Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems

Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems

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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

Early Triage Prediction for Outpatient Care Based on Heterogeneous Medical Data Utilizing Machine Learning.

Early Triage Prediction for Outpatient Care Based on Heterogeneous Medical Data Utilizing Machine Learning.

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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

Predicted multi-chronic disease by supervised machine learning algorithms: Performance and evaluation

Predicted multi-chronic disease by supervised machine learning algorithms: Performance and evaluation

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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 20242025 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 Cited by 25 Related articles All 5 versions

e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model

by Publication

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

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

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

by Publication

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

GITEX Dubai 2025

GITEX Dubai 2025

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Tarqea proudly participated in GITEX Dubai 2025, one of the world’s largest and most influential technology exhibitions, bringing together global leaders in AI, digital transformation, and emerging technologies.

This participation marked a strategic milestone in Tarqea’s regional expansion journey.

During GITEX, Tarqea focused on:

  • Presenting its AI-first education ecosystem vision
  • Showcasing Tarqea Net as an AI-powered skills and employment platform
  • Exploring partnerships with international technology companies
  • Studying emerging trends in AI, smart cities, and digital transformation
  • Building relationships with investors and startup ecosystems

Strategic Impact

Participation in GITEX demonstrates:

  • Clear regional ambition
  • Commitment to global standards
  • Vision to transfer international innovation into the Iraqi market

Tarqea positions itself not only as an Iraqi company — but as a regional AI-driven ecosystem builder.

Tarqea at ITEX Iraq 2023

ITEX Iraq 2023

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Tarqea at ITEX Iraq 2023
Tarqea participated in ITEX Iraq 2023 as part of our commitment to supporting digital transformation and innovation in Iraq.
During the exhibition, we:

  • Showcased our AI and education solutions
  • Presented skills and career ecosystem concepts
  • Discussed the future of AI in education
  • Connected with technology companies and institutions

The event allowed us to:

  • Build strategic partnerships
  • Explore collaboration opportunities
  • Present Tarqea’s vision for intelligent education transformation

Our presence reinforced our leadership role in Iraq’s growing tech ecosystem.

Web Summit Qatar 2024

Web Summit Qatar 2024

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Tarqea proudly participated in Web Summit Qatar 2024, one of the largest global gatherings for technology, startups, and innovation.
Our participation focused on:
• Presenting Tarqea as an AI-first education ecosystem from Iraq
• Networking with international technology leaders
• Exploring regional expansion opportunities
• Studying emerging AI and digital transformation trends
This event strengthened Tarqea’s position as a forward-looking Iraqi company with regional ambitions.

Key Features of TeleMedQuick:
✅ Preliminary Risk Assessment based on medical protocols (Manchester Triage System).
✅ Medical Sign Analysis to detect potential diseases.
✅ Customized Medical Tests based on the patient’s condition.
✅ Hospital Integration, allowing medical facilities to access patient data, prepare necessary supplies, and dispatch ambulances, reducing wait times.
✅ Medical Report Archiving with data updates for continuous doctor monitoring. With AI-driven efficiency, TeleMedQuick transforms remote healthcare and improves elderly patient care.
contact us on: omar@tarqea.com +964-7816092428

التعليم المدمج

Blended learning is the ideal solution for training, especially for companies

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To prove this assumption, we must first understand: what is blended learning, and how can training programs be designed according to this model?

Blended learning, also known as hybrid learning, is an educational approach that combines online learning materials and opportunities for online interaction with traditional classroom methods that involve in-person attendance.

How do you design a blended learning program?

Define objectives

As with any type of training, you should start by defining your goals. This can be done by answering the following questions: What is the purpose of the training or teaching? What is the final objective? This not only provides clarity for learners (students or trainees), but also enables you to create a more focused program that equips them with the skills and knowledge they need.

Make it interactive

Blended learning courses can be as interactive as you want. It is up to you to decide how much learning will take place online and how much will be self-directed. For example, you can assign a task for learners to complete before a live online session. During the webinar, you can then discuss the task and share solutions.

Assessments

You will need to monitor and evaluate learners’ progress at the end of the program. This will give you insight into how successful the training has been and whether there are any knowledge gaps that need to be addressed. Using e-learning platforms, you can easily assign quizzes or assessments to be completed at the end of the training.

Why is blended learning ideal for corporate training?

Increased return on investment (ROI)

Blended learning reduces the costs associated with face-to-face training, such as travel, accommodation, and printed materials. It also offers the added advantage of scaling training programs, making it easier to train globally.

Flexibility at work

In a corporate environment, it is important to balance training with regular workloads. Blended learning models combine training and online assignments, allowing learners to study at their convenience. This gives them more control over their learning and development.

Enhanced engagement

Companies can use various online learning methods, such as webinars, to record training sessions for later use. If you have access to a Learning Management System (LMS), you can incorporate gamification, certifications, and rewards, adding a sense of friendly competition and improving learner engagement.

شركة ترقية لتكنلوجيا المعلومات

Why should a university professor prepare a course implementation plan? And how should course lectures be organized?

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Preparing course plans is considered a key factor for educational success, as curriculum design provides both the instructor and the student with a “roadmap” that guides them throughout the teaching and learning journey. This roadmap not only highlights the key milestones of the journey but also connects them as part of a structured educational development process. Its strength lies in organizing all planned activities according to a clear timeline.

The course plan clarifies that the journey begins with announcing the course objectives and its intended learning outcomes. It then progresses through a sequence of lectures, where each session is treated as a محطة (milestone), detailing the topics covered, teaching methods used, and whether there will be an assessment (exam) or not.

At the end of the journey, both the instructor and the students reflect on what has been accomplished. This initiates the evaluation process, identifying which learning outcomes have been achieved. This reflection allows for constructive feedback and recommendations, which can later be incorporated into future curriculum revisions—this is what is known as improvement planning.

The core component of any curriculum is the clear definition of its learning objectives or outcomes. Designing courses according to international educational standards provides both professors and students with the following advantages:

During the course:

  • Defining the course objectives and learning outcomes (What will students learn?)
  • Helping students understand the importance of the course and its future relevance
  • Creating a structured “roadmap” where each lecture represents a محطة with clearly defined topics and teaching methods
  • Allowing students to know the academic content of each lecture in advance
  • Enabling the instructor to track progress and completion rates
  • Selecting appropriate teaching strategies and suitable instructional methods
  • Developing and scheduling assessment plans appropriately
  • Organizing lectures and exams within a clear timeline
  • Focusing students’ efforts on meaningful tasks
  • Keeping department heads, deans, and academic supervisors fully informed about the teaching process and schedule
  • Enhancing the quality of higher education in line with global standards

After the course:

  • Identifying time periods where challenges occurred (to avoid them in the future)
  • Measuring the effectiveness of knowledge delivery (whether learning objectives were achieved)
  • Documenting the process by maintaining a comprehensive course record

At Tarqia Company, we provide high-quality training for lecturers, teachers, and academic staff to guide them in designing course plans that meet international standards.

Contact us to learn more.