Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.

Research and Discussion on Flipped Classroom Combined with Case-Based Learning in Pharmacoeconomics Teaching

Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selected as the study subjects,and the cost-effectiveness analysis of different dosage forms of Yinzhihuang in the treatment of neonatal jaundice was selected as the teaching case.The flipped classroom combined with case-based learning teaching method was used to carry out theoretical teaching to the students.After the course,questionnaires were distributed through the Sojump platform to evaluate the teaching effect.Results:The results of the questionnaire showed that 85.71%of the students believed that the flipped classroom combined with case-based learning teaching method was helpful in mobilizing the learning enthusiasm and initiative,and improving the comprehensive application ability of the knowledge of pharmacoeconomics.92.86%of the students think that it is conducive to the understanding and memorization of learning content,as well as the cultivation of teamwork,communication,etc.Conclusion:Flipped classroom combined with case-based learning teaching method can improve students’knowledge mastery,thinking skills,and practical application skills,as well as optimize and improve teachers’teaching levels.

Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma:a multicenter study

Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Engagement in clinical learning environment among nursing students: Role of nurse educators

Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This paper is significant in Arab world as there is a lack of knowledge, attitude and practice of student involvement in the new clinical learning environment. The purpose of this review article is to describe the experiences and perspectives of the nurse educator in facilitating pre-licensure nursing students’ engagement in the new clinical learning environment. The review suggests that novice students prefer actual engagement in clinical learning facilitated through diversity experiences, shared learning opportunities, student-faculty interaction and active learning. They expressed continuous supervision, ongoing feedback, interpersonal relationship and personal support from nurse educators useful in the clinical practice. However, the value of this review lies in a better understanding of what constitutes quality clinical learning environment from the students’ perspective of engagement in evidence-based nursing, reflective practice, e-learning and simulated case scenarios facilitated by the nurse educators. This review is valuable in planning and implementing innovative clinical and educational experiences for improving the quality of the clinical teaching-learning environment.

Practices of caring for nursing students: A clinical learning environment

Objective:The purpose of this study was to explore,describe and illuminate nursing students'best encounters of caring in the clinical learning environment.Caring for nursing students was emphasized and recommendations provided to enhance caring for nursing students within their clinical learning environment.Methods:Qualitative data was collected by the researcher using semi-structured individual interviews and an Appreciative Inquiry(AI)methodology.Ten second year nursing students undertaking the bridging course leading to registration as general nurses in terms of Regulation 683 of the South African Nursing Council(SANC)were purposively sampled from 3 private hospitals within the Western Cape.Data was analysed using Giorgi's method.Results:The main theme included the best and'least best'caring practices embedded in the centrality of the heart.The subthemes comprised of the nursing students'experiences of caring literacy and caring illiteracy.The second theme included the creation of best caring practices within a conducive clinical learning environment.Within this theme,the subthemes comprised of the caring attributes required in reflecting best caring practices,as well the creation of a clinical learning environment to optimise caring.Conclusions:The significance and necessity of caring for the nursing student were clearly illustrated and confirmed by participants.Caring was equated to the heart as the core to the nursing students'being.Recommendations for nursing education,management,practice and research were therefore specifically formulated to enhance caring towards nursing students.

Decoding degeneration:the implementation of machine learning for clinical detection of neurodegenerative disorders

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.

Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer

BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.

A Hybrid Deep Learning Model for COVID-19 Prediction and Current Status of Clinical Trials Worldwide

Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARSCoV-2(Severe Acute Respiratory Syndrome-Coronavirus-2),is a respiratory based touch contiguous disease.The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally.The whole world is making tremendous efforts to combat this life-threatening disease.For taking remedial action and planning preventive measures on time,there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO’s daily bulletin report as well as provides information regarding clinical trials across the world.To evaluate the suitability and performance of our proposed model compared to other well-established prediction models,an experimental study has been performed.To estimate the prediction results,the three performance measures,i.e.,Root Mean Square Error(RMSE),Coefficient of determination(R2 Score),and Mean Absolute Percentage Error(MAPE)have been employed.The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models.The proposed model archives the lowest RMSE,lowest MAPE,and highest R2 Score throughout the testing,under varied selection criteria(country-wise).This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe.

Learning and competence development via clinical cases-what elements should be investigated to best train good medical doctors?

