Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records

• URL: http://arxiv.org/abs/2310.19967v2
• Date: Fri, 3 Nov 2023 19:32:02 GMT
• Title: Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records
• Authors: Bing Wang, Weizi Li, Anthony Bradlow, Antoni T.Y. Chan, Eghosa Bazuaye
• Abstract summary: We present fusion and ensemble learning-based methods using multimodal data to assist decision-making in the early detection of IA. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals.
• Score: 3.713465395871697
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely labor-intensive and inefficient. A large amount of clinical information needs to be assessed for every referral from General Practice (GP) to the hospitals. Machine learning shows great potential in automating repetitive assessment tasks and providing decision support for the early detection of IA. However, most machine learning-based methods for IA detection rely on blood testing results. But in practice, blood testing data is not always available at the point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA. In this research, we present fusion and ensemble learning-based methods using multimodal data to assist decision-making in the early detection of IA, and a conformal prediction-based method to quantify the uncertainty of the prediction and detect any unreliable predictions. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals.

Related papers

• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Towards Reducing Diagnostic Errors with Interpretable Risk Prediction [18.474645862061426]
  We propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses. Our ultimate aim is to increase access to evidence and reduce diagnostic errors.
  arXiv  Detail & Related papers  (2024-02-15T17:05:48Z)
• A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images [71.26717896083433]
  Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging.
  arXiv  Detail & Related papers  (2023-09-05T19:45:09Z)
• 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)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)
• Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision [10.929271646369887]
  The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Traditional rule-based systems are vulnerable to the prevalent linguistic variations and ambiguities in clinical text. We propose leveraging patient-level supervision from medical registries, which are often readily available and capture key patient information.
  arXiv  Detail & Related papers  (2022-03-20T03:42:03Z)
• Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning [2.2612425542955292]
  Current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. To introduce this approach into clinical routine is associated with challenges such as a lack of experts and labour-intensive work. We are interested in AI-based methods that reliably can detect cancer given only per-patient labels.
  arXiv  Detail & Related papers  (2022-02-03T15:04:26Z)
• Quality control for more reliable integration of deep learning-based image segmentation into medical workflows [0.23609258021376836]
  We present an analysis of state-of-the-art automatic quality control (QC) approaches to estimate the certainty of their outputs. We validated the most promising approaches on a brain image segmentation task identifying white matter hyperintensities (WMH) in magnetic resonance imaging data.
  arXiv  Detail & Related papers  (2021-12-06T16:30:43Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time [37.160335232396406]
  A sub-problem in CADx is to guide the physician during the entire peri-diagnostic workflow, including the acquisition stage. We propose a novel approach which is enticingly simple: use dropout at the input layer, and integrated gradients of the trained network at test-time to attribute feature importance dynamically. Results show that our proposed approach is more cost- and feature-efficient than prior approaches and achieves a higher overall accuracy.
  arXiv  Detail & Related papers  (2020-03-31T12:00:44Z)

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.