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Found 8 papers, 1 papers with code
Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation
Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality.
User-Driven Research of Medical Note Generation Software
A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations.
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation
In recent years, machine learning models have rapidly become better at generating clinical consultation notes; yet, there is little work on how to properly evaluate the generated consultation notes to understand the impact they may have on both the clinician using them and the patient's clinical safety.
PriMock57: A Dataset Of Primary Care Mock Consultations
Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably produce automatic transcripts of clinician-patient conversations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Towards more patient friendly clinical notes through language models and ontologies
Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.
Towards objectively evaluating the quality of generated medical summaries
We propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts.
A preliminary study on evaluating Consultation Notes with Post-Editing
We time this and find that it is faster than writing the note from scratch.
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks.
