Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System(CDSS).Generally,CDSS is used to predict the individuals’heart disease and periodically update the condition of the patients.This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers.Here,the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease.Then,the optimization is achieved using the Adam Optimizer(AO)model with the publicly available dataset known as the Stalog dataset.This flowis used to construct the model,and the evaluation is done with various prevailing approaches like Decision tree,Random Forest,Logistic Regression,Naive Bayes and so on.The statistical analysis is done with theWilcoxon rank-summethod for extracting the p-value of the model.The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy.This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage.Thus,the earlier treatment process helps to eliminate the death rate.Here,simulation is done withMATLAB 2016b,and metrics like accuracy,precision-recall,F-measure,p-value,ROC are analyzed to show the significance of the model.

Progress of clinical decision support systems in stroke nursing care

Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent computer system,can assist nurses in decision-mak-ing to collect information quickly,make the most suitable personalized decisions for patients,and improve nurses’decision-making judgment and quality of care.Promoting the development and application of decision support sys-tems in stroke nursing significantly enhances the nursing staff’s work quality and patients’prognosis.Therefore,this paper reviews the research progress of domestic and international clinical decision support systems in stroke nursing care to provide other researchers with specific research directions for developing and applying decision support systems in stroke nursing care.

Bibliometrics analysis of clinical decision support systems research in nursing

Objective:Artificial intelligence(AI)has a big impact on healthcare now and in the future.Nurses play an important role in the medical field and will benefit greatly from this technology.AI-Enabled Clinical Decision Support Systems have received a great deal of attention recently.Bibliometric analysis can offer an objective,systematic,and comprehensive analysis of a specific field with a vast background.However,no bibliometric analysis has investigated AI-enabled clinical decision support systems research in nursing.The purpose of research to determine the characteristics of articles about the global performance and development of AI-enabled clinical decision support systems research in nursing.Methods:In this study,the bibliometric approach was used to estimate the searched data on clinical decision support systems research in nursing from 2009 to 2022,and we also utilized CiteSpace and VOSviewer software to build visualizing maps to assess the contribution of different journals,authors,et al.,as well as to identify research hot spots and promising future trends in this research field.Result:From 2009 to 2022,a total of 2,159 publications were retrieved.The number of publications and citations on AI-enabled clinical decision support systems research in nursing has increased obvious ly in recent years.However,they are understudied in the field of nursing and there is a compelling need to develop more high-quality research.Conclusion:AI-Enabled Nursing Decision Support System use in clinical practice is still in its early stages.These analyses and results hope to provide useful information and references for future research directions for researchers and nursing practitioners who use AI-enabled clinical decision support systems.

Clinical decision support for drug related events: Moving towards better prevention

Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.

Advanced decision support for complex clinical decisions

A Physician’s decision-making skills are directly related to the patient’s positive outcomes. Therefore, a wealth of medical knowledge and clinical experience are key assets for a physician to have. The goal here is to use historical clinical data and relationships processed by Artificial Intelligence (AI) techniques to aid physicians in their decision making process. Presenting this information in a Clinical Decision Support System (CDSS) is an effective means to consolidate decision results. The CDSS provides a large number of medical support functions to help clinicians make the most reasonable diagnosis and choose the best treatment measures. Initial results have shown great promise in accurately predicting Fibrosis Stage in Hepatitis patients. Utilizing this tool could mitigate the need for some liver biopsies in the more than 170 million Hepatitis patients worldwide. The prototype is extendable to accommodate additional techniques (for example genetic algorithms and logistics regression) and additional medical domain solutions (for example HIV/AIDS).

Nursing decision support system:application in electronic health records

The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.

Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics. With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient. The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts. Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging. The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study’s main goal. In order to assess the system’s acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards. Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of “strongly agree”. This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.

Computerized decision support in adult and pediatric critical care

Computerized decision support(CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization ofmeaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

A clinical decision support system using rough set theory and machine learning for disease prediction

Objective Technological advances have led to drastic changes in daily life,and particularly healthcare,while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient health-care records with digital files.Using the latest technology and data mining techniques,we aimed to develop an automated clinical decision support system(CDSS),to improve patient prognoses and healthcare delivery.Our proposed approach placed a strong emphasis on improvements that meet patient,parent,and physician expec-tations.We developed a flexible framework to identify hepatitis,dermatological conditions,hepatic disease,and autism in adults and provide results to patients as recommendations.The novelty of this CDSS lies in its inte-gration of rough set theory(RST)and machine learning(ML)techniques to improve clinical decision-making accuracy and effectiveness.Methods Data were collected through various web-based resources.Standard preprocessing techniques were applied to encode categorical features,conduct min-max scaling,and remove null and duplicate entries.The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values,respectively.A rough set approach was applied as feature selection,to remove highly redundant and irrelevant elements.Then,various ML techniques,including K nearest neighbors(KNN),linear support vector machine(LSVM),radial basis function support vector machine(RBF SVM),decision tree(DT),random forest(RF),and Naive Bayes(NB),were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle.The model was implemented in Python,and various validity metrics,including precision,recall,F1-score,and root mean square error(RMSE),applied to measure its performance.Results Features were selected using an RST approach and examined by RF analysis and important features of hepatitis,dermatology conditions,hepatic disease,and autism determined by RST and RF exhibited 92.85%,90.90%,100%,and 80%similarity,respectively.Selected features were stored as electronic health records and various ML classifiers,such as KNN,LSVM,RBF SVM,DT,RF,and NB,applied to classify patients with hepatitis,dermatology conditions,hepatic disease,and autism.In the last phase,the performance of proposed classifiers was compared with that of existing state-of-the-art methods,using various validity measures.RF was found to be the best approach for adult screening of:hepatitis with accuracy 88.66%,precision 74.46%,recall 75.17%,F1-score 74.81%,and RMSE value 0.244;dermatology conditions with accuracy 97.29%,precision 96.96%,recall 96.96%,F1-score 96.96%,and RMSE value,0.173;hepatic disease,with accuracy 91.58%,precision 81.76%,recall 81.82%,F1-Score 81.79%,and RMSE value 0.193;and autism,with accuracy 100%,precision 100%,recall 100%,F1-score 100%,and RMSE value 0.064.Conclusion The overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis,dermatology conditions,hepatic disease,and autism.

