Decision Support System Research Articles

Application Of The Weighted Product (WP) Method In The Selection Of Prospective New Employees Using Assessment Indicators At PT. Delta Sukses Sejahtera

In running its business, the success of a company is largely determined by the quality of its employees. To obtain quality employees who meet the required qualifications, companies must carry out a selection process in recruiting qualified new employee candidates. PT. Delta Sukses Sejahtera is a private company that operates in the field of recruitment services. This recruitment service serves companies by taking over the job of recruiting candidates. Each assessment is taken into account and considered according to the company's needs. So far, the selection process for prospective employees has experienced difficulties because they are still comparing the test results of prospective employees one by one to determine potential new employees. This process takes a long time. Apart from that, the old employee selection system created an element of subjective assessment. So it is necessary to build a decision support system using the Weighted Product (WP) method as an alternative solution, so that it runs effectively and reduces the occurrence of subjective assessments. Weighted Product (WP) is a method in a decision making system that connects attribute ratings using a multiplication technique, where the rating for each attribute needs to be raised to the power of the relevant attribute weight before calculation, this process is similar to the normalization stage. Based on the results of this research, it shows that from the five orders of candidates for recruiting new employees, it can be seen that the scores obtained by each prospective new employee are not too far apart from first to fifth. In the first and fifth places, they have similarities in the final psychological and educational criteria. Where the first and fifth psychological test criteria have almost perfect scores.

Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study.

One of the significant changes in intensive care medicine over the past 2 decades is the acknowledgment that improper mechanical ventilation settings substantially contribute to pulmonary injury in critically ill patients. Artificial intelligence (AI) solutions can optimize mechanical ventilation settings in intensive care units (ICUs) and improve patient outcomes. Specifically, machine learning algorithms can be trained on large datasets of patient information and mechanical ventilation settings. These algorithms can then predict patient responses to different ventilation strategies and suggest personalized ventilation settings for individual patients. In this study, we aimed to design and evaluate an AI solution that could tailor an optimal ventilator strategy for each critically ill patient who requires mechanical ventilation. We proposed a reinforcement learning-based AI solution using observational data from multiple ICUs in the United States. The primary outcome was hospital mortality. Secondary outcomes were the proportion of optimal oxygen saturation and the proportion of optimal mean arterial blood pressure. We trained our AI agent to recommend low, medium, and high levels of 3 ventilator settings-positive end-expiratory pressure, fraction of inspired oxygen, and ideal body weight-adjusted tidal volume-according to patients' health conditions. We defined a policy as rules guiding ventilator setting changes given specific clinical scenarios. Off-policy evaluation metrics were applied to evaluate the AI policy. We studied 21,595 and 5105 patients' ICU stays from the e-Intensive Care Unit Collaborative Research (eICU) and Medical Information Mart for Intensive Care IV (MIMIC-IV) databases, respectively. Using the learned AI policy, we estimated the hospital mortality rate (eICU 12.1%, SD 3.1%; MIMIC-IV 29.1%, SD 0.9%), the proportion of optimal oxygen saturation (eICU 58.7%, SD 4.7%; MIMIC-IV 49%, SD 1%), and the proportion of optimal mean arterial blood pressure (eICU 31.1%, SD 4.5%; MIMIC-IV 41.2%, SD 1%). Based on multiple quantitative and qualitative evaluation metrics, our proposed AI solution outperformed observed clinical practice. Our study found that customizing ventilation settings for individual patients led to lower estimated hospital mortality rates compared to actual rates. This highlights the potential effectiveness of using reinforcement learning methodology to develop AI models that analyze complex clinical data for optimizing treatment parameters. Additionally, our findings suggest the integration of this model into a clinical decision support system for refining ventilation settings, supporting the need for prospective validation trials.

Spatial Analysis of Middle-Mile Transport for Advanced Air Mobility: A Case Study of Rural North Dakota

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies.

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data

PurposeSpontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortality rate of 40 ​%, accurate diagnosis and prompt initiation of appropriate antibiotic therapy are crucial for optimizing patient outcomes and preventing life-threatening complications. This study aimed to expand the use of computational models to improve the diagnostic accuracy of SBP in cirrhotic patients by incorporating a broader range of data, including clinical variables and laboratory values. Patients and methodsWe employed 5 machine learning classification methods - Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Random Forest, utilizing a variety of demographic, clinical, and laboratory features and biomarkers. ResultsAscitic fluid markers, including white blood cell (WBC) count, lactate dehydrogenase (LDH), total protein, and polymorphonuclear cells (PMN), significantly differentiated between SBP and non-SBP patients. The Random Forest model demonstrated the highest overall accuracy at 86 ​%, while the Naive Bayes model achieved the highest sensitivity at 72 ​%. Utilizing 10 key features instead of the full feature set improved model performance, notably enhancing specificity and accuracy. ConclusionOur analysis highlights the potential of machine learning to enhance the accuracy of SBP diagnosis in cirrhotic patients. Integrating these models into clinical workflows could substantially improve patient outcomes. To achieve this, ongoing multidisciplinary research is crucial. Ensuring model interpretability, continuous monitoring, and rigorous validation will be essential for the successful implementation of real-time clinical decision support systems.

