Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization

• URL: http://arxiv.org/abs/2005.00163v1
• Date: Fri, 1 May 2020 01:12:49 GMT
• Title: Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
• Authors: Sajad Sotudeh and Nazli Goharian and Ross W. Filice
• Abstract summary: Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer.
• Score: 22.062385543743293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of Rouge metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients' welfare.

Related papers

• TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images [62.53931644063323]
  In this study we extended the capabilities of TotalSegmentator to MR images. We trained an nnU-Net segmentation algorithm on this dataset and calculated similarity coefficients (Dice) to evaluate the model's performance. The model significantly outperformed two other publicly available segmentation models (Dice score 0.824 versus 0.762; p0.001 and 0.762 versus 0.542; p)
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference [2.1067045507411195]
  Large language models (LLMs) have recently exhibited impressive performance on various medical natural language processing tasks. We developed a detailed schema for annotating textual oncology information, encompassing patient characteristics, tumor characteristics, tests, treatments, and temporality. The GPT-4 model exhibited overall best performance, with an average BLEU score of 0.73, an average ROUGE score of 0.72, an exact-match F1-score of 0.51, and an average accuracy of 68% on complex tasks.
  arXiv  Detail & Related papers  (2023-08-07T18:03:10Z)
• A Marker-based Neural Network System for Extracting Social Determinants of Health [12.6970199179668]
  Social determinants of health (SDoH) on patients' healthcare quality and the disparity is well-known. Many SDoH items are not coded in structured forms in electronic health records. We explore a multi-stage pipeline involving named entity recognition (NER), relation classification (RC), and text classification methods to extract SDoH information from clinical notes automatically.
  arXiv  Detail & Related papers  (2022-12-24T18:40:23Z)
• MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation [57.336056469276585]
  We present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke dataset. Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102.
  arXiv  Detail & Related papers  (2022-11-11T14:17:04Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization [3.977582258550673]
  We propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization. In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization.
  arXiv  Detail & Related papers  (2022-04-02T07:59:28Z)
• Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization [5.234281904315526]
  The IMPRESSIONS section of a radiology report is a summary of the radiologist's reasoning and conclusions. Prior research on radiology report summarization has focused on single-step end-to-end models. We propose a two-step approach: extractive summarization followed by abstractive summarization.
  arXiv  Detail & Related papers  (2022-03-15T21:18:09Z)
• Event-based clinical findings extraction from radiology reports with pre-trained language model [0.22940141855172028]
  We present a new corpus of radiology reports annotated with clinical findings. The gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT.
  arXiv  Detail & Related papers  (2021-12-27T05:03:10Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• 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)
• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [47.999621481852266]
  We propose an interpretable graph learning-based model which interprets the clinical relevance of the input features towards the task. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task.
  arXiv  Detail & Related papers  (2021-03-29T13:04:02Z)

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.