Clinical Decision Support Research Articles

Improving patient’s medical history classification using a feature construction approach based on situation awareness and granular computing

AbstractHealthcare decision support systems aid physicians in disease classification by analyzing patients’ medical histories to suggest preliminary diagnoses. As physicians largely base their analysis on anamnesis, integrating this process into an automated recommendation system can expedite decision-making and transition to relevant clinical investigations, thus enhancing efficiency in diagnosing potential pathologies. In this research, an innovative method for feature construction is introduced, drawing on the concepts of Situation Awareness and Granular Computing. The aim of this method is to enhance the performance of out-of-the-box classification algorithms used in machine learning. The approach is specifically tailored to mimic physicians’ cognitive processes when analyzing a patient’s medical history, resulting in the generation of new, information-dense features that can be used for classification tasks. By employing this strategy, a deeper comprehension of the data can be achieved, as well as a more precise categorization of anamneses in relation to possible medical conditions. To authenticate the efficacy of the proposed technique, three major disease categories, namely cardiac, gastrointestinal, and thyroid, were considered. The dataset comprised 1213 medical histories. The experimental results indicate that the study’s six classifiers attained a balanced accuracy exceeding 90%. Among these, the SVM classifier demonstrated the highest balanced accuracy at 93%. Overall, the proposed approach resulted in an average increase of 16 percentage points in balanced accuracy, representing an improvement over the traditional methods commonly employed in machine learning. This approach could be integrated into a clinical decision support system, aiding physicians in accurately identifying necessary investigations and expediting diagnosis.

Abstract A139: Patient and provider barriers and facilitators to lung cancer screening: I-STEP 2 Study

Abstract Purpose: While lung cancer is a leading cause of death in the U.S., annual lung cancer screening (LCS) rates remain low. After deployment of an implementation intervention to increase lung cancer screening (LCS) and referral, we used a qualitative approach to identify barriers and facilitators, and recommended supports to increase screening. Methods: Semi-structured interviews were conducted with patients (n=66) and providers (n=59) across 15 BJC Consortium hospitals. Interview domains included perceptions and experiences with LCS referral and completion, barriers and facilitators to LCS, and the impact of the COVID-19 pandemic on these processes. Patients were eligible if they had been referred for LCS. Providers were eligible if they were involved in the LCS decision-making and referral process. Interviews were audio-recorded, de-identified, and transcribed. Deductive and inductive codes were developed separately for patients and providers. Coded data were analyzed to determine themes. Results: Patients cited transportation, uncertainty surrounding insurance coverage for screening, and the COVID-19 pandemic as barriers to accessing LCS. Due to the Medicare and Medicaid requirement of a documented shared decision-making visit prior to providing an LCS referral, providers cited time constraints during patient encounters as a barrier to delivering optimal LCS care. Both patients and providers reported low general awareness of LCS (compared to other preventive screening services) as a barrier to utilization. Patients described many facilitators to LCS including fears regarding cancer risk, knowledge of the benefits of early cancer detection in improving cancer outcomes, provider recommendations, ease of LCS process, and receiving educational materials about LCS. Providers also describe access to evidence-based outreach and education materials regarding LCS, and implementation of clinical decision support tools (e.g. pre-screening for LCS eligibility and insurance authorization, integration of EMR prompts centered around LCS and nodule surveillance) as facilitators for screening. Patients and providers highlighted the importance of LCS navigators in facilitating communication and reducing barriers to LCS and surveillance – specifically, patients found one-on-one support and education with navigators to be encouraging as they underwent LCS. Conclusion: While there are some key barriers related to lung cancer screening and follow-up, there are many facilitators to screening that can be leveraged to increase referrals and enable successful follow-up management after screening. Recommended additions to basic lung screening intervention materials include provider education on how lung cancer screening referral processes are unique in comparison to other screenings, how providers can foster patient understanding of lung nodules, and local support and resources to help patients navigate barriers to lung screening. Citation Format: Amy Ayala, Meera Muthukrishnan, Sydney Beache, Michelle Silver, Courtney Harriss, Graham Colditz, Aimee James. Patient and provider barriers and facilitators to lung cancer screening: I-STEP 2 Study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A139.

Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.

To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows. Guided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery. Interviews with thirteen HHC clinicians yielded 16 codes mapping to the CDS "Five-Rights" framework (right information, right person, right format, right channel, right time) and 11 codes for unintended consequences and training needs. Clinicians favored risk levels displayed in color-coded horizontal bars, concrete risk indicators in bullet points, and actionable instructions in the existing EHR system. They preferred non-intrusive risk alerts requiring mandatory confirmation. Clinicians anticipated risk information updates aligned with patient's condition severity and their visit pace. Additionally, they requested training to understand the CDSS's underlying logic, and raised concerns about information accuracy and data privacy. While recognizing CDSS's value in enhancing early risk warning, clinicians highlighted concerns about increased workload, alert fatigue, and CDSS misuse. The top risk factors identified by machine learning algorithms, especially text features, can be ambiguous due to a lack of context. Future research should ensure that CDSS outputs align with clinical evidence and are explainable. This study identified HHC clinicians' expectations, preferences, adaptations, and unintended uses of CDSS for early risk warning. Our findings endorse operationalizing the CDS "Five-Rights" framework to optimize CDSS information delivery and integration into HHC workflows.

Guidelines or mindlines? – implementing a new CKD guideline in German primary care

BackgroundThe development of clinical guidelines aimed at GPs is a key strategy to improving the management of chronic kidney disease (CKD). In 2019, the first CKD guideline aimed specifically at GPs practicing in Germany was published by the German College of General Practitioners and Family Physicians (DEGAM.)AimsThe aim of this study is to identify the barriers and enablers for the implementation of this guideline. The results of this project, together with quantitative evaluation against quality indicators for CKD in primary care will inform an update to the guideline.MethodsWe performed 17 semi-structured interviews with GPs practicing in Berlin and Brandenburg. Transcripts were analysed using qualitative content analysis as described by Mayring.ResultsWe found that the perception of low clinical priority of CKD compared to other chronic diseases, opportunity cost of using guidelines, as well as poor patient understanding were significant barriers. GPs expressed that improved graphic design or integration of guideline recommendations in clinical decision support systems were enabling factors. Clinical problems concerning CKD were mostly solved by recourse to informal communication with specialists. GPs reported that they rarely consulted CKD guidelines as an aide to clinical decision making.ConclusionThe most significant barrier to use was that guidelines were not used as step-by-step decision aide in consultations with patients. Our analysis suggests that informal contact between primary and secondary care is significant conduit for evidence-based information on CKD in German primary care. Implementation projects should support the development of these relationships.

Large language models in medical and healthcare fields: applications, advances, and challenges

Large language models (LLMs) are increasingly recognized for their advanced language capabilities, offering significant assistance in diverse areas like medical communication, patient data optimization, and surgical planning. Our survey meticulously searched for papers with keywords such as “medical,” “clinical,” “healthcare,” and “LLMs” across various databases, including ACM and Google Scholar. It sought to delve into the latest trends and applications of LLMs in healthcare, analyzing 175 relevant publications to support both practitioners and researchers in the field. We have compiled 56 experimental datasets, various evaluation methods and reviewed cutting-edge LLMs across tasks. Our comprehensive analysis of LLMs in healthcare applications, including medical question-answering, dialogue summarization, electronic health record generation, scientific research, medical education, medical product safety monitoring, clinical health reasoning, and clinical decision support. Furthermore, we have identified the challenges, including data security, inaccurate information, fairness and bias, plagiarism, copyrights, and accountability, and the potential solutions, namely de-identification framework, references,counterfactually fair prompting,opening and ending control codes, and establishing normative standards,to address these open issues,respectively. The findings of this survey exert a profound impact on spurring innovation in practical applications and addressing inherent challenges within the academic and medical communities.

A clinical decision support tool for metabolic dysfunction-associated steatohepatitis in real-world clinical settings: a mixed-method implementation research study protocol.

