# MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries

arXiv: https://arxiv.org/abs/2408.12980 · HTML: https://mlsanity.com/paper/2408.12980/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 66 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [L-Eval: Instituting Standardized Evaluation for Long Context Language Models](https://mlsanity.com/paper/2307.11088/) (2023)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://mlsanity.com/paper/1806.04185/) (2018)
- [Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://mlsanity.com/paper/1904.01606/) (2019)
- [PHEE: A Dataset for Pharmacovigilance Event Extraction from Text](https://mlsanity.com/paper/2210.12560/) (2022)
- [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021)
- [Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition](https://mlsanity.com/paper/2004.14514/) (2020)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes](https://mlsanity.com/paper/2106.02524/) (2021)
- [DICE: Data-Efficient Clinical Event Extraction with Generative Models](https://mlsanity.com/paper/2208.07989/) (2022)
- [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022)
- [MDACE: MIMIC Documents Annotated with Code Evidence](https://mlsanity.com/paper/2307.03859/) (2023)
- [ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them](https://mlsanity.com/paper/2005.07934/) (2020)
- [Mirror: A Universal Framework for Various Information Extraction Tasks](https://mlsanity.com/paper/2311.05419/) (2023)
- [Learning to Infer Entities, Properties and their Relations from Clinical Conversations](https://mlsanity.com/paper/1908.11536/) (2019)
- [NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques](https://mlsanity.com/paper/2102.12254/) (2021)
- [Detect and Classify -- Joint Span Detection and Classification for Health Outcomes](https://mlsanity.com/paper/2104.07789/) (2021)
- [DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling](https://mlsanity.com/paper/2109.08818/) (2021)
- [Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations](https://mlsanity.com/paper/2109.02254/) (2021)
- [An Empirical Study on Finding Spans](https://mlsanity.com/paper/2210.06824/) (2022)

## Built on by (most cited first, 4 of 4)

- [Three Generations of Healthcare IT: From the Digital Record to the Computable Care Process](https://mlsanity.com/paper/2608.08806/) (2026)
- [Linguistic Blind Spots in Clinical Decision Extraction](https://mlsanity.com/paper/2602.03942/) (2026)
- [Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction](https://mlsanity.com/paper/2605.06191/) (2026)
- [PrionNER: A Named Entity Recognition Dataset for Prion Disease Biomedical Literature](https://mlsanity.com/paper/2605.28375/) (2026)

## Abstract (arXiv)

Medical decisions directly impact individuals' health and well-being. Extracting decision spans from clinical notes plays a crucial role in understanding medical decision-making processes. In this paper, we develop a new dataset called "MedDec", which contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. We introduce the task of medical decision extraction, aiming to jointly extract and classify different types of medical decisions within clinical notes. We provide a comprehensive analysis of the dataset, develop a span detection model as a baseline for this task, evaluate recent span detection approaches, and employ a few metrics to measure the complexity of data samples. Our findings shed light on the complexities inherent in clinical decision extraction and enable future work in this area of research. The dataset and code are available through https://github.com/CLU-UML/MedDec.