# Extracting Symptoms and their Status from Clinical Conversations

arXiv: https://arxiv.org/abs/1906.02239 · HTML: https://mlsanity.com/paper/1906.02239/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets](https://mlsanity.com/paper/2204.08997/) (2022)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts](https://mlsanity.com/paper/2405.13179/) (2024)
- [MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations](https://mlsanity.com/paper/2410.14204/) (2024)
- [ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://mlsanity.com/paper/2109.00430/) (2021)
- [DialMed: A Dataset for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2203.07094/) (2022)
- [Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction](https://mlsanity.com/paper/2104.02219/) (2021)
- [Medical Dialogue Response Generation with Pivotal Information Recalling](https://mlsanity.com/paper/2206.08611/) (2022)
- [KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition](https://mlsanity.com/paper/2305.16833/) (2023)
- [Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges](https://mlsanity.com/paper/2405.10630/) (2024)
- [A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction](https://mlsanity.com/paper/2307.16200/) (2023)
- [MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System](https://mlsanity.com/paper/2111.09381/) (2021)
- [Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation](https://mlsanity.com/paper/2204.10178/) (2022)
- [Extracting Semantic Process Information from the Natural Language in Event Logs](https://mlsanity.com/paper/2103.11761/) (2021)
- [ESSumm: Extractive Speech Summarization from Untranscribed Meeting](https://mlsanity.com/paper/2209.06913/) (2022)
- [Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling](https://mlsanity.com/paper/2211.15544/) (2022)
- [Doctor Recommendation in Online Health Forums via Expertise Learning](https://mlsanity.com/paper/2203.02932/) (2022)
- [RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences](https://mlsanity.com/paper/2203.03543/) (2022)
- [Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations](https://mlsanity.com/paper/2206.02428/) (2022)
- [Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy](https://mlsanity.com/paper/2607.18984/) (2026)

## Abstract (arXiv)

This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly available corpus in this privacy-sensitive domain led us to develop our own corpus, consisting of about 3K conversations annotated by professional medical scribes. We propose two novel deep learning approaches to infer the symptom names and their status: (1) a new hierarchical span-attribute tagging (SAT) model, trained using curriculum learning, and (2) a variant of sequence-to-sequence model which decodes the symptoms and their status from a few speaker turns within a sliding window over the conversation. This task stems from a realistic application of assisting medical providers in capturing symptoms mentioned by patients from their clinical conversations. To reflect this application, we define multiple metrics. From inter-rater agreement, we find that the task is inherently difficult. We conduct comprehensive evaluations on several contrasting conditions and observe that the performance of the models range from an F-score of 0.5 to 0.8 depending on the condition. Our analysis not only reveals the inherent challenges of the task, but also provides useful directions to improve the models.