Aim: A clinical decision support (CDS) tool for metabolic dysfunction-associated steatohepatitis (MASH) was developed to align health systems with clinical guidelines detailed in the MASH Clinical Care Pathway and improve patients' proactive self-management of their disease. The tool includes a provider-facing web-based application and a mobile application (app) for patients. This protocol outlines a pilot study that will systematically evaluate the implementation of the tool in real-world clinical practice settings. Materials & methods: This implementation research study will use a simultaneous mixed-methods design and is guided by the Consolidated Framework for Implementation Research. The CDS tool for MASH will be piloted for ≥3months at multiple US-based sites with eligible gastroenterologists and hepatologists (n=5-10 per site) and their patients (n=50-100 per site) with MASH or suspected MASH. Each pilot site may choose one or all focus areas within the tool (i.e., risk stratification, screening and referral, or patient care management), based on on-site capabilities. Prior to and at the end of the pilot period, providers and patients will complete quantitative surveys and partake in semi-structured interviews. Outcomes will include understanding the feasibility of implementing the tool in real-world clinical settings, its effectiveness in increasing patient screenings and risk stratification for MASH, its ability to improve provider and patient knowledge of MASH, barriers to adoption of the tool and the tool's capacity to enhance patient engagement and satisfaction with their care. Conclusion: Findings will inform the scalable implementation of the tool to ensure patients at risk for MASH are identified early, referred to specialty care when necessary and managed appropriately. Successful integration of the patient app can contribute to better health outcomes for patients by facilitating their active participation in the management of their condition.

The Development of an Electronic Medical Record System to Improve Quality of Care for Individuals With Type 1 Diabetes in Rwanda: Qualitative Study.

Electronic medical record (EMR) systems have the potential to improve the quality of care and clinical outcomes for individuals with chronic and complex diseases. However, studies on the development and use of EMR systems for type 1 (T1) diabetes management in sub-Saharan Africa are few. The aim of this study is to analyze the need for improvements in the care processes that can be facilitated by an EMR system and to develop an EMR system for increasing quality of care and clinical outcomes for individuals with T1 diabetes in Rwanda. A qualitative, cocreative, and multidisciplinary approach involving local stakeholders, guided by the framework for complex public health interventions, was applied. Participant observation and the patient's personal experiences were used as case studies to understand the clinical care context. A focus group discussion and workshops were conducted to define the features and content of an EMR. The data were analyzed using thematic analysis. The identified themes related to feature requirements were (1) ease of use, (2) automatic report preparation, (3) clinical decision support tool, (4) data validity, (5) patient follow-up, (6) data protection, and (7) training. The identified themes related to content requirements were (1) treatment regimen, (2) mental health, and (3) socioeconomic and demographic conditions. A theory of change was developed based on the defined feature and content requirements to demonstrate how these requirements could strengthen the quality of care and improve clinical outcomes for people with T1 diabetes. The EMR system, including its functionalities and content, can be developed through an inclusive and cocreative process, which improves the design phase of the EMR. The development process of the EMR system is replicable, but the solution needs to be customized to the local context.

Do clinical decision support tools improve quality of care outcomes in the primary prevention of cardiovascular disease: A systematic review and meta-analysis

