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Artikel: A hybrid feature selection algorithm to determine effective factors in predictive model of success rate for in vitro fertilization/intracytoplasmic sperm injection treatment: A cross-sectional study.

Mehrjerd, Ameneh / Rezaei, Hassan / Eslami, Saeid / Khadem Ghaebi, Nayyere

International journal of reproductive biomedicine

2024 Band 21, Heft 12, Seite(n) 995–1012

Abstract: Background: Previous research has identified key factors affecting in vitro fertilization or intracytoplasmic sperm injection success, yet the lack of a standardized approach for various treatments remains a challenge.: Objective: The objective of ... ...

Abstract	Background: Previous research has identified key factors affecting in vitro fertilization or intracytoplasmic sperm injection success, yet the lack of a standardized approach for various treatments remains a challenge. Objective: The objective of this study is to utilize a machine learning approach to identify the principal predictors of success in in vitro fertilization and intracytoplasmic sperm injection treatments. Materials and methods: We collected data from 734 individuals at 2 infertility centers in Mashhad, Iran between November 2016 and March 2017. We employed feature selection methods to reduce dimensionality in a random forest model, guided by hesitant fuzzy sets (HFSs). A hybrid approach enhanced predictor identification and accuracy (ACC), as assessed using machine learning metrics such as Matthew's correlation coefficient, runtime, ACC, area under the receiver operating characteristic curve, precision or positive predictive value, recall, and F-Score, demonstrating the effectiveness of combining feature selection methods. Results: Our hybrid feature selection method excelled with the highest ACC (0.795), area under the receiver operating characteristic curve (0.72), and F-Score (0.8), while selecting only 7 features. These included follicle-stimulation hormone (FSH), 16Cells, FAge, oocytes, quality of transferred embryos (GIII), compact, and unsuccessful. Conclusion: We introduced HFSs in our novel method to select influential features for predicting infertility success rates. Using a multi-center dataset, HFSs improved feature selection by reducing the number of features based on standard deviation among criteria. Results showed significant differences between pregnant and non-pregnant groups for selected features, including FSH, FAge, 16Cells, oocytes, GIII, and compact. We also found a significant correlation between FAge and fetal heart rate and clinical pregnancy rate, with the highest FSH level (31.87%) observed for doses ranging from 10-13 (mIU/ml).
Sprache	Englisch
Erscheinungsdatum	2024-01-25
Erscheinungsland	Iran
Dokumenttyp	Journal Article
ZDB-ID	2898387-7
ISSN	2476-3772 ; 2476-4108
ISSN (online)	2476-3772
ISSN	2476-4108
DOI	10.18502/ijrm.v21i12.15038
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Early prediction of medical students' performance in high-stakes examinations using machine learning approaches.

Mastour, Haniye / Dehghani, Toktam / Moradi, Ehsan / Eslami, Saeid

Heliyon

2023 Band 9, Heft 7, Seite(n) e18248

Abstract: Introduction: Since the advent of medical education systems, managing high-stakes exams has been a top priority and challenge for all policymakers. However, considering machine learning (ML) techniques as a replacement for medical licensing examinations, ...

Abstract	Introduction: Since the advent of medical education systems, managing high-stakes exams has been a top priority and challenge for all policymakers. However, considering machine learning (ML) techniques as a replacement for medical licensing examinations, particularly during crises such as the COVID-19 outbreak, could be an effective solution. This study uses ML models to develop a framework for predicting medical students' performance on high-stakes exams, such as the Comprehensive Medical Basic Sciences Examination (CMBSE). Material and methods: Prediction of students' status and score on high-stakes examinations faces several challenges, including an imbalanced number of failing and passing students, a large number of heterogeneous and complex features, and the need to identify at-risk and top-performing students. In this study, two major categories of ML approaches are compared: first, classic models (logistic regression (LR), support vector machine (SVM), and k-nearest neighbors (KNN)), and second, ensemble models (voting, bagging (BG), random forests (RF), adaptive boosting (ADA), extreme gradient boosting (XGB), and stacking). Results: To evaluate the models' discrimination ability, they are assessed using a real dataset containing information on medical students over a five-year period (n = 1005). The findings indicate that ensemble ML models demonstrate optimal performance in predicting CMBSE status (RF and stacking). Similarly, among the classic regressors, LR exhibited the highest root-mean-square deviation (RMSD) (0.134) and coefficient of determination (R2) (0.62), whereas the RF model had the highest RMSD (0.077) and R2 (0.80) overall. Furthermore, Anatomical Sciences, Biochemistry, Parasitology, and Entomology grade point average (GPA) and grades demonstrated the strongest positive correlation with the outcomes. Conclusion: Comparing classic and ensemble ML models revealed that ensemble models are superior to classic models. Therefore, the presented framework could be considered a suitable alternative for the CMBSE and other comparable medical licensing examinations.
Sprache	Englisch
Erscheinungsdatum	2023-07-13
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e18248
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Inflammatory bowel disease patients' perspectives of non-medical needs.

