Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

Kierner, Slawomir; Kucharski, Jacek; Kierner, Zofia

doi:10.1016/j.jbi.2023.104428

Cited by 13 publications

(3 citation statements)

References 86 publications

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“…IT infrastructure helps manage, retrieve, and store data. IT infrastructure is essential for storing and retrieving graphical data, according to S. Kierner, Kucharski, and Z. Kierner (2023). This is essential for performance and quick decision-making.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Management and Service Systems in Higher Education: A Quantitative Examination of Data Imaging, Interaction Systems, and Decision Support for Informed Decision-Making and Performance Enhancement

Li,

Ren

2024

J INFORM SYSTEMS ENG

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Making informed decisions and improving organizational performance are crucial in the modern, data-driven environment. These processes are significantly shaped by a number of variables, including Data Imaging, Interaction Systems, Decision Support Systems, IT Infrastructure, and Technology Readiness. Interaction Systems enable communication and teamwork, Data Imaging translates complex data into visual insights, and Decision Support Systems offer cutting-edge analytics. The IT infrastructure serves as the foundation of technology, and technology readiness measures how ready people and universities are to adopt new technologies. This research aims to explore the interplay between these variables within the context of organizational change theory and their impact on organizational performance and decision-making. Additionally, it examines the moderating effect of Technology Readiness and the mediating role of IT Infrastructure in the organizational change process. Structural Equation Modeling (SEM) in AMOS is used to do this study quantitatively. A total of 450 professionals from various fields are surveyed using reliable questionnaires to compile this data. Within the context of organizational change theory, this study provides insights into the complex interactions between these factors and their combined impact on organizational performance and decision-making. It offers insightful information about how university management can use technology and human resources to improve decision-making procedures and overall performance results. This study adds to both practical and theoretical knowledge, providing concrete recommendations for firms trying to thrive in a technologically driven society. It also increases theoretical understanding by offering a comprehensive framework and putting light on the roles of IT Infrastructure, and Technology Readiness in the decision-making and performance improvement of universities.

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Section: Discussionmentioning

confidence: 99%

Optimizing Management and Service Systems in Higher Education: A Quantitative Examination of Data Imaging, Interaction Systems, and Decision Support for Informed Decision-Making and Performance Enhancement

Li,

Ren

2024

J INFORM SYSTEMS ENG

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“…The hybrid model does not require training; instead, it utilizes previously trained XGBoost models (one for each candidate set of XGBoost model predictors) [26].…”

Section: Hybrid Modelmentioning

confidence: 99%

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department

Dipaola,

Gatti,

Menè

et al. 2023

JPM

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Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58–83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient’s initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.

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“…AI-driven Deep Learning can expedite the creation of PSMs, aiding medical analysis [ 12 ]. Despite advancements, clinical validation of AI-generated PSMs remains crucial [ [13] , [14] , [15] ]. It is crucial to assess the clinical significance of liver segmentation using automated algorithms, including factors such as the time required to achieve clinically acceptable segmentation [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification

Khan,

Su,

Zaman

et al. 2024

Heliyon

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Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

Cited by 13 publications

References 86 publications

Optimizing Management and Service Systems in Higher Education: A Quantitative Examination of Data Imaging, Interaction Systems, and Decision Support for Informed Decision-Making and Performance Enhancement

Optimizing Management and Service Systems in Higher Education: A Quantitative Examination of Data Imaging, Interaction Systems, and Decision Support for Informed Decision-Making and Performance Enhancement

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification

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