Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

• URL: http://arxiv.org/abs/2206.07365v1
• Date: Wed, 15 Jun 2022 08:19:16 GMT
• Title: Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases
• Authors: Eman Yahia Alqaissi, Fahd Saleh Alotaibi, and Muhammad Sher Ramzan
• Abstract summary: This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal.

Related papers

• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• Towards System Modelling to Support Diseases Data Extraction from the Electronic Health Records for Physicians Research Activities [0.0]
  This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. It is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities.
  arXiv  Detail & Related papers  (2024-04-01T16:18:40Z)
• OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine [55.29668193415034]
  We present OpenMEDLab, an open-source platform for multi-modality foundation models. It encapsulates solutions of pioneering attempts in prompting and fine-tuning large language and vision models for frontline clinical and bioinformatic applications. It opens access to a group of pre-trained foundation models for various medical image modalities, clinical text, protein engineering, etc.
  arXiv  Detail & Related papers  (2024-02-28T03:51:02Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights [0.0]
  The recent development of Monkeypox disease poses a global pandemic threat when the world is still fighting Coronavirus Disease 2019 (COVID-19). We have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%.
  arXiv  Detail & Related papers  (2022-11-01T18:07:34Z)
• Forecasting new diseases in low-data settings using transfer learning [0.0]
  We show how knowledge of related diseases can help make predictions of new diseases using transfer learning. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease.
  arXiv  Detail & Related papers  (2022-04-07T17:54:39Z)
• Machine learning based disease diagnosis: A comprehensive review [0.0]
  This review explains how Machine Learning (ML) and Deep Learning (DL) are being used to help in the early identification of numerous diseases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in Machine Learning-based Disease Diagnosis (MLBDD)
  arXiv  Detail & Related papers  (2021-12-31T16:25:23Z)
• 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)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)
• A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records [0.5672132510411464]
  We develop and publish a Python package to transform public health dataset into easy to access universal format. We propose two novel model architectures to predict multiple diagnoses simultaneously. Both models can predict multiple diagnoses simultaneously with high accuracy.
  arXiv  Detail & Related papers  (2020-06-23T14:58:58Z)

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.