AimTo assess the effectiveness of Clinical Decision Support Tools (CDSTs) in enhancing the quality of care outcomes in primary cardiovascular disease (CVD) prevention. MethodsA systematic review was undertaken in accordance with PRISMA guidelines, and included searches in Ovid Medline, Ovid Embase, CINAHL, and Scopus. Eligible studies were randomized controlled trials of CDSTs comprising digital notifications in electronic health systems (EHS/EHR) in various primary healthcare settings, published post-2013, in patients with CVD risks and without established CVD. Two reviewers independently assessed risk of bias using the Cochrane RoB-2 tool. Attainment of clinical targets was analysed using a Restricted Maximum Likelihood random effects meta-analysis. Other relevant outcomes were narratively synthesised due to heterogeneity of studies and outcome metrics. ResultsMeta-analysis revealed CDSTs showed improvement in systolic (Mean Standardised Difference (MSD)=0.39, 95 %CI=-0.31, -1.10) and diastolic blood pressure target achievement (MSD=0.34, 95 %CI=-0.24, -0.92), but had no significant impact on lipid (MSD=0.01; 95 %CI=-0.10, 0.11) or glucose target attainment (MSD=-0.19, 95 %CI=-0.66, 0.28). The CDSTs with active prompts increased statin initiation and improved patients’ adherence to clinical appointments but had minimal effect on other medications and on enhancing adherence to medication. ConclusionCDSTs were found to be effective in improving blood pressure clinical target attainments. However, the presence of multi-layered barriers affecting the uptake, longer-term use and active engagement from both clinicians and patients may hinder the full potential for achieving other quality of care outcomes. Lay SummaryThe study aimed to evaluate how Clinical Decision Support Tools (CDSTs) impact the quality of care for primary cardiovascular disease (CVD) management. CDSTs are tools designed to support healthcare professionals in delivering the best possible care to patients by providing timely and relevant information at the point of care (ie. digital notifications in electronic health systems). Although CDST are designed to improve the quality of healthcare outcomes, the current evidence of their effectiveness is inconsistent. Therefore, we conducted a systematic review with meta-analysis, to quantify the effectiveness of CDSTs. The eligibility criteria targeted patients with CVD risk factors, but without diagnosed CVD. The meta-analysis found that CDSTs showed improvement in systolic and diastolic blood pressure target achievement but did not significantly impact lipid or glucose target attainment. Specifically, CDSTs showed effectiveness in increasing statin prescribing but not antihypertensives or antidiabetics prescribing. Interventions with CDSTs aimed at increasing screening programmes were effective for patients with kidney diseases and high-risk patients, but not for patients with diabetes or teenage patients with hypertension. Alerts were effective in improving patients' adherence to clinical appointments but not in medication adherence. This study suggests CDSTs are effective in enhancing a limited number of quality of care outcomes in primary CVD prevention, but there is need for future research to explore the mechanisms and context of multiple barriers that may hinder the full potential for cardiovascular health outcomes to be achieved.

Flipping healthcare by including the patient perspective in integrated care pathway design: A scoping review

BackgroundDespite the recognized benefits of integrating patient perspectives into healthcare design and clinical decision support, theoretical approaches and standardized methods are lacking. Various strategies, such as developing pathways, have evolved to address these challenges. Previous research emphasized the need for a framework for care pathways that includes theoretical principles, extensive user involvement, and data from electronic health records to bridge the gap between different fields and disciplines. Standardizing the representation of the patient perspective could facilitate its sharing across healthcare organizations and domains and its integration into journal systems, shifting the balance of power from the provider to the patient. ObjectivesThis study aims to 1) Identify research approaches taken to develop patient-centred, integrated, care pathways supported by electronic health records 2) Propose a socio-technical framework for designing patient-centred care pathways across multiple healthcare levels that integrates the voice of the patient with the knowledge of the care provider and technological perspectives. MethodsThis study conducted a scoping review following the Joanna Briggs Institute guidelines and PRISMA-ScR protocol. The databases PubMed, Scopus, Web of Science, ProQuest, IEEE, and Google Scholar were searched using a key term search strategy including variations of patient-centred, integrated care, pathway, framework and model to identify relevant studies. Eligible articles included peer-reviewed literature documenting methodologies for mapping patient-centred, integrated care pathways in healthcare service design. ResultsThis review summarizes the application of care pathway modelling practices across various areas of healthcare innovation. The search resulted in 410 studies, with 16 articles included after the full review and grey literature search. ConclusionsOur research illustrated incorporating patient perspectives into modelling care pathways and healthcare service design. Regardless of the medical domain, our methodology proposes an approach for modelling patient-centred, integrated care pathways across the care continuum, including using electronic health records to support the pathways.

