Posts in category: Publication

Authors:
Abdulrahman Ahmed Jasim, Oguz Ata, Omar Hussein Salman

Publication date: 2024/12/1

Journal: International Journal of Medical Informatics

Volume: 192

Pages: 105608

Publisher: Elsevier

Description:
Background and Objective
The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT).

Methods:
The study’s methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed …

Total citations: Cited by 8

Scholar articles:
Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems
AA Jasim, O Ata, OH Salman – International Journal of Medical Informatics, 2024

Authors:
Omar Sadeq Salman, Abdul Latiff, Nurul Mu’azzah, Sharifah Hafizah Syed Arifin, Omar Hussein Salman

Publication date: 2024/8/1

Journal: Pertanika Journal of Science & Technology

Volume: 32

Issue: 5

Description:
Traditional triage tools hospitals use face limitations in handling the increasing number of patients and analyzing complex data. These ongoing challenges in patient triage necessitate the development of more effective prediction methods. This study aims to use machine learning (ML) to create an automated triage model for remote patients in telemedicine systems, providing more accurate health services and health assessments of urgent cases in real time. A comparative study was conducted to ascertain how well different supervised machine learning models, like SVM, RF, DT, LR, NB, and KNN, evaluated patient triage outcomes for outpatient care. Hence, data from diverse, rapidly generated sources is crucial for informed patient triage decisions. Collected through IoMT-enabled sensors, it includes sensory data (ECG, blood pressure, SpO2, temperature) and non-sensory text frame measurements. The study examined six supervised machine learning algorithms. These models were trained using patient medical data and validated by assessing their performance. Supervised ML technology was implemented in Hadoop and Spark environments to identify individuals with chronic illnesses accurately. A dataset of 55,680 patient records was used to evaluate methods and determine the best match for disease prediction. The simulation results highlight the powerful integration of ML in telemedicine to analyze data from heterogeneous IoMT devices, indicating that the Decision Tree (DT) algorithm outperformed the other five machine learning algorithms by 93.50% in terms of performance and accuracy metrics. This result provides practical insights …

Total citations: Cited by 4

Scholar articles:
Early Triage Prediction for Outpatient Care Based on Heterogeneous Medical Data Utilizing Machine Learning.
OS Salman, A Latiff, N Mu’azzah, SH Syed Arifin… – Pertanika Journal of Science & Technology, 2024

Authors:
Omar Sadeq Salman, Nurul Mu’azzah Abdul Latiff, Sharifah Hafizah Syed Arifin, Omar H Salman

Publication date: 2024/4/27

Journal: ELEKTRIKA-Journal of Electrical Engineering

Volume: 23

Issue: 1

Pages: 55-64

Description:
Current environmental conditions and human lifestyles have resulted in the emergence of numerous diseases. The medical field generates an enormous amount of new data each year for remote monitoring of patients. Due to increased data growth in the medical and healthcare industries, accurate medical data analysis has been advantageous to early patient care. However, physicians often face challenges in accurately diagnosing diseases in patients far from hospitals. Therefore, utilizing remote patient systems (telemedicine systems) due to the complexities associated with their chronic conditions. On the other hand, predicting illness is also a challenging task. Thus, data extracted from heterogeneous, fast-flowing, and reliable sources is crucial for decision-making and disease prediction. This research paper aims to utilize supervised Machine Learning (ML) techniques to predict chronic diseases such as heart and hypertension based on the patient’s features or symptoms by analyzing patient data collected by sensors and sources enabled by the Internet of Medical Things (IoMT). Supervised ML technology in Hadoop and Spark environments is employed to guarantee that this classification accurately identifies individuals with chronic illnesses. The methods are evaluated using 55,680 patient records to discover the proper match between the data set and the final disease-predicted result. The results demonstrate that the proposed procedure employing the Decision Tree (DT) algorithm is 94% accurate, and DT outperforms the other four ML algorithms. This includes the Support Vector Machine (SVM), a Naive Bayes (NB) model, Random …

Total citations: Cited by 5

Scholar articles:
Predicted multi-chronic disease by supervised machine learning algorithms: Performance and evaluation
OS Salman, NMA Latiff, SHS Arifin, OH Salman – ELEKTRIKA-Journal of Electrical Engineering, 2024

Authors:
Abdulrahman Ahmed Jasim, Layth Rafea Hazim, Hayder Mohammedqasim, Roa’a Mohammedqasem, Oguz Ata, Omar Hussein Salman

Publication date: 2024/7

Journal: The Journal of Supercomputing

Volume: 80

Issue: 11

Pages: 15664-15689

Publisher: Springer US

Description:
One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the …

Total citations: Cited by 25

Scholar articles:
e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model
AA Jasim, LR Hazim, H Mohammedqasim… – The Journal of Supercomputing, 2024

Authors:
Omar Sadeq Salman, Nurul Mu’azzah Abdul Latiff, Omar H Salman, Sharifah Hafizah Syed Ariffin

Publication date: 2024/6

Journal: Neural Computing and Applications

Volume: 36

Issue: 17

Pages: 10109-10122

Publisher: Springer London

Description:
In the Internet of Medical Things (IoMT)-based real-time telemedicine systems, patients can utilize a wide range of medical devices and sensors, which leads to the continuous generation of massive amounts of data. The high speed of data generation poses challenges in collecting, organizing, processing, and making decisions about patients’ emergency levels. Existing methods for classifying (triaging) patients in such environments often yield inaccurate triage levels, necessitating a computational approach to enhance accuracy. This research aims to handle data from multiple heterogeneous sources in IoMT-based real-time telemedicine systems, analyze the data to accurately triage patients with the most urgent cases, and provide swift healthcare services. The proposed solution, the Data Processing with Triaging Model (DPTM), employs a hybrid approach that combines principal component analysis and …

Total citations: Cited by 7

Scholar articles:
A hybrid computational approach to process real-time streaming multi-sources data and improve classification for emergency patients triage services: moving forward to an efficient IoMT-based real-time telemedicine systems
OS Salman, NM Abdul Latiff, OH Salman… – Neural Computing and Applications, 2024