Decision making techniques in mass gathering medicine during the COVID-19 pandemia: a scoping review.

The COVID-19 pandemic has profoundly affected mass gatherings (MGs) worldwide, necessitating the implementation of advanced decision support techniques. These techniques, including mathematical models and risk assessment tools, have played a critical role in ensuring the safe conduct of events by mitigating the spread of SARS-CoV-2. This mini-review aims to explore and synthesize the decision support methodologies employed in managing MGs during the COVID-19 pandemic. A scoping review was conducted following the PRISMA guidelines covering the period from 2020 to 2024. Studies were categorized by event type (e.g., academic, religious, political, sports) and decision-making tools applied. The review identified a range of decision support techniques, with risk assessment and simulation tools being the most commonly employed across various event types. A total of 199 studies were initially identified, with 10 selected finally for inclusion based on relevance to decision support techniques. Case studies included the successful risk mitigation strategies during the 2020 Hajj, the 2021 Tokyo Olympics, and the 2022 FIFA World Cup in Qatar. Techniques such as fuzzy logic, Bayesian analysis, and multi-criteria decision-making were also highlighted, particularly in complex scenarios. These tools significantly contributed to reducing COVID-19 transmission risks at large-scale events. The review underscores the importance of decision support systems in the safe management of MGs during the pandemic. Further research should focus on the integration of emerging technologies and the long-term impacts of decision support tools on public health management.

Ranking sustainable management alternatives for Chilika wetland ecosystem, in India, using decision support system

Ranking sustainable management alternatives for Chilika wetland ecosystem, in India, using decision support system

Exploring the role of artificial intelligence in oral cancer diagnosis: Review

Oral cancer (OC) is the most common forms of head and neck cancer. Even with advancing research it still continues to have lowest survival rate globally. Histopathologiy still remains the gold standard for oral cancer diagnosis. But, it has few drawbacks being prone to errors (variability) and is time consuming leading to delay in diagnosis. In order to overcome these pitfalls there is a need for alternative diagnostic approaches which are faster and accurate at the same time. Considerable efforts have been focused on exploring the role of artificial intelligence (AI) in medical diagnostics in recent times. The role of AI in medical imaging and diagnosis seems promising. Key benefit of AI is better accuracy, rapid diagnosis and reduced manual visualization of slides. This review article focuses on the role of AI in oral cancer detection with emphasis on machine learning and deep learning methods, decision support systems. Future challenges, benefits and limitations of AI have also been discussed.

A novel multicriteria web‐based decision support system to enhance resource allocation in energy companies: a portfolio selection with c‐optimal PROMETHEE

AbstractIn an uncertain world, decision support systems (DSSs) are essential for effective portfolio management. Electricity companies often face challenges in allocating limited resources among competing projects, influenced by sustainability and regulatory requirements. To address these challenges, multicriteria approaches have been developed, focusing on complex issues involving outranking methods. This paper introduces a methodological advance with a novel algorithm that enhances the c‐optimal PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) approach, resolving scaling issues inherent in the PROMETHEE V method. Our key contribution is a refined procedure for efficiently identifying and comparing c‐optimal solutions while preserving the method's axiomatic integrity, ensuring the selection of the “true best” portfolio. Additionally, we present a web‐based DSS designed to implement this methodology, featuring interactive visualization and sensitivity analysis tools. Freely available online, both the algorithm and DSS were validated through a case study with a Brazilian electricity company, demonstrating their practical benefits and applicability across various business contexts.

Decision Support System for Extracurricular Determination to Increase Student Involvement in Activities Outside the Classroom Using the AHP Method

SMK Dharma Pancasila Medan is a private school located at Jl. Dr. T. Mansyur No.71 C Medan, Kel. PB Selayang. Currently, the selection of extracurricular activities at the school relies on a conventional manual system, lacking a formal system to identify the best extracurricular activities among the available options. This study aims to develop a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) method to evaluate and prioritize extracurricular activities based on four main criteria: achievement, short-distance running, height, and discipline. The system is designed to provide objective and measurable recommendations, facilitating students in choosing the most suitable extracurricular activities to enhance their success in out-of-class activities. The research findings demonstrate that the DSS developed with the AHP method is effective in providing accurate recommendations and increasing student engagement in extracurricular activities. The analysis and assessment process using AHP results in more targeted decisions aligned with the goal of enhancing student involvement. Overall, the system not only aids students in optimal extracurricular selection but also contributes to the development of better extracurricular activity strategies at SMK Dharma Pancasila Medan. This research enriches educational theory and practice by offering practical solutions to the challenges in extracurricular selection and enhances the overall educational experience for students.

