{"url":"/sota/medical-code-prediction-on-mimic-iv-icd-10-1","task":{"name":"Medical Code Prediction","url":"/task/medical-code-prediction","note":null},"dataset":{"name":"MIMIC-IV-ICD-10-full","url":"/dataset/mimic-iv-icd-10-full"},"category":"Medical","categories":["Medical"],"category_note":null,"description":"Context: Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort by human coders today. A new milestone will mark a meaningful step toward fully Autonomous Medical Coding in machines reaching parity with human coders' performance in medical code prediction.\r\n\r\nQuestion: What exactly is the medical code prediction problem?\r\n\r\nAnswer: Clinical notes contain much information about what precisely happened during the patient's entire stay. And those clinical notes (e.g., discharge summary) is typically long, loosely structured, consists of medical domain language, and sometimes riddled with spelling errors. So, it's a highly multi-label classification problem, and the forthcoming ICD-11 standard will add more complexity to the problem! The medical code prediction problem is to annotate this clinical note with multiple codes subset from nearly 70K total codes (in the current ICD-10 system, for example).","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Macro-AUC","Micro-AUC","Macro-F1","Micro-F1","Precision@8"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Macro-AUC":"higher","Micro-AUC":"higher","Macro-F1":"higher","Micro-F1":"higher","Precision@8":"higher"}},"counts":{"rows":5,"rows_with_code":5,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MSMN","metrics":{"Macro-AUC":"97.07","Macro-F1":"5.42","Micro-AUC":"99.61","Micro-F1":"55.91","Precision@8":"67.66"},"uses_additional_data":false,"paper_date":"2023-04-27","paper":"/paper/mimic-iv-icd-a-new-benchmark-for-extreme-1","paper_url":"https://arxiv.org/abs/2304.13998v1","paper_title":"Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification","code":"https://github.com/thomasnguyen92/MIMIC-IV-ICD-data-processing","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Joint LAAT","metrics":{"Macro-AUC":"93.64","Macro-F1":"5.71","Micro-AUC":"99.27","Micro-F1":"55.89","Precision@8":"66.89"},"uses_additional_data":false,"paper_date":"2023-04-27","paper":"/paper/mimic-iv-icd-a-new-benchmark-for-extreme-1","paper_url":"https://arxiv.org/abs/2304.13998v1","paper_title":"Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification","code":"https://github.com/thomasnguyen92/MIMIC-IV-ICD-data-processing","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"LAAT","metrics":{"Macro-AUC":"92.96","Macro-F1":"4.47","Micro-AUC":"99.14","Micro-F1":"55.40","Precision@8":"66,97"},"uses_additional_data":false,"paper_date":"2023-04-27","paper":"/paper/mimic-iv-icd-a-new-benchmark-for-extreme-1","paper_url":"https://arxiv.org/abs/2304.13998v1","paper_title":"Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification","code":"https://github.com/thomasnguyen92/MIMIC-IV-ICD-data-processing","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"PLM","metrics":{"Macro-AUC":"91.85","Macro-F1":"4.90","Micro-AUC":"99.02","Micro-F1":"56.95","Precision@8":"69.47"},"uses_additional_data":false,"paper_date":"2023-04-27","paper":"/paper/mimic-iv-icd-a-new-benchmark-for-extreme-1","paper_url":"https://arxiv.org/abs/2304.13998v1","paper_title":"Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification","code":"https://github.com/thomasnguyen92/MIMIC-IV-ICD-data-processing","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"CAML","metrics":{"Macro-AUC":"89.91","Macro-F1":"4.07","Micro-AUC":"98.79","Micro-F1":"52.67","Precision@8":"64.43"},"uses_additional_data":false,"paper_date":"2023-04-27","paper":"/paper/mimic-iv-icd-a-new-benchmark-for-extreme-1","paper_url":"https://arxiv.org/abs/2304.13998v1","paper_title":"Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification","code":"https://github.com/thomasnguyen92/MIMIC-IV-ICD-data-processing","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. 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