In European higher education,application of information technology,concentration on the learning-processes,consistent implementation,transfer learning,case-based learning,autonomous learning has been extensively studied in the last decade.Educational sciences based on neuroscientific findings use brain-based learning and teaching,including integrated thematic instructions and emotion-theory.Elements essential to this strategy,such as theory and methods for learning,competencies,attitudes,social reality,and a metadiscourse are described herein.Research on learning tends to focus on declarative knowledge,associative learning with conditional stimuli,and procedural knowledge with polythematic/crosslinking thinking.Research on competencies:In research on competencies(e.g.,for clinical reasoning,decision-making),intuitive and analytical components are studied.As repeated presentation and exercising of clinical cases is crucial for an efficient learning process,the implementation of interactive scenarios including affectively involving didactics is considered.For competence-development observational methods,questionnaires/item sets or factors have to be targeted and empirically validated.Attitudes and social reality:Clinical decision-making,identification processes and attitudes(“Hidden curriculum”),as well as secondary socialization processes(integration of social norms,values,preparation of role-acquisition,occupational role)are studied via process research,conceptual research,and observational methods.With respect to social reality research,conscious and unconscious bargaining processes have to be taken into account.Methodology:Neuroscience-memory,neuronal,molecular biology,and computer science(Neurocircuits)are integrated into observational process research(e.g.,affective-cognitive interface,identification processes)and conceptual research is added and studied on the meta-level,including discussion of research paradigms.This discussion provides ongoing feedback to projects in a hermeneutic circle.

Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine

Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.

Active Learning Improves Nursing Student Clinical Performance in an Academic Institution in Macao

Objective： To assess the outcome of the application of active learning during practicum among nursing students using clinical assessment and evaluation scores as a measurement. Methods： Nursing students were instructed on the basics of active learning prior to the initiation of their clinical experience. The participants were divided into 5groups of nursing students （ n = 56） across three levels （years 2-4） in a public academic institute of a bachelor degree program in Macao. Final clinical evaluation was averaged and compared between groups with and without intervention. Results： These nursing students were given higher appraisals in verbal and written comments than previous students without interventian. The groups with the invention achieved higher clinical assessment and evaluation scores on average than comparable groups without the active learning intervention. One group of sophomore nursing students （year 2） did not receive as high of evaluations as the other groups, receiving an average score of above 80. Conclusions＂ Nursing students must engage in active learning to demonstrate that they are willing to gain knowledge of theory, nursing skills and communication skills during the clinical practicum.

Using preceptors to improve nursing students' clinical learning outcomes: A Malawian students' perspective

Objective: Clinical environment remains an important aspect in the development of student's compe-tence, confidence and fulfillment of their expected learning outcomes. Preceptors and clinical teachers play a significant role in preparing students to achieve their professional goals through continuous support and resource provision. This study explored the students' perception of using clinical preceptors to improve their clinical learning outcomes.Methods: The researchers used semi structured interviews to collect qualitative data. A total of 48 stu-dents were purposively and conveniently sampled from 8 hospitals to participate in the study. Content analysis was used to analyse the data. Findings: Two themes emerged from the study findings namely: creating a positive clinical learning environment and facilitating academic clinical collaboration. Conclusions: The findings of the study revealed that students gained more confidence and competence in clinical practice when supported by preceptors as well as achieved clinical learning outcomes. These findings suggest the need to train more clinical preceptors for improvement of students' clinical learning outcomes.

Integration of Theory and Practice in Medical Morphology Curriculum in Postgraduate Training:A Flipped Classroom and Case-based Learning Exercise

Objective:The integration of training in theory and practice across the medical education spectrum is being encouraged to increase student understanding and skills in the sciences.This study aimed to determine the deciding factors that drive students'perceived advantages in class to improve precision education and the teaching model.Methods:A mixed strategy of an existing flipped classroom(FC)and a case-based learning(CBL)model was conducted in a medical morphology curriculum for 575 postgraduate students.The subjective learning evaluation of the individuals(learning time,engagement,study interest and concentration,and professional integration)was collected and analyzed after FC-CBL model learning.Results:The results from the general evaluation showed promising results of the medical morphology in the FC-CBL model.Students felt more engaged by instructors in person and benefited in terms of time-saving,flexible arrangements,and professional improvement.Our study contributed to the FC-CBL model in Research Design in postgraduate training in 4 categories:1)advancing a guideline of precision teaching according to individual characteristics;2)revealing whether a learning background is needed for a Research Design course to guide setting up a preliminary course;3)understanding the perceived advantages and their interfaces;and 4)barriers and/or improvement to implement the FC-CBL model in the Research Design class,such as a richer description of e-learning and hands-on practice.Conclusion:Undertaking a FC-CBL combined model could be a useful addition to pedagogy for medical morphology learning in postgraduate training.

