Effectiveness of simulation-based learning regarding management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students:A quasi-experimental study

Objective:To assess the effectiveness of simulation-based learning regarding the management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students.Methods:This was a quasi-experimental study conducted among 1152nd-year nursing students.The participants were selected by a simple random sampling technique.The participants were divided into an experimental(n=56)and a comparison group(n=59)by a random table method.Data were analyzed using descriptive and inferential statistics with SPSS version 20.Results:There were significant differences in mean post-test knowledge scores(P=0.03)and mean post-test self-efficacy scores(P=0.001)between the experimental and the comparison groups while the difference in mean post-test clinical decision-making ability scores between the two groups was non-significant(P=0.07).A positive correlation was found between knowledge and clinical decision-making ability in pre-test(P=0.03)and in post-test(P<0.001)and a non-significant correlation was found between pre-test knowledge and self-efficacy score(P=0.52)among the experimental group.Conclusions:Simulation-based learning regarding the management of post-COVID complications is effective among nursing students.Simulation labs should be established in health care settings where simulation training can be provided for updating the knowledge,clinical decision-making ability,and self-efficacy of nursing personnel during program installment and continuous nursing education.

Discussion on Homogenization of Emergency Clinical Decision

The objective of this work is to explore how to realize the homogenization of emergency clinical decision, and it means that patients receive the same effect of clinical decisions and the treatment in a different hospital. In order to achieve that, emergency doctors should first have the same clinical thinking and thinking mode which is the biggest challenge for homogenization of emergency clinical decision. The task of emergency medicine is to give priority to the treatment of critically ill patients, so step-down thinking of “excluding life-threatening symptoms first” is the basis, the preemptive thinking is the means, and Process thinking is the key of homogenization;The initial diagnosis and treatment mode of symptom-oriented is the starting point for emergency decision;establishing a unified “checklist” can not only broaden the lateral thinking of emergency doctors, but also unify the thinking of differential diagnosis of emergency;dynamic observation should run through the whole diagnosis and treatment process, which is necessary for the homogenization of emergency decision.

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.

Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding

Artificial Intelligence(AI)is a type of intelligence that comes from machines or computer systems that mimics human cognitive function.Recently,AI has been utilized in medicine and helped clinicians make clinical decisions.In gastroenterology,AI has assisted colon polyp detection,optical biopsy,and diagnosis of Helicobacter pylori infection.AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding.Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding.AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion,determining bleeding stigmata during endoscopy,and predicting recurrence of gastrointestinal bleeding.The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding,primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods.This review will also discuss challenges in implementing AI in routine practice.

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 on Disorders of Consciousness After Acquired Brain Injury:Stepping Forward

Major advances have been made over the past few decades in identifying and managing disorders of consciousness(DOC)in patients with acquired brain injury(ABI),bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration.Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools,sophisticated neuroimaging,and electrophysiological techniques,a considerably higher diagnostic accuracy rate of DOC may now be reached.During the treatment of patients with DOC,a variety of intervention methods are available,including amantadine and transcranial direct current stimulation,which have both provided class II evidence,zolpidem,which is also of high quality,and non-invasive stimulation,which appears to be more encouraging than pharmacological therapy.However,heterogeneity is profoundly ingrained in study designs,and only rare schemes have been recommended by authoritative institutions.There is still a lack of an effective clinical protocol for managing patients with DOC following ABI.To advance future clinical studies on DOC,we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC.We propose a preliminary clinical decision protocol,which could serve as an ideal reference tool for many medical institutions.

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.

Diagnostic accuracy of the tongue blade test combined with clinical signs to detect maxillary and mandibular fractures in the emergency department

BACKGROUND: To evaluate the diagnostic accuracy of clinical signs combined with the tongue blade test(TBT) to detect maxillary and mandibular fractures.METHODS: A cross-sectional study enrolled patients with maxillary and mandibular injuries in the emergency department. Physical examination and the TBT were performed, followed by radiological imaging(facial X-ray or computed tomography [CT]). The diagnostic accuracy was calculated for individuals and a combination of clinical findings at predicting maxillary and mandibular fractures.RESULTS: A total of 98 patients were identified, of whom 31.6% had maxillary fractures and9.2% had mandibular fractures. The combination of malocclusion, tenderness on palpation and swelling with positive TBT had 100% specificity to detect maxillary and mandibular fractures. In the absence of malocclusion, the combination of tenderness on palpation and swelling with positive TBT produced a specificity of 97.8% for maxillary fracture and a specificity of 96.2% for mandibular fracture. A clinical decision tool consisting of malocclusion, tenderness on palpation, swelling and TBT revealed a specificity of 100% and a positive predictive value of 100%.CONCLUSION: The clinical decision tool is potentially useful to rule out mandibular fractures,thus preventing unnecessary radiation exposure.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Prognostic value of decision criteria for emergency liver transplantation in patients with wild mushroom induced acute liver injury

