Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

Adewole, Kayode S.; Mojeed, Hammed A.; Ogunmodede, James Ayodele; Gabralla, Lubna Abdelkareim; Фарук, Насир; Abdulkarim, Abubakar; Emmanuel, Ifada; Folawiyo, Yusuf Y.; Oloyede, Abdukareem A.; Olawoyin, Lukman A.; Sikiru, Ismaeel A.; Musa, Nehemiah; Gital, Abdulsalam Ya’u; Chiroma, Haruna

doi:10.3390/app122312342

Cited by 4 publications

(2 citation statements)

References 117 publications

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“…Although the rate of recovery is quite good than the previous methods and so is the reduction in the mortality rate. [26,27,28] were taken to conducted a trial using the decision support models to make the mortality rate as low as possible.…”

Section: Discussionmentioning

confidence: 99%

Decision Support System for early Diagnosis of Heart Diseases

Ahmed

2023

VFAST trans. softw. eng.

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Heart disease remains a leading cause of death worldwide, emphasizing the critical need for early diagnosis to enable effective treatment and management. Decision support systems (DSS) have the potential to significantly enhance the accuracy and efficiency of heart disease diagnosis. An advanced DSS designed for early diagnosis can provide healthcare professionals with essential information, expert advice, and treatment recommendations based on comprehensive patient data. By gathering information from various sources including medical history, risk factors, and test results, the DSS analyzes and processes the data using established diagnostic criteria and the latest medical research. The DSS then generates a diagnosis or a list of possible diagnoses, along with appropriate treatment recommendations, empowering healthcare professionals to make well-informed decisions and deliver more effective patient care. Incorporating machine learning algorithms into the DSS can further enhance its accuracy and efficiency. By training the DSS on extensive patient datasets, it can identify patterns and make predictions based on new patient data, ultimately leading to improved decision-making and better patient outcomes. In conclusion, the utilization of a DSS for heart disease diagnosis holds the potential to revolutionize the field by providing healthcare professionals with vital information, expert guidance, and treatment recommendations, thereby enhancing the accuracy, efficiency, and overall outcomes of heart disease diagnosis and management.

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Section: Discussionmentioning

confidence: 99%

Decision Support System for early Diagnosis of Heart Diseases

Ahmed

2023

VFAST trans. softw. eng.

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“…In this paper, the planning of the systematic review commences with the establishment of a procedure that provides adequate guidelines for carrying out the review work. The guidelines adopted in this paper include [14][15][16][17][18][19] (a) Definition of the research questions; (b) Outlining the search strategy (c) listing the inclusion and exclusion criteria which are essentially the study selection criteria) and (d) data extraction, analysis, and synthesis of results.…”

Section: Planning the Reviewmentioning

confidence: 99%

A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

Adewole,

Alozie,

Olagunju

et al. 2024

Discov Data

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A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond.

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