Machine Learning in Clinical Decision Support: Applications, Challenges, and Future Directions

Machine Learning in Clinical Decision Support: Applications, Challenges, and Future Directions

Machine Learning-Based Predictions of Mortality and Readmission in Type 2 Diabetes Patients in the ICU

Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the potential of machine learning techniques for enhancing predictive accuracy has not been fully realized. This study seeks to develop and validate predictive models employing machine learning algorithms to forecast mortality and 30-day post-discharge readmission rates among ICU type 2 diabetes patients, thereby enhancing predictive accuracy and supporting clinical decision-making. Data were extracted and preprocessed from the MIMIC-III database, focusing on 14,222 patients with type 2 diabetes and their corresponding ICU admission records. Comprehensive information, including vital signs, laboratory results, and demographic characteristics, was utilized. Six machine learning algorithms—bagging, AdaBoost, GaussianNB, logistic regression, MLP, and SVC—were developed and evaluated using 10-fold cross-validation to predict mortality at 3 days, 30 days, and 365 days, as well as 30-day post-discharge readmission rates. The machine learning models demonstrated strong predictive performance for both mortality and readmission rates. Notably, the bagging and AdaBoost models showed superior performance in predicting mortality across various time intervals, achieving AUC values up to 0.8112 and an accuracy of 0.8832. In predicting 30-day readmission rates, the MLP and AdaBoost models yielded the highest performance, with AUC values reaching 0.8487 and accuracy rates of 0.9249. The integration of electronic health record data with advanced machine learning techniques significantly enhances the accuracy of mortality and readmission predictions in ICU type 2 diabetes patients. These models facilitate the identification of high-risk patients, enabling timely interventions, improving patient outcomes, and demonstrating the significant potential of machine learning in clinical prediction and decision support.

Impact of Triage Nurse Training on Accuracy and Efficiency: A Systematic Review-Based Study

Background Triage in the emergency department (ED) is a crucial procedure that establishes the urgency of patient care based on symptoms at presentation. Although the efficacy of triage nurse training programs varies, their goal is to increase the precision and efficiency of their decision-making. Aim With a focus on research done between 2019 and 2024, the systematic review attempts to evaluate the effect of training programs on the accuracy and efficiency of triage nurses in emergency departments. Method We conducted a thorough search of the PubMed, EMBASE, and Web of Science databases to find pertinent research. Studies assessing training treatments for triage nurses and reporting accuracy and efficiency-related results were included in the inclusion criteria. Both the quality assessment and data extraction were carried out separately by two reviewers. Result Ten studies that used a range of triage training techniques and covered a variety of geographic areas satisfied the inclusion criteria. Research findings consistently showed that, despite variations in study designs and outcome measures, training programs increased triage accuracy and efficiency. Interventions included clinical decision support system integration, training based on simulation, and conventional classroom-based instruction. Conclusion By increasing decision-making efficiency and accuracy in patient evaluation, training programs greatly improve the performance of triage nurses in emergency departments. To improve comparability and support evidence-based policies, future research must standardize training methodologies and outcome measurements.

A clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals

A clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals

AI-Led Healthcare Leadership: Unveiling Nursing Trends and Pathways Ahead

Background: Artificial intelligence (AI) is transforming healthcare systems by improving operational efficiency, simplifying patient care procedures, and improving diagnostic accuracy. Artificial intelligence (AI) technologies, like machine learning and natural language processing, present previously unheard-of chances to quickly and accurately evaluate enormous volumes of healthcare data, assisting with clinical decision-making and enhancing patient outcomes. Aim thorough examination and analysis of artificial intelligence's impact on healthcare leadership, with a particular emphasis on present nursing trends and their implications for the future. The study tries to uncover the advantages, difficulties, and consequences of AI integration by looking at how AI technologies including clinical decision support systems, predictive analytics, robots, natural language processing, and telehealth are being used in nursing practice. Method: A comprehensive analysis including research articles published between 2015 and 2024 was carried out. To give a thorough overview of AI's present and future uses in healthcare, major themes and trends were found and summarized. Results: By stressing AI's role in improving diagnostic accuracy and patient outcomes, the study highlights the technology's major contributions to drug discovery, virtual patient care, and medical imaging. Human-centered design concerns, the necessity of educational changes, and ethical challenges surrounding the application of AI surfaced as crucial topics needing attention. Conclusion: AI has enormous potential to transform healthcare by enhancing operational effectiveness, optimizing the delivery of care, and increasing diagnostic precision. Still, ethical issues must be resolved, interdisciplinary cooperation must be promoted, and educational frameworks must be improved in order to provide healthcare workers with the necessary AI skills.

Influence of the use of a tablet-based clinical decision support algorithm by general practitioners on the consultation process: the example of FeverTravelApp

BackgroundDespite the proven positive effects of clinical decision support systems (CDSSs) on general practitioners’ (GPs’) performance and patient management, their adoption remains slow. Several factors have been proposed to explain GPs' reluctance to adopt these tools. This study hypothesizes that the influence of CDSSs on patient-physician interactions could be a determining factor. To explore this hypothesis, we utilized the FeverTravelApp, designed to assist GPs in managing patients presenting with fever after returning from the tropics. A case–control study was conducted, observing and analyzing fourteen consultations between seven physicians and three simulated patients. Each physician conducted consultations both with and without the FeverTravelApp. The consultations were video-recorded and analyzed using a custom analysis grid based on three existing tools. Simulated patients completed the Communication Assessment Tool (CAT) after each consultation, and each physician participated in a semistructured interview following the use of the app.ResultsThe use of the FeverTravelApp influenced multiple aspects of the consultation, particularly communication. Both patient and GP speaking times decreased, while active silence (no one talking while the GP actively performed a task) increased. GPs focused more on the app, which reduced direct patient interaction. However, this influence seemed to bother GPs more than simulated patients, who rated their GPs equally whether the app was used or not. This could be because patients felt better understood when GPs asked fewer but more specific questions related to travel medicine, thus effectively addressing their concerns.ConclusionsThis study supports the hypothesis that CDSSs influence consultation dynamics, which may contribute to their slow adoption. It is essential to involve clinicians early in the development of CDSSs to adapt them to clinical workflows and ensure system interoperability. Additionally, tools that allow clinicians to follow the entire clinical reasoning process, such as decision trees, are needed. Further research is necessary to confirm these findings in real patient settings and to develop CDSSs that meet both patients’ and GPs’ expectations.

Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

When time is of the essence: ethical reconsideration of XAI in time-sensitive environments

The objective of explainable artificial intelligence systems designed for clinical decision support (XAI-CDSS) is to enhance physicians’ diagnostic performance, confidence and trust through the implementation of interpretable methods, thus providing...

Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice.

Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in healthcare is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. ChatGPT could be exploited in the medical field to improve patient education and information, thus increasing compliance. ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education. It could assist the clinician in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, it is unclear whether it will be possible to use ChatGPT for the management of patients who require rapid decisions. Indeed, many drawbacks are associated with the daily use of these technologies in the medical field, such as insufficient expertise in specialized fields and a lack of comprehension of the context in which it works. The pros and cons of its use have been explored in this review, which was not written with the help of ChatGPT.

A drug mix and dose decision algorithm for individualized type 2 diabetes management

Pharmacotherapy guidelines for type 2 diabetes (T2D) emphasize patient-centered care, but applying this approach effectively in outpatient practice remains challenging. Data-driven treatment optimization approaches could enhance individualized T2D management, but current approaches cannot account for drug-specific and dose-dependent variations in safety and efficacy. We developed and evaluated an AI Drug mix and dose Advisor (AIDA) for glycemic management, using electronic medical records from 107,854 T2D patients in the SingHealth Diabetes Registry. Given a patient’s medical profile, AIDA leverages a predict-then-optimize approach to identify the minimal drug mix and dose changes required to optimize glycemic control, subject to clinical knowledge-based guidelines. On unseen data from large internal, external, and temporal validation sets, AIDA recommendations were estimated to improve post-visit glycated hemoglobin (HbA1c) by an average of 0.40–0.68% over standard of care (P < 0.0001). In qualitative evaluations on 60 diverse cases by a panel of three endocrinologists, AIDA recommendations were mostly rated as reasonable and precise. Finally, AIDA’s ability to account for drug-dose specifics offered several advantages over competing methods, including greater consistency with practice preferences and clinical guidelines for practical but effective options, indication-based treatments, and renal dosing. As AIDA provides drug-dose recommendations to improve outcomes for individual T2D patients, it could be used for clinical decision support at point-of-care, especially in resource-limited settings.