Implementing Reinforcement Learning in a Financial Decision Support System for Stock Market Investment

Implementing Reinforcement Learning in a Financial Decision Support System for Stock Market Investment

Comprehensive Approach to TB Diagnosis Powered by Machine Learning in Endemic Countries

Abstract Introduction/Objective Tuberculosis (TB), a leading cause of mortality worldwide, is primarily diagnosed through methods that face numerous challenges, including lengthy result times and diagnostic inaccuracy, particularly in the context of drug-resistant strains and latent infections. This study proposes a novel approach that integrates multiple diagnostic modalities with machine learning to enhance TB diagnosis accuracy. Methods/Case Report The Machine learning model was tailored for the classification of radiological images, specifically CT scans, using original DICOM images to preserve valuable voxel information. The study involved a curated dataset of CT scans from Pakistan, TB-positive (smear+/Culture+ n=72; smear+/culture- n= 3; smear-/Culture+ n=16; smear-/Culture- n=14; clinical and radiological diagnosis only n= 12) and healthy (n=103) scans from COVID-CTSet were included in the training to assess the specificity of the model. Data preprocessing included noise reduction, histogram matching, and data augmentation techniques to ensure a robust training dataset. The methodology also incorporated using k-fold cross validation and a custom learning rate scheduling strategy to optimize the model’s training process. To further test its robustness, the model was evaluated by swapping random patches and analyzing 60 mm patches from the entire CT scan for local feature categorization. Results (if a Case Study enter NA) The model has demonstrated outstanding performance, with sensitivity and specificity exceeding 95% on our internal dataset (n=117) and aligning well with clinical, smear, culture, and anti-TB antibody findings. It also showed promising results on the external NIH TB Portal’s dataset (n= 1,460). The model is robust to variations in CT scanner image quality and noise profiles, maintaining strong generalization across datasets. Even when analyzing random 60mm patches, effectively classifying all local features Conclusion Our study enhances tuberculosis diagnostic methods in endemic regions by blending machine learning with conventional diagnostic techniques. This approach improves TB detection, particularly the early and accurate identification in areas impacted by drug-resistant strains and asymptomatic latent infections. By integrating radiological and clinico-pathological data, we aim to develop a comprehensive diagnostic score. Moving forward, we plan to further integrate this method into clinical decision support systems and adapt the approach to other infectious diseases.

Personalised decision support in the management of patients with musculoskeletal pain in primary physiotherapy care: a cluster randomised controlled trial (the SupportPrim project).

We developed the SupportPrim PT clinical decision support system (CDSS) using the artificial intelligence method case-based reasoning to support personalised musculoskeletal pain management. The aim of this study was to evaluate the effectiveness of the CDSS for patients in physiotherapy practice. A cluster randomised controlled trial was conducted in primary care in Norway. We randomised 44 physiotherapists to (1) use the CDSS alongside usual care or (2) usual care alone. The CDSS provided personalised treatment recommendations based on a case base of 105 patients with positive outcomes. During the trial, the case-based reasoning system did not have an active learning capability; therefore, the case base size remained the same throughout the study. We included 724 patients presenting with neck, shoulder, back, hip, knee, or complex pain (CDSS; n = 358, usual care; n = 366). Primary outcomes were assessed with multilevel logistic regression using self-reported Global Perceived Effect (GPE) and Patient-Specific Functional Scale (PSFS). At 12 weeks, 165/298 (55.4%) patients in the intervention group and 176/321 (54.8%) in the control group reported improvement in GPE (odds ratio, 1.18; confidence interval, 0.50-2.78). For PSFS, 173/290 (59.7%) patients in the intervention group and 218/310 (70.3%) in the control group reported clinically important improvement in function (odds ratio, 0.41; confidence interval, 0.20-0.85). No significant between-group differences were found for GPE. For PSFS, there was a significant difference favouring the control group, but this was less than the prespecified difference of 15%. We identified several study limitations and recommend further investigation into artificial intelligence applications for managing musculoskeletal pain.

CIC 2023: Enhancing Referrals to the Supplemental Nutrition Assistance Program (SNAP) through Clinical Integration of a Standards-Based Decision Support System.

