorfer","Klemens Budde","Felix A. Gers","Alexander Löser"],"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.","url_abs":"https://arxiv.org/abs/2102.04110v1","url_pdf":"https://arxiv.org/pdf/2102.04110v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"clinical-outcome-prediction-from-admission","repo_url":"https://github.com/bvanaken/clinical-outcome-prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"length-of-stay-prediction","task_name":"Length-of-Stay prediction"},{"task_slug":"medical-diagnosis","task_name":"Medical Diagnosis"},{"task_slug":"medical-procedure","task_name":"Medical Procedure"},{"task_slug":"mortality-prediction","task_name":"Mortality Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[{"slug":"hospital-admission-notes-from-mimic-iii","name":"Clinical Admission Notes from MIMIC-III","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/length-of-stay-prediction-on-clinical","task":"Length-of-Stay prediction","dataset":"Clinical Admission Notes from MIMIC-III","model":"CORe","rank_in_archive_order":1,"of":3,"metrics":{"AUROC":"72.53"},"uses_additional_data":true},{"leaderboard":"/sota/length-of-stay-prediction-on-clinical","task":"Length-of-Stay prediction","dataset":"Clinical Admission Notes from MIMIC-III","model":"BioBERT Base","rank_in_archive_order":2,"of":3,"metrics":{"AUROC":"71.59"},"uses_additional_data":true},{"leaderboard":"/sota/length-of-stay-prediction-on-clinical","task":"Length-of-Stay prediction","dataset":"Clinical Admission Notes from MIMIC-III","model":"BERT Base","rank_in_archive_order":3,"of":3,"metrics":{"AUROC":"70.40"},"uses_additional_data":true},{"leaderboard":"/sota/medical-diagnosis-on-clinical-admission-notes","task":"Medical Diagnosis","dataset":"Clinical Admission Notes from MIMIC-III","model":"CORe","rank_in_archive_order":1,"of":2,"metrics":{"AUROC":"83.54"},"uses_additional_data":true},{"leaderboard":"/sota/medical-diagnosis-on-clinical-admission-notes","task":"Medical Diagnosis","dataset":"Clinical Admission Notes from MIMIC-III","model":"BioBERT Base","rank_in_archive_order":2,"of":2,"metrics":{"AUROC":"82.81"},"uses_additional_data":true},{"leaderboard":"/sota/medical-procedure-on-clinical-admission-notes","task":"Medical Procedure","dataset":"Clinical Admission Notes from MIMIC-III","model":"CORe","rank_in_archive_order":1,"of":3,"metrics":{"AUROC":"88.37"},"uses_additional_data":true},{"leaderboard":"/sota/medical-procedure-on-clinical-admission-notes","task":"Medical Procedure","dataset":"Clinical Admission Notes from MIMIC-III","model":"BioBERT Base","rank_in_archive_order":2,"of":3,"metrics":{"AUROC":"86.36"},"uses_additional_data":true},{"leaderboard":"/sota/medical-procedure-on-clinical-admission-notes","task":"Medical Procedure","dataset":"Clinical Admission Notes from MIMIC-III","model":"BERT Base","rank_in_archive_order":3,"of":3,"metrics":{"AUROC":"85.84"},"uses_additional_data":true},{"leaderboard":"/sota/mortality-prediction-on-clinical-admission","task":"Mortality Prediction","dataset":"Clinical Admission Notes from MIMIC-III","model":"CORe","rank_in_archive_order":1,"of":3,"metrics":{"AUROC":"84.04"},"uses_additional_data":true},{"leaderboard":"/sota/mortality-prediction-on-clinical-admission","task":"Mortality Prediction","dataset":"Clinical Admission Notes from MIMIC-III","model":"BioBERT Base","rank_in_archive_order":2,"of":3,"metrics":{"AUROC":"82.55"},"uses_additional_data":true},{"leaderboard":"/sota/mortality-prediction-on-clinical-admission","task":"Mortality Prediction","dataset":"Clinical Admission Notes from MIMIC-III","model":"BERT Base","rank_in_archive_order":3,"of":3,"metrics":{"AUROC":"81.13"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2102.04110","atlas_url":"https://app.syntology.ai/?focus=2102.04110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.04110"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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