Challenges related to clinical decision-making in hepatocellular carcinoma recurrence post-liver transplantation: Is there a hope?

Hepatocellular carcinoma(HCC)is a common liver malignancy and represents a serious cause of cancer-related mortality and morbidity.One of the favourable curative surgical therapeutic options for HCC is liver transplantation(LT)in selected patients fulfilling the known standard Milan/University of California San Francisco criteria which have shown better outcomes and longer-term survival.Despite careful adherence to the strict HCC selection criteria for LT in different transplant centres,the recurrence rate still occurs which could negatively affect HCC patients’survival.Hence HCC recurrence post-LT could predict patients’survival and prognosis,depending on the exact timing of recurrence after LT(early or late),and whether intra/extrahepatic HCC recurrence.Several factors may aid in such a complication,particularly tumour-related criteria including larger sizes,higher grades or poor tumour differentiation,microvascular invasion,and elevated serum alpha-fetoprotein.Therefore,managing such cases is challenging,different therapeutic options have been proposed,including curative surgical and ablative treatments that have shown better outcomes,compared to the palliative locoregional and systemic therapies,which may be helpful in those with unresectable tumour burden.To handle all these issues in our review.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical,genetic,environmental,and lifestyle factors to optimize medication management.This study investigates how artificial intelligence(AI)and machine learning(ML)can address key challenges in integrating pharmacogenomics(PGx)into psychiatric care.In this integration,AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions.AI-driven models integrating genomic,clinical,and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder.This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry,highlighting the importance of ethical considerations and the need for personalized treatment.Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care.Future research should focus on developing enhanced AI-driven predictive models,privacy-preserving data exchange,and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

Big Data for Precision Medicine

This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. Weexamine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.

Making Evidence-based Decisions in the Clinical Practice of Integrative Medicine

In 1948, the first clinical paper adopting the protocol of randomized and controlled design was published in British Medical Journal by Bradford Hill,a noted British biostatistician, who introduced rigorous theory of mathematical statistics into clinical design the first time and successfully evaluated the therapeutic effect of streptomycin on tuberculosis.

Endoscopic ultrasound-guided fine-needle aspiration for suspected malignancies adjacent to the gastrointestinal tract

AIM: To investigate the impact of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in association with a multidisciplinary team evaluation for the detection of gastrointestinal malignancies.

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.

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.

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.

医患共同决策在妇产科的应用与探讨

临床共同决策是医患双方共同参与,医务人员充分告知决策利弊,权衡风险,基于患者个人意愿、偏好及价值观念,发挥患者在医疗决策中的主观能动性,主动反映决策中自身的观点,最终达到医患双方决策的一致性。本文就临床共同决策在妇产科中的应用进行论述,明确临床共同决策在妇产科疾病中的应用价值。