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Research Article

BEST PAPER 2021

Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics


Submission Deadline: 30 May 2022

Abstract

This article has no abstract.

Keywords

The topics to be discussed in this special issue but are not limited to:

(1) AI and Machine Learning for Precision Medicine and Preventive Healthcare
(2) AI and Machine Learning for Identifying diseases and diagnosis
(3) AI and Machine Learning for Drug discovery and manufacturing
(4) AI and Machine Learning for Medical Imaging diagnosis
(5) AI and Machine Learning for Personalized medicine
(6) AI and Machine Learning based behavioural modification
(7) AI and Machine Learning for Smart health records
(8) AI and Machine Learning for Clinical Trial and research
(9) AI and Machine Learning based Crowd sourced data collection
(10) AI and Machine Learning in Better Radiotherapy and Radiology
(11) AI and Machine Learning in Outbreak Prediction
(12) AI and Machine Learning based Robotic Surgery
(13) AI, Machine learning and deep learning for Biomedical and Health Informatics
(14) AI and Machine Learning in Rule Based Expert Systems- Used in EHR (Electronic Health record)
(15) AI and ML Applications in Pharmacy
(16) AI and ML applications in Clinical Trial Research
(17) Physical Robots
(18) Natural Language Processing
(19) Robotic Process Automation
(20) AI and Machine Learning based Diagnosis and treatment Applications
(21) AI and Machine Learning for Precision Medicine and Preventive Healthcare
(22) Medicine 5.0: AI and Machine Learning algorithm in healthcare
(23) AI and Machine Learning for Predictive cardiovascular disease using electronic health record
(24) AI and Machine Learning based Electronic Medical Data
(25) AI and Machine Learning in Image Processing, Computer Vision and Pattern recognition
(26) AI and Machine Learning- Neural Network and Deep Learning
(27) AI and ML for Treatment and Prediction of disease: devices for disease (wearable bionic)
(28) Internet of Things (IoT) based applications
(29) Computational Intelligence and Soft Computing based applications
(30) Artificial Intelligence and Machine Learning based Biological Models
(31) AI and Machine Learning based Numerical Computing
  • Research Article

    BEST PAPER 2021

    Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph

    Hafifah Ab Hamid1, Nazrul Anuar Nayan1,*, Mohd Zubir Suboh1, Nurin Izzati Mohamad Azizul1, Mohamad Nazhan Mohd Nizar1, Amilia Aminuddin2, Mohd Shahrir Mohamed Said3, Saharuddin Ahmad4 CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 287-304, 2022, DOI:10.32604/cmc.2022.021987
    Abstract Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

    Wei Pei1, Yan Li1, Siuly Siuly1,*, Peng Wen2 CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830
    Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More >

    Graphic Abstract

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