Background： The reported mortality rate of mushroom-induced acute liver failure with conventionaltreatment is 1.4%–16.9%. Emergency liver transplantation may be indicated and can be the only curativetreatment option. This study aimed to assess the prognostic value of criteria for emergency livertransplantation in predicting 28-day mortality in patients with mushroom-induced acute liver injury.Methods： A retrospective cohort study was performed between January 2005 and December 2015. Alladult patients aged≥18 years admitted with mushroom intoxication at our emergency department wereevaluated. All patients with acute liver injury, defined as elevation of serum liver enzymes （〉5 timesthe upper limit of normal, ULN） or moderate coagulopathy （INR 〉 2.0） were included. The ability of the King’s College, Ganzert’s, and Escudié’s criteria to predict 28-day mortality was evaluated.

Clinical diagnosis of severe COVID-19:A derivation and validation of a prediction rule

BACKGROUND The widespread coronavirus disease 2019(COVID-19)has led to high morbidity and mortality.Therefore,early risk identification of critically ill patients remains crucial.AIM To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit(ICU)care.METHODS This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19,2020,and March 14,2020 in Shenzhen Third People’s Hospital.Multivariate logistic regression was applied to develop the predictive model.The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020,by area under the receiver operating curve(AUROC),goodness-of-fit and the performance matrix including the sensitivity,specificity,and precision.A nomogram was also used to visualize the model.RESULTS Among the patients in the derivation and validation datasets,38 and 9 participants(10.5%and 2.54%,respectively)developed severe COVID-19,respectively.In univariate analysis,21 parameters such as age,sex(male),smoker,body mass index(BMI),time from onset to admission(>5 d),asthenia,dry cough,expectoration,shortness of breath,asthenia,and Rox index<18(pulse oxygen saturation,SpO2)/(FiO2×respiratory rate,RR)showed positive correlations with severe COVID-19.In multivariate logistic regression analysis,only six parameters including BMI[odds ratio(OR)3.939;95%confidence interval(CI):1.409-11.015;P=0.009],time from onset to admission(≥5 d)(OR 7.107;95%CI:1.449-34.849;P=0.016),fever(OR 6.794;95%CI:1.401-32.951;P=0.017),Charlson index(OR 2.917;95%CI:1.279-6.654;P=0.011),PaO2/FiO2 ratio(OR 17.570;95%CI:1.117-276.383;P=0.041),and neutrophil/lymphocyte ratio(OR 3.574;95%CI:1.048-12.191;P=0.042)were found to be independent predictors of COVID-19.These factors were found to be significant risk factors for severe patients confirmed with COVID-19.The AUROC was 0.941(95%CI:0.901-0.981)and 0.936(95%CI:0.886-0.987)in both datasets.The calibration properties were good.CONCLUSION The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU.It assisted the ICU clinicians in making timely decisions for the target population.

Towards automated calculation of evidence-based clinical scores

AIM To determine clinical scores important for automated calculation in the inpatient setting.METHODS A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories-"not important", "nice to have", or "very important". Surveys were solicited via specialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation importance.RESULTS Seventy-nine divided by one hundred and forty-four(54.9%) surveys were completed and 72/144(50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold(14 respondents each). For internists, 2/110(1.8%) of scores were "very important" and 73/110(66.4%) were "nice to have". For intensivists, no scores were "very important" and 26/76(34.2%) were "nice to have". Only the number of medical history(OR = 2.34; 95%CI: 1.26-4.67; P < 0.05) and vital sign(OR = 1.88; 95%CI: 1.03-3.68; P < 0.05) variables for clinical scores used by internists was predictive of desire for automation. CONCLUSION Few clinical scores were deemed "very important" for automated calculation. Future efforts towards score calculator automation should focus on technically feasible "nice to have" scores.

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 Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health data.Thus,early detection of any disease or derangement can aid doctors in saving patients’lives.However,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper classification.We propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health status.To begin with,various patient datasets were collected and trained into the system using IoT sensors.As a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result visibility.Additionally,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant information.Then,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription.We found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

To scan or not to scan:Use of transient elastography in an integrated health system

BACKGROUND Non-invasive tests,such as Fibrosis-4 index and transient elastography(com-monly FibroScan),are utilized in clinical pathways to risk stratify and diagnose non-alcoholic fatty liver disease(NAFLD).In 2018,a clinical decision support tool(CDST)was implemented to guide primary care providers(PCPs)on use of FibroScan for NAFLD.AIM To analyze how this CDST impacted health care utilization and patient outcomes.METHODS We performed a retrospective review of adults who had FibroScan for NAFLD indication from January 2015 to December 2017(pre-CDST)or January 2018 to December 2020(post-CDST).Outcomes included FibroScan result,laboratory tests,imaging studies,specialty referral,patient morbidity and mortality.RESULTS We identified 958 patients who had FibroScan,115 before and 843 after the CDST was implemented.The percentage of FibroScans ordered by PCPs increased from 33%to 67.1%.The percentage of patients diagnosed with early F1 fibrosis,on a scale from F0 to F4,increased from 7.8%to 14.2%.Those diagnosed with ad-vanced F4 fibrosis decreased from 28.7%to 16.5%.There were fewer laboratory tests,imaging studies and biopsy after the CDST was implemented.Though there were more specialty referrals placed after the CDST was implemented,multivariate analysis revealed that healthcare utilization aligned with fibrosis score,whereby patients with more advanced disease had more referrals.Very few patients were hospitalized or died.CONCLUSION This CDST empowered PCPs to diagnose and manage patients with NAFLD with appropriate allocation of care towards patients with more advanced disease.

Development of Medical Informatization in the Era of Big Data

The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.

A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation

Objective:To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease(CAD).Methods and Results:A total of 1,025 patients referred for coronary angiography were included in a retrospective,single-center study.Randomly,720 patients(70%)were selected as the development group and the other patients were selected as the validation group.Multivariate logistic regression analysis showed that the seven risk factors age,sex,systolic blood pressure,lipoprotein-associated phospholipase A 2,type of angina,hypertension,and diabetes were signifi cant for diagnosis of CAD,from which we established model A.We established model B with the risk factors age,sex,height,systolic blood pressure,low-density lipoprotein cholesterol,lipoprotein-associated phospholipase A 2,type of angina,hypertension,and diabetes via the Akaike information criterion.The risk factors from the original Framingham Risk Score were used for model C.From comparison of the areas under the receiver operating characteristic curve,net reclassifi cation improvement,and integrated discrimination improvement of models A,B,and C,we chose model B to develop the nomogram because of its fi tness in discrimination,calibration,and clinical effi ciency.The nomogram for diagnosis of CAD could be used easily and conveniently.Conclusion:An individualized clinical prediction model for patients with CAD allowed an accurate estimation in Chinese populations.The Akaike information criterion is a better method in screening risk factors.The net reclassifi-cation improvement and integrated discrimination improvement are better than the area under the receiver operating characteristic curve in discrimination.Decision curve analysis can be used to evaluate the effi ciency of clinical prediction models.

A Motivation Framework to Promote Knowledge Translation in Healthcare

Globally,there is a great gulf between medical knowledge and clinical practice.Translating knowledge into clinical decision support(CDS) application has become the biggest challenge faced by evidence based medicine.This paper proposed a comprehensive motivation framework to facilitate knowledge translation in healthcare.Based on a unified medical knowledge ontology and knowledge base,the framework provides an infrastructure of fundamental services,such as inference service and data acquisition,to support development of knowledge-driven CDS applications and integration into clinical workflow.The framework has been implemented in a 2600-bed Chinese hospital,and is able to reduce the time and cost of developing typical CDS applications.

Screening dementia and predicting high dementia risk groups using machine learning

New technologies such as artificial intelligence,the internet of things,big data,and cloud computing have changed the overall society and economy,and the medical field particularly has tried to combine traditional examination methods and new technologies.The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence.This review introduces:(1)the definition,main concepts,and classification of machine learning and overall distinction of it from traditional statistical analysis models;and(2)the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry.As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia,various machine learning algorithms such as boosting model,artificial neural network,and random forest were used for predicting dementia.The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.