Wen-wai Yim


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Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization
Wen-wai Yim | Meliha Yetisgen
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations

Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system.


Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation
Wen-wai Yim | Meliha Yetisgen | Jenny Huang | Micah Grossman
Proceedings of the Twelfth Language Resources and Evaluation Conference

For every patient’s visit to a clinician, a clinical note is generated documenting their medical conversation, including complaints discussed, treatments, and medical plans. Despite advances in natural language processing, automating clinical note generation from a clinic visit conversation is a largely unexplored area of research. Due to the idiosyncrasies of the task, traditional methods of corpus creation are not effective enough approaches for this problem. In this paper, we present an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences. The sets can further be grouped with higher order tags to mark sets with related information. This direct linkage from input to output decouples the annotation from specific language understanding or generation strategies. Here we provide data statistics and qualitative analysis describing the unique annotation challenges. Given enough annotated data, such a resource would support multiple modeling methods including information extraction with template language generation, information retrieval type language generation, or sequence to sequence modeling.


Automatic rubric-based content grading for clinical notes
Wen-wai Yim | Ashley Mills | Harold Chun | Teresa Hashiguchi | Justin Yew | Bryan Lu
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.


Annotation of pain and anesthesia events for surgery-related processes and outcomes extraction
Wen-wai Yim | Dario Tedesco | Catherine Curtin | Tina Hernandez-Boussard
BioNLP 2017

Pain and anesthesia information are crucial elements to identifying surgery-related processes and outcomes. However pain is not consistently recorded in the electronic medical record. Even when recorded, the rich complex granularity of the pain experience may be lost. Similarly, anesthesia information is recorded using local electronic collection systems; though the accuracy and completeness of the information is unknown. We propose an annotation schema to capture pain, pain management, and anesthesia event information.


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In-depth annotation for patient level liver cancer staging
Wen-wai Yim | Sharon Kwan | Meliha Yetisgen
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis