Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2210.07198v1
• Date: Thu, 13 Oct 2022 17:17:17 GMT
• Title: Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
• Authors: Arsene Fansi Tchango, Rishab Goel, Julien Martel, Zhi Wen, Gaetan Marceau Caron, Joumana Ghosn
• Abstract summary: We propose to model the evidence acquisition and automatic diagnosis tasks using a deep reinforcement learning framework. We show that our approach performs better than existing models while maintaining competitive pathology prediction accuracy.
• Score: 2.314562406457073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The automation of the medical evidence acquisition and diagnosis process has recently attracted increasing attention in order to reduce the workload of doctors and democratize access to medical care. However, most works proposed in the machine learning literature focus solely on improving the prediction accuracy of a patient's pathology. We argue that this objective is insufficient to ensure doctors' acceptability of such systems. In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential diagnosis (in the form of a short list of plausible diseases) because the medical evidence collected from patients is often insufficient to establish a final diagnosis. Moreover, doctors explicitly explore severe pathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases. In particular, interactions between a system and a patient need to emulate the reasoning of doctors. We therefore propose to model the evidence acquisition and automatic diagnosis tasks using a deep reinforcement learning framework that considers three essential aspects of a doctor's reasoning, namely generating a differential diagnosis using an exploration-confirmation approach while prioritizing severe pathologies. We propose metrics for evaluating interaction quality based on these three aspects. We show that our approach performs better than existing models while maintaining competitive pathology prediction accuracy.

Related papers

• RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
  We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians. Experimental results demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-08-22T17:44:40Z)
• DWARF: Disease-weighted network for attention map refinement [6.658039889530641]
  This study addresses the "human out of the loop" and "trustworthiness" issues in medical image analysis by integrating medical professionals into the interpretability process. We propose a disease-weighted attention map refinement network (DWARF) that leverages expert feedback to enhance model relevance and accuracy.
  arXiv  Detail & Related papers  (2024-06-24T18:00:11Z)
• Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment [14.563188427409958]
  Our work aims to build a medical dialogue system that aligns with clinicians' diagnostic reasoning processes. We propose a novel framework, Emulation, designed to generate an appropriate response. Our framework furnishes clear explanations for the generated responses, enhancing its transparency in medical consultations.
  arXiv  Detail & Related papers  (2024-06-20T02:02:53Z)
• Towards Knowledge-Infused Automated Disease Diagnosis Assistant [14.150224660741939]
  We build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. We propose a two-channel, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication. In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification.
  arXiv  Detail & Related papers  (2024-05-18T05:18:50Z)
• Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis [14.17497921394565]
  We propose a medical dialogue generation framework with the Intuitive-then-Analytic Differential Diagnosis (IADDx) Our method starts with a differential diagnosis via retrieval-based intuitive association and subsequently refines it through a graph-enhanced analytic procedure. Experimental results on two datasets validate the efficacy of our method.
  arXiv  Detail & Related papers  (2024-01-12T12:35:19Z)
• A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis [84.6348989654916]
  We propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process. It integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path.
  arXiv  Detail & Related papers  (2023-11-26T14:42:31Z)
• DDXPlus: A new Dataset for Medical Automatic Diagnosis [2.7126836481535213]
  We present a large-scale synthetic dataset that includes a differential diagnosis, along with the ground truth pathology, for each patient. As a proof-of-concept, we extend several existing AD and ASD systems to incorporate differential diagnosis. We provide empirical evidence that using differentials in training signals is essential for such systems to learn to predict differentials.
  arXiv  Detail & Related papers  (2022-05-18T18:03:39Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

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.