Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution

• URL: http://arxiv.org/abs/2402.14039v1
• Date: Wed, 21 Feb 2024 06:39:04 GMT
• Title: Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution
• Authors: Alaa Alomari, Hossam Faris, Pedro A. Castillo
• Abstract summary: The study focuses on handling multiclass and highly imbalanced datasets for Arabic medical questions. The proposed module is deployed in both synchronous and asynchronous medical consultations.
• Score: 3.992328888937568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Covid-19 pandemic has led to an increase in the awareness of and demand for telemedicine services, resulting in a need for automating the process and relying on machine learning (ML) to reduce the operational load. This research proposes a specialty detection classifier based on a machine learning model to automate the process of detecting the correct specialty for each question and routing it to the correct doctor. The study focuses on handling multiclass and highly imbalanced datasets for Arabic medical questions, comparing some oversampling techniques, developing a Deep Neural Network (DNN) model for specialty detection, and exploring the hidden business areas that rely on specialty detection such as customizing and personalizing the consultation flow for different specialties. The proposed module is deployed in both synchronous and asynchronous medical consultations to provide more real-time classification, minimize the doctor effort in addressing the correct specialty, and give the system more flexibility in customizing the medical consultation flow. The evaluation and assessment are based on accuracy, precision, recall, and F1-score. The experimental results suggest that combining multiple techniques, such as SMOTE and reweighing with keyword identification, is necessary to achieve improved performance in detecting rare classes in imbalanced multiclass datasets. By using these techniques, specialty detection models can more accurately detect rare classes in real-world scenarios where imbalanced data is common.

Related papers

• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models [17.643421997037514]
  We propose a novel framework that tackles both discriminative and generative multimodal medical tasks. The learning of Med-MoE consists of three steps: multimodal medical alignment, instruction tuning and routing, and domain-specific MoE tuning. Our model can achieve performance superior to or on par with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-04-16T02:35:17Z)
• Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification [40.366659911178964]
  Endoscopy plays a major role in identifying any underlying abnormalities within the gastrointestinal (GI) tract. Deep learning, specifically Convolution Neural Networks (CNNs) which are designed to perform automatic feature learning without any prior feature engineering, has recently reported great benefits for GI endoscopy image analysis. We investigate three KD-based learning frameworks, response-based, feature-based, and relation-based mechanisms, and introduce a novel multi-head attention-based feature fusion mechanism to support relation-based learning.
  arXiv  Detail & Related papers  (2023-08-17T02:02:11Z)
• MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes [4.648677931378919]
  Deep learning can be used to optimize diagnosis and patient monitoring for clinical-based applications. For cardiovascular disease, one such condition where the rising number of patients increasingly outweighs the availability of medical resources in different parts of the world, a core challenge is the automated classification of various cardiac abnormalities. The proposed novel multi-modal Transformer architecture would be able to accurately perform this task while demonstrating the cross-domain effectiveness of Transformers.
  arXiv  Detail & Related papers  (2023-02-21T21:38:41Z)
• 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)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification [11.160089265436689]
  Computer-aided diagnosis (CAD) is becoming a prominent approach to assist clinicians spanning multiple fields. In this study, a novel end-to-end knowledge-driven classification framework is presented. The proposed system leverages cues provided by an ensemble of experts to guide the learning phase of a densely connected convolutional network (DenseNet)
  arXiv  Detail & Related papers  (2020-05-28T16:12:04Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.