基于CDSS的统一消息集成平台关键技术研究与应用

目的 研究临床决策支持系统的统一消息集成平台关键技术及应用,为临床医务人员提供综合的辅助决策,改变以往单点消息提醒和预警的混乱状况。方法 利用数据集成技术和新一代人工智能数据治理技术,对各类辅助决策类消息进行统一界面的、多维度的展示。结果 统一消息集成平台的构建,能够减少各类消息预警提示框对业务系统的干扰,减少关键提醒信息的遗漏。结论 消息集成平台的构建能够规范单点消息提醒和预警,有效减少用户对关键预警信息的漏读率,提升医疗安全和质量。

基于人工智能的CDSS诊断符合率研究

目的通过分析临床决策支持系统(CDSS)推出的诊断,包含目标病例最终诊断的覆盖情况及准确度,评价系统诊断疾病的效果,评估现有临床知识库标准程度。方法选定医院心内科1850例出院确诊病历作为测试病历,对比CDSS推出的诊断与患者病案首页最终诊断的覆盖情况。结果通过3轮评测,最终得到CDSS召回率为66.5%,准确率93.6%。结论 CDSS推出诊断绝大多数都是合理的,多数情况下覆盖了临床医生认为重要的确诊诊断,能够帮助临床医生作出完整诊断,并快速聚焦诊断得出鉴别结果。

基于HIS的CDSS的探讨

临床决策支持系统(Clinicdecisionsupportsystem,CDSS)是近年医院信息化建设的又一个热点问题,其所涉及的逻辑推理方法、知识库建立、数据挖掘、以及与现有HIS的整合等问题都处于探索阶段。本文从HIS、CDSS等有关概念谈传统CDSS的不足,以及基于HIS的CDSS的研究思路,最后重点分析基于HIS的CDSS的功能要求,为CDSS的研究提供参考资料。

基于统一信息模型的CDSS与EMR接口技术研究与实现

临床决策支持系统(CDSS)的实施和应用具有重要的临床意义,也是医院电子病历系统(EMR)应用水平评价的重要内容。由于CDSS和EMR接口开发的困难,CDSS的部署成本高、效率低,阻碍了CDSS的广泛应用。本文在分析接口开发挑战的基础上,提出了一种基于统一信息模型的接口实现技术,并在临床案例中验证了方法的可行性。

基于新型CDSS辅助的病案首页填报及质控

基于人工智能技术的新型临床决策支持系统,可通过构建设计编码规则与知识库系统、优化质控逻辑与质控指标体系,对医疗过程进行智能监测和管控,实时监控病案首页的填写情况与质量,强化医疗过程质量稽查,对存在质量缺陷与不符质控规则的诊疗流程进行预警提醒,有助于实现更有效的病案管理与医疗质控。

基于CCDSS管理阿尔茨海默病患者对其生活能力及生存质量影响的效果评价

目的探讨基于CCDSS的慢病管理对阿尔茨海默病患者生活能力及生存质量的影响。方法选取铜陵市仁和医院在2018年1月-2019年12月收治的轻、中度AD患者63例,采用抽签法分为CCDSS组32例和对照组31例。CCDSS组采用基于CCDSS的慢病管理对阿尔茨海默病患者实施干预,对照组采用常规治疗及护理。采用MMSE量表评定AD患者认知功能,ADL量表评估AD患者的生活能力,SF-36量表评估两组AD患者的生存质量,并比较两组患者的认知功能、生活能力和生存质量;随访并比较两组不良事件发生率。结果采用基于CCDSS的慢病管理干预后,两组患者干预前后MMSE评分无显著差异(P>0.05),但干预后CCDSS组患者PSMS评分、IADLs评分和SF-36评分均较干预前显著提高,且与对照组相比有显著差异(P<0.05);干预后CCDSS组不良事件发生率低于对照组,差异有统计学意义(P<0.05)。结论基于CCDSS的慢病管理可以改善阿尔茨海默病患者生活能力及生存质量,在慢病管理中值得深入研究。

CDSS辅助医疗质量管理的设计与实践探索

在医疗决策支持的基础上,进行基于医疗管理的专家意见收集与验证,建立适合本地的规则并转化为医疗管理知识库。探索运用于医疗管理的临床决策支持系统功能设计,使其具有与临床业务、医疗管理业务高度融合、有效协助诊疗质量管理等特点,该尝试在医院取得了良好的应用效果,有助于提升医疗机构工作效率和医疗服务质量,一定程度上可改善该系统在医院应用中存在的问题。本文探讨医疗质量管理的实际运用中的具体问题,为解决医疗质量智慧化管理提供新思路。

CCDSS在慢性病管理中的应用进展

综述计算机临床决策支持系统(CCDSS)的概念、功能及其在慢性病管理中的应用现状,旨在为CCDSS在我国社区中的使用及推广提供新思路。

A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data

Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.With the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become possible.Recently,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot.Based on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions.The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.