Neural Summarization of Electronic Health Records

• URL: http://arxiv.org/abs/2305.15222v1
• Date: Wed, 24 May 2023 15:05:53 GMT
• Title: Neural Summarization of Electronic Health Records
• Authors: Koyena Pal, Seyed Ali Bahrainian, Laura Mercurio, Carsten Eickhoff
• Abstract summary: We studied the viability of the automatic generation of various sections of a discharge summary using four state-of-the-art neural network summarization models. Fine-tuning language models that were previously instruction fine-tuned showed better performance in summarizing entire reports.
• Score: 8.784162652042957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hospital discharge documentation is among the most essential, yet time-consuming documents written by medical practitioners. The objective of this study was to automatically generate hospital discharge summaries using neural network summarization models. We studied various data preparation and neural network training techniques that generate discharge summaries. Using nursing notes and discharge summaries from the MIMIC-III dataset, we studied the viability of the automatic generation of various sections of a discharge summary using four state-of-the-art neural network summarization models (BART, T5, Longformer and FLAN-T5). Our experiments indicated that training environments including nursing notes as the source, and discrete sections of the discharge summary as the target output (e.g. "History of Present Illness") improve language model efficiency and text quality. According to our findings, the fine-tuned BART model improved its ROUGE F1 score by 43.6% against its standard off-the-shelf version. We also found that fine-tuning the baseline BART model with other setups caused different degrees of improvement (up to 80% relative improvement). We also observed that a fine-tuned T5 generally achieves higher ROUGE F1 scores than other fine-tuned models and a fine-tuned FLAN-T5 achieves the highest ROUGE score overall, i.e., 45.6. For majority of the fine-tuned language models, summarizing discharge summary report sections separately outperformed the summarization the entire report quantitatively. On the other hand, fine-tuning language models that were previously instruction fine-tuned showed better performance in summarizing entire reports. This study concludes that a focused dataset designed for the automatic generation of discharge summaries by a language model can produce coherent Discharge Summary sections.

Related papers

• Enhanced Electronic Health Records Text Summarization Using Large Language Models [0.0]
  This project builds on prior work by creating a system that generates clinician-preferred, focused summaries. The proposed system leverages the Flan-T5 model to generate tailored EHR summaries based on clinician-specified topics.
  arXiv  Detail & Related papers  (2024-10-12T19:36:41Z)
• Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports [2.932283627137903]
  The study utilized two datasets: 7,294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2,154 pathology reports for isocitrate dehydrogenase (IDH) mutation status.
  arXiv  Detail & Related papers  (2024-09-15T15:21:45Z)
• Fine-tuning Large Language Models for Automated Diagnostic Screening Summaries [0.024105148723769353]
  We evaluate several state-of-the-art Large Language Models (LLMs) for generating concise summaries from mental state examinations. We rigorously evaluate four different models for summary generation using established ROUGE metrics and input from human evaluators. Our top-performing fine-tuned model outperforms existing models, achieving ROUGE-1 and ROUGE-L values of 0.810 and 0.764, respectively.
  arXiv  Detail & Related papers  (2024-03-29T12:25:37Z)
• Information-Theoretic Distillation for Reference-less Summarization [67.51150817011617]
  We present a novel framework to distill a powerful summarizer based on the information-theoretic objective for summarization. We start off from Pythia-2.8B as the teacher model, which is not yet capable of summarization. We arrive at a compact but powerful summarizer with only 568M parameters that performs competitively against ChatGPT.
  arXiv  Detail & Related papers  (2024-03-20T17:42:08Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• IUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for multilayer summarization of clinical conversations? [0.0]
  We analyze summarization model ensembling approaches to improve the overall accuracy of the generated medical report called chart note. Our results indicate that although an ensemble of models specialized in each section produces better results, the multi-layer/stage approach does not improve accuracy.
  arXiv  Detail & Related papers  (2023-06-07T10:47:33Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• Text Embeddings by Weakly-Supervised Contrastive Pre-training [98.31785569325402]
  E5 is a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts.
  arXiv  Detail & Related papers  (2022-12-07T09:25:54Z)
• Textual Data Augmentation for Patient Outcomes Prediction [67.72545656557858]
  We propose a novel data augmentation method to generate artificial clinical notes in patients' Electronic Health Records. We fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data. We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate.
  arXiv  Detail & Related papers  (2022-11-13T01:07:23Z)
• Enhancing Clinical Information Extraction with Transferred Contextual Embeddings [9.143551270841858]
  Bidirectional Representations from Transformers (BERT) model has achieved the state-of-the-art performance for many natural language processing (NLP) tasks. We show that BERT based pre-training models can be transferred to health-related documents under mild conditions.
  arXiv  Detail & Related papers  (2021-09-15T12:22:57Z)
• CHEER: Rich Model Helps Poor Model via Knowledge Infusion [69.23072792708263]
  We develop a knowledge infusion framework named CHEER that can succinctly summarize such rich model into transferable representations. Our empirical results showed that CHEER outperformed baselines by 5.60% to 46.80% in terms of the macro-F1 score on multiple physiological datasets.
  arXiv  Detail & Related papers  (2020-05-21T21:44:21Z)

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.