Toward an Integrated Decision Making Framework for Optimized Stroke Diagnosis with DSA and Treatment under Uncertainty

• URL: http://arxiv.org/abs/2407.16962v1
• Date: Wed, 24 Jul 2024 03:01:55 GMT
• Title: Toward an Integrated Decision Making Framework for Optimized Stroke Diagnosis with DSA and Treatment under Uncertainty
• Authors: Nur Ahmad Khatim, Ahmad Azmul Asmar Irfan, Amaliya Mata'ul Hayah, Mansur M. Arief,
• Abstract summary: Current diagnostic methods, including Digital Subtraction Angiography (DSA), face limitations due to high costs and its invasive nature. We propose a novel approach using a Partially Observable Markov Decision Process (POMDP) framework. Our model integrates advanced diagnostic tools and treatment approaches with a decision-making algorithm that accounts for the inherent uncertainties in stroke diagnosis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study addresses the challenge of stroke diagnosis and treatment under uncertainty, a critical issue given the rapid progression and severe consequences of stroke conditions such as aneurysms, arteriovenous malformations (AVM), and occlusions. Current diagnostic methods, including Digital Subtraction Angiography (DSA), face limitations due to high costs and its invasive nature. To overcome these challenges, we propose a novel approach using a Partially Observable Markov Decision Process (POMDP) framework. Our model integrates advanced diagnostic tools and treatment approaches with a decision-making algorithm that accounts for the inherent uncertainties in stroke diagnosis. Our approach combines noisy observations from CT scans, Siriraj scores, and DSA reports to inform the subsequent treatment options. We utilize the online solver DESPOT, which employs tree-search methods and particle filters, to simulate potential future scenarios and guide our strategies. The results indicate that our POMDP framework balances diagnostic and treatment objectives, striking a tradeoff between the need for precise stroke identification via invasive procedures like DSA and the constraints of limited healthcare resources that necessitate more cost-effective strategies, such as in-hospital or at-home observation, by relying only relying on simulation rollouts and not imposing any prior knowledge. Our study offers a significant contribution by presenting a systematic framework that optimally integrates diagnostic and treatment processes for stroke and accounting for various uncertainties, thereby improving care and outcomes in stroke management.

Related papers

• Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Clinical Pathology Analysis [34.199766079609795]
  Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis. Traditional pure vision models face challenges of redundant feature extraction. Existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy. We propose two innovative strategies: the mixed task-guided feature enhancement, and the prompt-guided detail feature completion.
  arXiv  Detail & Related papers  (2024-12-12T18:07:23Z)
• Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning [1.5272023683653024]
  Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. We aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis.
  arXiv  Detail & Related papers  (2024-12-03T08:45:50Z)
• 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)
• 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)
• SSASS: Semi-Supervised Approach for Stenosis Segmentation [9.767759441883008]
  The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task. We introduce a semi-supervised approach for cardiovascular stenosis segmentation. Our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics.
  arXiv  Detail & Related papers  (2023-11-17T02:01:19Z)
• Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI [0.0]
  Left ventricular (LV) volume estimation is critical for valid diagnosis and management of various cardiovascular conditions. Recent machine learning advancements, particularly U-Net-like convolutional networks, have facilitated automated segmentation for medical images. This study proposes a novel methodology for post-hoc uncertainty estimation in LV volume prediction.
  arXiv  Detail & Related papers  (2023-10-30T13:44:55Z)
• Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning [2.0191844627740254]
  We aim to learn the optimal sequence of actions to perform in order to obtain a correct diagnosis from electronic health records. We apply various deep reinforcement learning algorithms to this task and experiment on a synthetic but realistic dataset to differentially diagnose anemia.
  arXiv  Detail & Related papers  (2023-05-10T16:36:54Z)
• 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)
• 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)
• Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis [50.591267188664666]
  Strokes are the leading cause of adult disability in the United States. Brain-Computer Interfaces (BCIs) help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic. The various machine learning techniques and algorithms that are addressed and combined with BCIs technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.
  arXiv  Detail & Related papers  (2020-08-06T19:50:29Z)

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.