| Challenge: | Existing models for extracting symptoms from clinical conversations are inherently difficult. |
| Approach: | They propose two new deep learning models tailored for a new application . they propose a hierarchical span-attribute tagging model and a sequence-to-sequence model . |
| Outcome: | The proposed models perform well under different conditions and are compared to existing models. |
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Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)
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| Challenge: | Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity. |
| Approach: | They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach. |
| Outcome: | The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties. |
Extracting relevant information from physician-patient dialogues for automated clinical note taking (D19-62)
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| Challenge: | a system that extracts pertinent medical information from dialogues between clinicians and patients is proposed . entering data into EMRs is currently slow and error-prone, and clinicians spend up to 50% of their time on data entry. |
| Approach: | They propose a system that automatically extracts medical information from dialogues between clinicians and patients using context and time information. |
| Outcome: | The proposed system extracts medical information from dialogues and automatically generates a patient note. |
The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
| Challenge: | Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking. |
| Approach: | They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model. |
| Outcome: | The proposed model is more useful than the F-scores reflect and can be used in clinical notes. |
Summarizing Medical Conversations via Identifying Important Utterances (2020.coling-main)
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| Challenge: | Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature. |
| Approach: | They propose to use a hierarchical encoder-tagger model to generate medical conversation summarization by identifying important utterances. |
| Outcome: | The proposed model outperforms baseline models and models and adds conversation-related features to improve performance. |
Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations (2023.emnlp-industry)
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| Challenge: | Utilizing natural language processing in clinical conversations is effective to improve the efficiency of workflows for medical staff and patients. |
| Approach: | They propose a model for dialogue segmentation and topic categorization that integrates natural language processing techniques into a joint model. |
| Outcome: | The proposed model improves on follow-up calls for diabetes management and reduces computational complexity and cost. |
An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023.eacl-main)
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| Challenge: | Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters. |
| Approach: | They propose to use a new dataset to generate automated and manual clinical notes from doctor-patient conversations in a clinical setting. |
| Outcome: | The proposed model could reduce the time spent writing clinical notes from doctor-patient conversations in a clinical setting. |
MIMICause: Representation and automatic extraction of causal relation types from clinical notes (2022.findings-acl)
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| Challenge: | Extracted causal information from clinical notes can be combined with structured EHR data such as demographics, diagnoses, and medications. |
| Approach: | They propose to annotate clinical notes and develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts. |
| Outcome: | The proposed annotation guidelines achieved a high inter-annotator agreement and a macro F1 score on the clinical text. |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation (2020.lrec-1)
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| Challenge: | Despite advances in natural language processing, converting a clinic visit conversation into a clinical note is a largely unexplored area of research. |
| Approach: | They propose an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences. |
| Outcome: | The proposed method is content- and technique-agnostic while associating note sentences to sets of dialogue sentences. |
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
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| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |