A Machine Learning and Bayesian Belief Network Approach to Predicting Cervical Cancer Risk: Implications for Risk Management

Khaled Toffaha,1 Mecit Can Emre Simsekler,1 Andrei Sleptchenko,1 Michael A Kortt,1 Laurette L Bukasa2 1Department of Management Science & Engineering, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates; 2Abu Dhabi Health Data Services, M42, Abu Dhabi, United Arab EmiratesCorrespondence: Khaled Toffaha; Mecit Can Emre Simsekler, Department of Management Science & Engineering, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates, Email [email protected]; [email protected]: Cervical cancer remains a major global health challenge, necessitating enhanced risk stratification and early detection methodologies. This study proposes a comprehensive predictive framework for cervical cancer leveraging advanced machine learning (ML) algorithms and Bayesian Belief Networks (BBNs), illustrating the transformative role of digital technologies in healthcare and education within an increasingly digitized society.Methods: A cohort of 858 patients was analyzed, addressing data challenges, including missing values, class imbalance, and nonlinear feature interactions, that frequently compromise the reliability of predictive modeling. Methodologically, this study integrated advanced data science approaches, including multiple imputation, feature selection, and imbalance mitigation, advancing medical analytics to ensure robust model generalizability.Results: High predictive performance was observed across different cervical cancer screening tests. The combined target ML model achieved an accuracy of 95.6%, an area under the receiver-operating characteristic curve (AUROC) of 0.958, and an F1-score of 0.945. The BBN, built upon the Bayesian Additive Regression Trees (BART) model, demonstrated a positive prediction rate (sensitivity) of 91.3% and a negative prediction rate (specificity) of 86.8%.Discussion: These results validate the technical efficacy of the proposed framework and underscore its potential for integration into clinical decision-support systems. Beyond clinical applications, this research contributes to computational oncology by demonstrating the synergistic potential of combining probabilistic graphical models with ML techniques. The study highlights the critical role of interdisciplinary collaboration between clinical experts and data scientists in creating effective AI healthcare solutions. It also emphasizes the need for upskilling healthcare workers and optimizing healthcare delivery processes to fully realize the benefits of precision medicine.Keywords: risk management, cervical cancer risk prediction, future of healthcare, cancer risk factors, Bayesian belief network, machine learning, digital health, patient safet

AI-Driven Decision Support Framework for Preventing Medical Equipment Failure and Enhancing Patient Safety: A New Perspective

Sara Awni Alkhatib,1,2 Rateb Katmah,1 Doua Kosaji,1 Syed Usama Bin Afzal,3 Muhammad Hamza Tariq,1 Mecit Can Emre Simsekler,4 Samer Ellahham5 1Department of Biomedical Engineering and Biotechnology, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates; 2Center for Catalysis and Separation (CeCaS), Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates; 3Department of Electrical and Computer Engineering, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates; 4Department of Management Science & Engineering, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates; 5Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab EmiratesCorrespondence: Mecit Can Emre Simsekler, Email [email protected]: Medical equipment failures pose serious risks to patient safety and healthcare system efficiency. Although AI-based predictive maintenance (PdM) has shown promise in other industries, its application in healthcare remains fragmented and insufficiently aligned with human-centered principles. This perspective paper proposes a novel AI-driven decision support framework that integrates systems thinking and prioritizes human-centered design. By leveraging real-time sensor data and historical maintenance records, the framework proactively predicts equipment failures and reduces downtime. It incorporates insights from key stakeholders, including biomedical engineers, technicians, patients, and administrators, to ensure human-centered and ethically responsible implementation. The paper also addresses major challenges such as data integration, human factors, and organizational readiness, offering practical strategies for sustainable adoption. This work contributes to the evolving role of AI in healthcare by emphasizing empathy, stakeholder collaboration, and safety, ultimately promoting more reliable medical devices and improved patient outcomes.Keywords: artificial intelligence, equipment failure analysis, maintenance, predictive, patient safety, human factors, clinical decision support system

Evaluation of System Mapping Approaches in Identifying Patient Safety Risks

Objective: While many system mapping approaches (SMAs) have been broadly used in safety-critical industries, few have so far been employed in the healthcare field to assist in the identification of patient safety risks. In this study, we evaluated a set of system modelling approaches to assess their potential contribution to the identification of risks affecting patient safety. The aim was to gain a greater understanding of the practical application of system modelling approaches with the help of the risk categorisation framework developed in this study. Setting: We conducted this study in a newly established Adult Attention Deficit Hyperactivity Disorder (ADHD) service at Cambridge and Peterborough Foundation Trust. Study Participants: Eight key stakeholders of the chosen service, including clinicians, managers, and administrative staff, were individually asked to evaluate a set of pre-defined six SMAs according to their usefulness in identifying patient safety risks through interview-based questionnaires. Results: It was found that each SMA could be useful in the chosen healthcare service in different ways. Further, specific types of diagrams were selected by stakeholders as more useful than others in identifying different sources of risks within the given system. Conclusions: The results of the evaluation showed that the system diagram is the most useful SMA in risk identification within the given system, while limited time, resources, and experience of stakeholders with SMAs may present possible obstacles for their potential use in the healthcare field in future

Design for Patient Safety: A Systems-based Risk Identification Framework

Current risk identification practices applied to patient safety in healthcare are insufficient. The situation can be improved, however, by studying systems approaches broadly and successfully utilised in other safety-critical industries, such as aviation and chemical industries. To illustrate this, this paper first investigates current risk identification practices in the healthcare field, and then examines the potential of systems approaches. A systems-based approach, called the Risk Identification Framework (RID Framework), is then developed to enhance improvement in risk identification. Demonstrating the strengths of using multiple inputs and methods, the RID Framework helps to facilitate the proactive identification of new risks. In this study, the potential value of the RID Framework is discussed by examining its application and evaluation, as conducted in a real-world healthcare setting. Both the application and evaluation of the RID Framework indicate positive results, as well as the need for further research

Integration of multiple methods in identifying patient safety risks

There is a growing awareness that risk identification plays an important role in the investigation of actual and potential harm to patients. Although current risk identification methods in healthcare have strengths and limitations, it is an open question whether they have been implemented optimally and how well they have been integrated to provide a complete picture of risk within complex healthcare systems. To shed light on this, this paper reviews the characteristics of reactive and proactive risk identification methods along with their implication on risk identification practices. Various learning points from other safety-critical industries are identified and integration of multiple methods are discussed to provide a more comprehensive view within the scope of risk management. As a particular example, this paper reviews a prognostic method, developed by the Future Aviation Safety Team (FAST), to enhance existing risk identification in the aviation industry by identifying risks that arise due to future changes. The FAST method also demonstrates integration of risk identification methods proposing four complementary approaches for use in the aviation industry. Similarly, our study provides a conceptual framework that can be used in healthcare to integrate multiple methods to accelerate patient safety improvement through comprehensive system coverage. While this paper suggests that such integration may provide better framework for identifying patient safety risks, the low-level maturity of safety management and safety culture should be considered prior to the integration. Future research is also required to provide evidence on effectiveness of integration and relevant costs involved with such integration in healthcare

Evaluating inputs of failure modes and effects analysis in identifying patient safety risks

Purpose: There is a growing awareness on the use of systems approaches to improve patient safety and quality. While earlier studies evaluated the validity of such approaches to identify and mitigate patient safety risks, so far only little attention has been given to their inputs, such as structured brainstorming and use of system mapping approaches (SMAs), to understand their impact in the risk identification process. To address this gap, the purpose of this paper is to evaluate the inputs of a well-known systems approach, failure modes and effects analysis (FMEA), in identifying patient safety risks in a real healthcare setting. Design/methodology/approach: This study was conducted in a newly established adult attention deficit hyperactivity disorder service at Cambridge and Peterborough Foundation Trust in the UK. Three stakeholders of the chosen service together with the facilitators conducted an FMEA exercise along with a particular system diagram that was initially found as the most useful SMA by eight stakeholders of the service. Findings: In this study, it was found that the formal structure of FMEA adds value to the risk identification process through comprehensive system coverage with the help of the system diagram. However, results also indicates that the structured brainstorming refrains FMEA participants from identifying and imagining new risks since they follow the process predefined in the given system diagram. Originality/value: While this study shows the potential contribution of FMEA inputs, it also suggests that healthcare organisations should not depend solely on FMEA results when identifying patient safety risks; and therefore prioritising their safety concerns

Trust-level risk identification guidance in the NHS East of England.

BACKGROUND: In healthcare, a range of methods are used to improve patient safety through risk identification within the scope of risk management. However, there is no evidence determining what trust-level guidance exists to support risk identification in healthcare organisations. This study therefore aimed to determine such methods through the content analysis of trust-level risk management documents. METHOD: Through Freedom of Information Act, risk management documents were requested from each acute, mental health and ambulance trust in the East of England region of NHS for content analysis. Received documents were also compared with guidance from other safety-critical industries to capture differences between the documents from those industries, and learning points to the healthcare field. RESULTS: A total of forty-eight documents were received from twenty-one trusts. Incident reporting was found as the main method for risk identification. The documents provided insufficient support for the use of prospective risk identification methods, such as Prospective Hazard Analysis (PHA) methods, while the guidance from other industries extensively promoted such methods. CONCLUSION: The documents provided significant insight into prescribed risk identification practice in the chosen region. Based on the content analysis and guidance from other safety-critical industries, a number of recommendations were made; such as introducing the use of PHA methods in the creation and revision of risk management documents, and providing individual guidance on risk identification to promote patient safety further