The Supplemental Nutrition Assistance Program (SNAP) is one of the most successful national programs to reduce poverty and improve health outcomes, but millions of Americans who qualify still do not have access to SNAP, and limited data is available to determine how referrals to the program can be completed successfully. We aimed to design and develop a standards-based digital care coordination platform to support closed-loop social services referrals between patients and social and health care providers; and demonstrate the feasibility to screen, diagnose, plan, and complete interventions with selected patients in real-settings. We partnered with community members to design the platform through Community Engagement Studios and with a Federally Qualified Health Center, Local Mental Health Authority, and Food Bank to determine the features and workflow requirements of the platform design. We customized currently available systems to use Fast Healthcare Interoperability Resources (FHIR) Application Programming Interfaces (APIs) that could exchange information in real-time across providers and participants. The platform was successfully demonstrated through a pilot where patients were recruited in clinical settings and referred to a Food Bank that provided SNAP application assistance to study participants. We translated the requirements of the platform to national standards and required workflows of providers and patients. Study participants were notified electronically of the specific steps to follow to complete their SNAP applications while receiving support from specialists from the Food Bank. The pilot demonstrated the feasibility of collecting granular social service referral information that can be used to better address gaps in social care. The pilot also highlighted the importance of further coordination on the usage and harmonization of needs assessment nationally and that current digital systems are still not ready to fully utilize national Social Determinants of Health (SDOH) data standards.

Application of the Simple Additive Weighting (SAW) Method in the Decision Support System for the Selection of Study Programs at the University of Papua

To help prospective students choose a study program that suits their abilities and interests, it is necessary to design a decision support system. This system aims to facilitate the selection process of study programs for prospective students. This research aims to develop a decision support system that can help prospective students in choosing a study program at the Faculty of Engineering, University of Papua in an objective and structured manner. This study applies the Simple Additive Weighting method to facilitate the decision-making process by considering a number of relevant criteria. The evaluation is based on the following criteria: tuition fees, accreditation status, number of teaching staff and staff, and job prospects. The findings of the study revealed that the Informatics Engineering and Civil Engineering Study Programs, both at the S1 level, obtained the highest identical preference value, namely 1, outperforming other study programs. However, the best alternative is the S1 Informatics Engineering Study Program.

Actionable human–water system modelling under uncertainty

Abstract. This paper develops an actionable interdisciplinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used decision support system for water resource planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where water management policies meet or miss their objectives and to explore potential tipping points. The application of the methods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by < 0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 % or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adaptation strategies and related uncertainties for identifying robust decisions.

Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.

World’s Best Practice Locust and Grasshopper Management: Accurate Forecasting and Early Intervention Treatments Using Reduced Chemical Pesticide

World’s Best Practice management of locusts and grasshoppers requires accurate forecasting that helps determine where and when surveys are preferentially conducted so that infestations can be found quickly as part of ensuring early intervention treatments. Using survey data downloaded directly into a Geographic Information System (GIS), as well as rainfall and other factors important in the population dynamics of the species concerned, models within the GIS provide forecasts of future developments. The GIS provides forecasts of likely events and is used by locust and grasshopper experts to make decisions; that is, the forecasting is part of a Decision Support System for improved locust and grasshopper management. Surveys are generally conducted by ground vehicles, but for locusts, surveys by aircraft can be an important way to rapidly find bands. In Australia, dense bands can often be seen from an aircraft flying overhead at a height of 300 m, and similar detection of bands of the desert locust by aircraft has been conducted in Somalia. Swarms can be detected by ground vehicles, but because swarms move, surveying by aircraft is also an important way of locating swarms for treatment. When locust infestations are found, they are rapidly treated as part of early intervention preventive management. However, it is generally recognized that it is extremely difficult for landholders alone to protect crops against locusts and grasshoppers, so government intervention is often necessary. These organizations use a variety of treatment techniques to reduce the amount of chemical pesticide applied either by strip spraying or treating very dense infestations, such as roosting swarms, or using biopesticides. These techniques, as used in a number of countries, have proven to be very effective in managing locust populations while reducing the risk to the natural environment and human health.

Decision support system for assessing the economic efficiency of territorial-production clusters: Application issues

Decision support system for assessing the economic efficiency of territorial-production clusters: Application issues

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. This review aims to present the most-used visualization techniques to estimate CVD risk. In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.

Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care. A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients' experiences and attitudes via focus groups and interviews. For Stage 1, a total of 281 respondents' (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR = 2.97; 95% CI, 1.36-6.49) and embrace (OR = 2.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS's role in their care to be disseminated by surgeons across multiple platforms. The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS's role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.