Extrinsically-Focused Evaluation of Omissions in Medical Summarization

• URL: http://arxiv.org/abs/2311.08303v1
• Date: Tue, 14 Nov 2023 16:46:15 GMT
• Title: Extrinsically-Focused Evaluation of Omissions in Medical Summarization
• Authors: Elliot Schumacher, Daniel Rosenthal, Varun Nair, Luladay Price, Geoffrey Tso, Anitha Kannan
• Abstract summary: We propose MED-OMIT, a new omission benchmark for medical summarization. Given a doctor-patient conversation and a generated summary, MED-OMIT categorizes the chat into a set of facts and identifies which are omitted from the summary. We evaluate MED-OMIT on a publicly-released dataset of patient-doctor conversations and find that MED-OMIT captures omissions better than alternative metrics.
• Score: 10.02553223045504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of automated summarization techniques (Paice, 1990; Kupiec et al, 1995) is to condense text by focusing on the most critical information. Generative large language models (LLMs) have shown to be robust summarizers, yet traditional metrics struggle to capture resulting performance (Goyal et al, 2022) in more powerful LLMs. In safety-critical domains such as medicine, more rigorous evaluation is required, especially given the potential for LLMs to omit important information in the resulting summary. We propose MED-OMIT, a new omission benchmark for medical summarization. Given a doctor-patient conversation and a generated summary, MED-OMIT categorizes the chat into a set of facts and identifies which are omitted from the summary. We further propose to determine fact importance by simulating the impact of each fact on a downstream clinical task: differential diagnosis (DDx) generation. MED-OMIT leverages LLM prompt-based approaches which categorize the importance of facts and cluster them as supporting or negating evidence to the diagnosis. We evaluate MED-OMIT on a publicly-released dataset of patient-doctor conversations and find that MED-OMIT captures omissions better than alternative metrics.

Related papers

• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications [2.838746648891565]
  We introduce MEDIC, a framework assessing Large Language Models (LLMs) across five critical dimensions of clinical competence. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks. Results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths.
  arXiv  Detail & Related papers  (2024-09-11T14:44:51Z)
• SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research [45.2233252981348]
  Large Language Models have shown promising results in their ability to encode general medical knowledge. We test the ability of state-of-the-art LLMs to leverage their internal knowledge and reasoning for epilepsy diagnosis.
  arXiv  Detail & Related papers  (2024-07-03T11:02:12Z)
• Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries [62.32403630651586]
  Large language models (LLMs) have shown the potential to generate accurate clinical text summaries, but still struggle with issues regarding grounding and evaluation. Here, we explore a general mitigation framework using Attribute Structuring (AS), which structures the summary evaluation process. AS consistently improves the correspondence between human annotations and automated metrics in clinical text summarization.
  arXiv  Detail & Related papers  (2024-03-01T21:59:03Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization [8.456700096020601]
  Large language models (LLMs) have shown promise in natural language processing (NLP), but their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks. A clinical reader study with ten physicians evaluates summary, completeness, correctness, and conciseness; in a majority of cases, summaries from our best adapted LLMs are either equivalent (45%) or superior (36%) compared to summaries from medical experts.
  arXiv  Detail & Related papers  (2023-09-14T05:15:01Z)
• Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges [18.56314471146199]
  Large volume of notes often associated with patients together with time constraints renders manually identifying relevant evidence practically infeasible. We propose and evaluate a zero-shot strategy for using LLMs as a mechanism to efficiently retrieve and summarize unstructured evidence in patient EHR.
  arXiv  Detail & Related papers  (2023-09-08T18:44:47Z)
• Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models [59.89454513692417]
  Tabular data is often hidden in text, particularly in medical diagnostic reports. We propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics.
  arXiv  Detail & Related papers  (2023-06-08T09:12:28Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization [2.8575516056239576]
  Long-form clinical summarization of hospital admissions has real-world significance because of its potential to help both clinicians and patients. We benchmark faithfulness metrics against fine-grained human annotations for model-generated summaries of a patient's Brief Hospital Course.
  arXiv  Detail & Related papers  (2023-03-07T14:57:06Z)

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.