Diagnostic Reasoning in Natural Language: Computational Model and Application

• URL: http://arxiv.org/abs/2409.05367v1
• Date: Mon, 9 Sep 2024 06:55:37 GMT
• Title: Diagnostic Reasoning in Natural Language: Computational Model and Application
• Authors: Nils Dycke, Matej Zečević, Ilia Kuznetsov, Beatrix Suess, Kristian Kersting, Iryna Gurevych,
• Abstract summary: We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
• Score: 68.47402386668846
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diagnostic reasoning is a key component of expert work in many domains. It is a hard, time-consuming activity that requires expertise, and AI research has investigated the ways automated systems can support this process. Yet, due to the complexity of natural language, the applications of AI for diagnostic reasoning to language-related tasks are lacking. To close this gap, we investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR). We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models and instantiate it in a comprehensive study of scientific paper assessment in the biomedical domain. We use the resulting dataset to investigate the human decision-making process in NL-DAR and determine the potential of LLMs to support structured decision-making over text. Our framework, open resources and tools lay the groundwork for the empirical study of collaborative diagnostic reasoning in the age of LLMs, in the scholarly domain and beyond.

Related papers

• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering [14.198330378235632]
  We use Multiple Choice and Abstractive Question Answering to conduct a large-scale empirical study on 22 datasets in three generalist and three specialist biomedical sub-domains. Our multifaceted analysis of the performance of 15 LLMs uncovers success factors such as instruction tuning that lead to improved recall and comprehension. We show that while recently proposed domain-adapted models may lack adequate knowledge, directly fine-tuning on our collected medical knowledge datasets shows encouraging results. We complement the quantitative results with a skill-oriented manual error analysis, which reveals a significant gap between the models' capabilities to simply recall necessary knowledge and to integrate it with the presented
  arXiv  Detail & Related papers  (2024-06-06T02:43:21Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Conversational Disease Diagnosis via External Planner-Controlled Large Language Models [18.93345199841588]
  This study presents a LLM-based diagnostic system that enhances planning capabilities by emulating doctors. By utilizing real patient electronic medical record data, we constructed simulated dialogues between virtual patients and doctors.
  arXiv  Detail & Related papers  (2024-04-04T06:16:35Z)
• The Radiation Oncology NLP Database [33.391114383354804]
  We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology. ROND is specifically designed to address this gap in the domain of radiation oncology. It encompasses various NLP tasks including Logic Reasoning, Text Classification, Named Entity Recognition (NER), Question Answering (QA), Text Summarization, and Patient-Clinician Conversations.
  arXiv  Detail & Related papers  (2024-01-19T19:23:37Z)
• Injecting linguistic knowledge into BERT for Dialogue State Tracking [60.42231674887294]
  This paper proposes a method that extracts linguistic knowledge via an unsupervised framework. We then utilize this knowledge to augment BERT's performance and interpretability in Dialogue State Tracking (DST) tasks. We benchmark this framework on various DST tasks and observe a notable improvement in accuracy.
  arXiv  Detail & Related papers  (2023-11-27T08:38:42Z)
• Unveiling A Core Linguistic Region in Large Language Models [49.860260050718516]
  This paper conducts an analogical research using brain localization as a prototype. We have discovered a core region in large language models that corresponds to linguistic competence. We observe that an improvement in linguistic competence does not necessarily accompany an elevation in the model's knowledge level.
  arXiv  Detail & Related papers  (2023-10-23T13:31:32Z)
• Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach [50.125704610228254]
  Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. We conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom taxonomy.
  arXiv  Detail & Related papers  (2023-10-12T09:55:45Z)
• ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT [15.973406739758856]
  This study presents an innovative approach to the application of large language models (LLMs) in clinical decision-making, focusing on OpenAI's ChatGPT. Our approach introduces the use of contextual prompts-strategically designed to include task description, feature description, and crucially, integration of domain knowledge-for high-quality binary classification tasks even in data-scarce scenarios.
  arXiv  Detail & Related papers  (2023-08-17T20:50:46Z)
• Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning [5.321587036724933]
  We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training. We demonstrate that a multi-task, clinically trained language model outperforms its general domain counterpart by a large margin.
  arXiv  Detail & Related papers  (2023-06-07T15:55:34Z)

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.