Nursing Students’Clinical Learning Environment in Norwegian Nursing Homes:Lack of Innovative Teaching and Learning Strategies

Background: Nursing students hesitate to choose aged care as a career, and the aged care sectors are on an edge regarding nursing positions. Clinical learning environments may influence nursing students’ career choices. Few studies have explored learning environments in nursing homes, although students increasingly have placements there. Objectives: The aim was to produce information for developing nursing students’ learning opportunities in nursing homes. Design: A cross-sectional survey design was used. Settings: The study was conducted at a university college in southeast Norway. Participants: Students in two cohorts of a bachelor degree program in nursing participated, N = 499. Methods: Data were collected on nursing students’ perceptions of clinical learning environments during placements in five nursing homes. A 42-item validated questionnaire with the subscales personalization, individualization, involvement, task orientation, innovation, and satisfaction was used. Data analysis used descriptive statistics, t-tests, and linear regression analysis. Results: Total scores showed overall satisfaction with clinical learning environments in nursing homes. However, innovation subscale scores were very low. First year students had significantly higher scores than third year students on the total scale, and most subscales. Age was significantly associated with total scale scores and subscale satisfaction scores. Higher education and work experience before entering nursing education were significantly associated with involvement subscale scores. Conclusions: Students are more positive than negative about their clinical learning environments. Low valuation of innovation seems to be a consistent finding in studies in both nursing homes and hospitals internationally. For innovative learning strategies to function they must be anchored at the organizational level. Future research should develop and test more innovative learning strategies for nursing students.

Hydraulic Circuit Design and Dynamic Learning Using Case-based Reasoning

This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.

From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment.

Intensive care unit-acquired weakness:Unveiling significant risk factors and preemptive strategies through machine learning

This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.

Is there an optimal environment in which to learn clinical anatomy? One man’s view

Learning anatomy is essential in medical schools, and even more so for budding surgeons. Much has been discussed regarding the updated curriculum, and whether the pedagogies should be based upon cadaveric, and/or multimedia, or hybrid approaches. Much debate has also been centered on who is best qualified to teach. While all these are important, the setting is also critically important for the medical students and surgical trainees. Therefore the overarching issue is whether all these activities should be held in the dissection room, the operating theatre or the classical “Theatrum Anatomicum” ? What are the key experiential learning differences in picking up anatomical knowledge in the various venues listed above? This paper will provide some insights for teachers and students of human anatomy, and some ideas for the future planners and developers of anatomy learning centers internationally.

Forecasting patient demand at urgent care clinics using explainable machine learning

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient flows.The delays arising from inadequate staffing levels during these periods have been linked with adverse clinical outcomes.Previous research into forecasting patient flows has mostly used statistical techniques.These studies have also predominately focussed on short‐term forecasts,which have limited practicality for the resourcing of medical personnel.This study joins an emerging body of work which seeks to explore the potential of machine learning algorithms to generate accurate forecasts of patient presentations.Our research uses datasets covering 10 years from two large urgent care clinics to develop long‐term patient flow forecasts up to one quarter ahead using a range of state‐of‐the‐art algo-rithms.A distinctive feature of this study is the use of eXplainable Artificial Intelligence(XAI)tools like Shapely and LIME that enable an in‐depth analysis of the behaviour of the models,which would otherwise be uninterpretable.These analysis tools enabled us to explore the ability of the models to adapt to the volatility in patient demand during the COVID‐19 pandemic lockdowns and to identify the most impactful variables,resulting in valuable insights into their performance.The results showed that a novel combination of advanced univariate models like Prophet as well as gradient boosting,into an ensemble,delivered the most accurate and consistent solutions on average.This approach generated improvements in the range of 16%-30%over the existing in‐house methods for esti-mating the daily patient flows 90 days ahead.