Towards more patient friendly clinical notes through language models and ontologies

• URL: http://arxiv.org/abs/2112.12672v1
• Date: Thu, 23 Dec 2021 16:11:19 GMT
• Title: Towards more patient friendly clinical notes through language models and ontologies
• Authors: Francesco Moramarco, Damir Juric, Aleksandar Savkov, Jack Flann, Maria Lehl, Kristian Boda, Tessa Grafen, Vitalii Zhelezniak, Sunir Gohil, Alex Papadopoulos Korfiatis, Nils Hammerla
• Abstract summary: We present a novel approach to automated medical text based on word simplification and language modelling. We use a new dataset pairs of publicly available medical sentences and a version of them simplified by clinicians. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning.
• Score: 57.51898902864543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians time. We present a novel approach to automated simplification of medical text based on word frequencies and language modelling, grounded on medical ontologies enriched with layman terms. We release a new dataset of pairs of publicly available medical sentences and a version of them simplified by clinicians. Also, we define a novel text simplification metric and evaluation framework, which we use to conduct a large-scale human evaluation of our method against the state of the art. 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.

Related papers

• LLMs Accelerate Annotation for Medical Information Extraction [7.743388571513413]
  We propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating ground truth labels for medical text annotation. We rigorously evaluate our method on a medical information extraction task, demonstrating that our approach not only substantially cuts down on human intervention but also maintains high accuracy.
  arXiv  Detail & Related papers  (2023-12-04T19:26:13Z)
• Multilingual Simplification of Medical Texts [49.469685530201716]
  We introduce MultiCochrane, the first sentence-aligned multilingual text simplification dataset for the medical domain in four languages. We evaluate fine-tuned and zero-shot models across these languages, with extensive human assessments and analyses. Although models can now generate viable simplified texts, we identify outstanding challenges that this dataset might be used to address.
  arXiv  Detail & Related papers  (2023-05-21T18:25:07Z)
• Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
  We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning. We demonstrate the effectiveness of our method in generating patient discharge instructions.
  arXiv  Detail & Related papers  (2022-10-23T16:34:39Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• Word-level Text Highlighting of Medical Texts forTelehealth Services [0.0]
  This paper aims to show how different text highlighting techniques can capture relevant medical context. Three different word-level text highlighting methodologies are implemented and evaluated. The results of our experiments show that the neural network approach is successful in highlighting medically-relevant terms.
  arXiv  Detail & Related papers  (2021-05-21T15:13:54Z)
• Paragraph-level Simplification of Medical Texts [35.650619024498425]
  Manual simplification does not scale to the rapidly growing body of biomedical literature. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts.
  arXiv  Detail & Related papers  (2021-04-12T18:56:05Z)
• Automated Lay Language Summarization of Biomedical Scientific Reviews [16.01452242066412]
  Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. Medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. This paper introduces the novel task of automated generation of lay language summaries of biomedical scientific reviews.
  arXiv  Detail & Related papers  (2020-12-23T10:01:18Z)
• Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation [48.87254340298189]
  We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-12-04T06:09:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.