Learning medical triage from clinicians using Deep Q-Learning

• URL: http://arxiv.org/abs/2003.12828v2
• Date: Wed, 24 Jun 2020 16:39:37 GMT
• Title: Learning medical triage from clinicians using Deep Q-Learning
• Authors: Albert Buchard, Baptiste Bouvier, Giulia Prando, Rory Beard, Michail Livieratos, Dan Busbridge, Daniel Thompson, Jonathan Richens, Yuanzhao Zhang, Adam Baker, Yura Perov, Kostis Gourgoulias, Saurabh Johri
• Abstract summary: We present a Deep Reinforcement Learning approach to triage patients using curated clinical vignettes. The dataset, consisting of 1374 clinical vignettes, was created by medical doctors to represent real-life cases. We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases.
• Score: 0.3111424566471944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference and are not immediately applicable in a fully automated setting. On the other hand, learning triage policies directly from experts may correct for some of the limitations of hard-coded decision-trees. In this work, we present a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage patients using curated clinical vignettes. The dataset, consisting of 1374 clinical vignettes, was created by medical doctors to represent real-life cases. Each vignette is associated with an average of 3.8 expert triage decisions given by medical doctors relying solely on medical history. We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases. The trained agent learns when to stop asking questions, acquires optimized decision policies requiring less evidence than supervised approaches, and adapts to the novelty of a situation by asking for more information. Overall, we demonstrate that a Deep Reinforcement Learning approach can learn effective medical triage policies directly from expert decisions, without requiring expert knowledge engineering. This approach is scalable and can be deployed in healthcare settings or geographical regions with distinct triage specifications, or where trained experts are scarce, to improve decision making in the early stage of care.

Related papers

• Leveraging graph neural networks for supporting Automatic Triage of Patients [5.864579168378686]
  We propose an AI based module to manage patients emergency code assignments in emergency departments. Data containing relevant patient information, such as vital signs, symptoms, and medical history, are used to accurately classify patients into triage categories.
  arXiv  Detail & Related papers  (2024-03-11T09:54:35Z)
• Defining Expertise: Applications to Treatment Effect Estimation [58.7977683502207]
  We argue that expertise - particularly the type of expertise the decision-makers of a domain are likely to have - can be informative in designing and selecting methods for treatment effect estimation. We define two types of expertise, predictive and prognostic, and demonstrate empirically that: (i) the prominent type of expertise in a domain significantly influences the performance of different methods in treatment effect estimation, and (ii) it is possible to predict the type of expertise present in a dataset.
  arXiv  Detail & Related papers  (2024-03-01T17:30:49Z)
• Transformer-based classification of user queries for medical consultancy with respect to expert specialization [4.124390946636936]
  This research presents an innovative strategy, utilizing the RuBERT model, for categorizing user inquiries in the field of medical consultation. We fine-tuned the pre-trained RuBERT model on a varied dataset, which facilitates precise correspondence between queries and particular medical specialisms.
  arXiv  Detail & Related papers  (2023-09-26T04:36:12Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• POETREE: Interpretable Policy Learning with Adaptive Decision Trees [78.6363825307044]
  POETREE is a novel framework for interpretable policy learning. It builds probabilistic tree policies determining physician actions based on patients' observations and medical history. It outperforms the state-of-the-art on real and synthetic medical datasets.
  arXiv  Detail & Related papers  (2022-03-15T16:50:52Z)
• Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View [36.025217954247125]
  We introduce the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners. We discuss how these methods have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies.
  arXiv  Detail & Related papers  (2021-12-05T17:12:53Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• 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)
• The Ethical Implications of Shared Medical Decision Making without Providing Adequate Computational Support to the Care Provider and to the Patient [1.52292571922932]
  There is a clear need to involve patients in medical decisions. Recent progress in Artificial Intelligence suggests adding a third agent: a computer. Ethical physicians should exploit computational decision support technologies.
  arXiv  Detail & Related papers  (2021-02-03T00:30:21Z)
• Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence [0.0]
  We construct a probabilistic logic network to represent a portion of the knowledge base of a medical toxicologist. The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases.
  arXiv  Detail & Related papers  (2021-02-01T19:16:59Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)

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.