Decision Support for Intoxication Prediction Using Graph Convolutional Networks

• URL: http://arxiv.org/abs/2005.00840v1
• Date: Sat, 2 May 2020 14:20:32 GMT
• Title: Decision Support for Intoxication Prediction Using Graph Convolutional Networks
• Authors: Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni, Tobias Zellner, Florian Eyer, Nassir Navab, Seyed-Ahmad Ahmadi
• Abstract summary: We propose a new machine learning based CADx method which fuses symptoms and meta information of the patients using graph convolutional networks. We validate our method against 10 medical doctors with different experience diagnosing intoxication cases for 10 different toxins from the PCC in Munich.
• Score: 34.73713173968106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame. Usually the toxin is known and recommendations can be made accordingly. However, in challenging cases only symptoms are mentioned and doctors have to rely on their clinical experience. Medical experts and our analyses of a regional dataset of intoxication records provide evidence that this is challenging, since occurring symptoms may not always match the textbook description due to regional distinctions, inter-rater variance, and institutional workflow. Computer-aided diagnosis (CADx) can provide decision support, but approaches so far do not consider additional information of the reported cases like age or gender, despite their potential value towards a correct diagnosis. In this work, we propose a new machine learning based CADx method which fuses symptoms and meta information of the patients using graph convolutional networks. We further propose a novel symptom matching method that allows the effective incorporation of prior knowledge into the learning process and evidently stabilizes the poison prediction. We validate our method against 10 medical doctors with different experience diagnosing intoxication cases for 10 different toxins from the PCC in Munich and show our method's superiority in performance for poison prediction.

Related papers

• MAGDA: Multi-agent guideline-driven diagnostic assistance [43.15066219293877]
  In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists. In this work, we introduce a new approach for zero-shot guideline-driven decision support. We model a system of multiple LLM agents augmented with a contrastive vision-language model that collaborate to reach a patient diagnosis.
  arXiv  Detail & Related papers  (2024-09-10T09:10:30Z)
• Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis [36.45569352490318]
  We introduce Xplainer, a framework for explainable zero-shot diagnosis in the clinical setting. Xplainer adapts the classification-by-description approach of contrastive vision-language models to the multi-label medical diagnosis task. Our results suggest that Xplainer provides a more detailed understanding of the decision-making process.
  arXiv  Detail & Related papers  (2023-03-23T16:07:31Z)
• NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization [59.15047491202254]
  symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. We propose a new approach based on the supervised learning of neural models with logic regularization. Our experiments show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.
  arXiv  Detail & Related papers  (2022-06-02T07:57:17Z)
• DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations [26.337392652262103]
  A diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. We propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis. Our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.
  arXiv  Detail & Related papers  (2022-05-08T01:52:42Z)
• Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification [0.6415701940560564]
  We first propose a novel method for medical automatic diagnosis with symptom inquiring and disease diagnosing formulated as a reinforcement learning task and a classification task, respectively. We create a new dataset extracted from the MedlinePlus knowledge base that contains more diseases and more complete symptom information. Experimental evaluation results show that our method outperforms three recent state-of-the-art methods on different datasets.
  arXiv  Detail & Related papers  (2021-12-01T11:25:42Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• 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.