Norouzkhani, Narges / Faramarzi, Mahbobeh / Bahari, Ali / Shirvani, Javad Shokri / Eslami, Saeid / Tabesh, Hamed

BMC gastroenterology

2024 Band 24, Heft 1, Seite(n) 134

Abstract: Background: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources ... ...

Abstract	Background: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Methods: IBD patients were recruited from different centers. Considering inclusion and exclusion criteria, 521 participants were filled a predefined questionnaire. This questionnaire was prepared through literature review of the recent well-known guidelines on the needs of IBD patients, which was further approved by the experts of IBD area in three rounds of Delphi consensus. It includes 56 items in four sections of informational needs (25), supportive needs (15), sources of information (7), and methods of obtaining information (9). Results: In particular, EFA was used to apply data reduction and structure detection. Given that this study tries to identify patterns, structures as well as inter-relationships and classification of the variables, EFA was utilized to simplify presentation of the variables in a way that large amounts of observations transform into fewer ones. Accordingly, the EFA identified five factors out of 25 items in the information needs section, three factors out of 15 items in the supportive needs section, two factors out of 7 items in the information sources section, and two factors out of 9 items in the information presentation methods. Through the CFA, all 4 models were supported by Root Mean Squared Error of Approximation (RMSEA); Incremental Fit Index (IFI); Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); and SRMR. These values were within acceptable ranges, indicating that the twelve factors achieved from EFA were validated. Conclusions: This study introduced a reliable 12-factor model as an efficient tool to comprehensively identify preferences of IBD patients in informational and supportive needs along with sources and methods of obtaining information. An in-depth understanding of the needs of IBD patients facilitates informing and supporting health service provision. It also assists patients in a fundamental way to improve adaptation and increase the quality of life. We suggest that health care providers consider the use of this tool in clinical settings in order to precisely assess its efficacy.
Mesh-Begriff(e)	Humans ; Quality of Life ; Factor Analysis, Statistical ; Health Personnel ; Inflammatory Bowel Diseases
Sprache	Englisch
Erscheinungsdatum	2024-04-13
Erscheinungsland	England
Dokumenttyp	Review ; Journal Article
ZDB-ID	2041351-8
ISSN	1471-230X ; 1471-230X
ISSN (online)	1471-230X
ISSN	1471-230X
DOI	10.1186/s12876-024-03214-x
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

Rahmatinejad, Zahra / Dehghani, Toktam / Hoseini, Benyamin / Rahmatinejad, Fatemeh / Lotfata, Aynaz / Reihani, Hamidreza / Eslami, Saeid

Scientific reports

2024 Band 14, Heft 1, Seite(n) 3406

Abstract: This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based ... ...

Abstract	This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.
Mesh-Begriff(e)	Adult ; Humans ; Logistic Models ; Hospital Mortality ; Cross-Sectional Studies ; Emergency Service, Hospital ; Machine Learning ; Retrospective Studies
Sprache	Englisch
Erscheinungsdatum	2024-02-10
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-54038-4
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes.

Kazemi, Azar / Rasouli-Saravani, Ashkan / Gharib, Masoumeh / Albuquerque, Tomé / Eslami, Saeid / Schüffler, Peter J

Computers in biology and medicine

2024 Band 173, Seite(n) 108306

Abstract: The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for ... ...

Abstract	The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.
Mesh-Begriff(e)	Humans ; Lymphocytes, Tumor-Infiltrating/pathology ; Reproducibility of Results ; Colorectal Neoplasms/diagnosis ; Colorectal Neoplasms/pathology ; Tumor Microenvironment
Sprache	Englisch
Erscheinungsdatum	2024-03-13
Erscheinungsland	United States
Dokumenttyp	Systematic Review ; Journal Article ; Review
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2024.108306
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Development and Validation of an Educational Book on Self-Management in Inflammatory Bowel Disease Based on Patient Preferences and Expert Opinions: A Methodological Study.

Norouzkhani, Narges / Bahari, Ali / Faramarzi, Mahbobeh / Shokri Shirvani, Javad / Eslami, Saeid / Tabesh, Hamed

Journal of clinical medicine

2023 Band 12, Heft 24

Abstract: Background: Self-management education resources for inflammatory bowel disease (IBD) using concepts remain infrequent. We aim to describe the development and evaluation process of educational material for self-management in IBD based on patient ... ...

Abstract	Background: Self-management education resources for inflammatory bowel disease (IBD) using concepts remain infrequent. We aim to describe the development and evaluation process of educational material for self-management in IBD based on patient preferences and expert opinions. Research design and methods: The method of this study includes two main phases of development and validation in five steps in the following order: (1) identification of information needs for patients with IBD; (2) content development with a comprehensive literature review and scientific texts related to IBD; (3) measuring the face validity of the content based on the expert opinions in the field of IBD; (4) validation of the content with the experts in the field of IBD; and (5) validation by target audiences. Results: The expert panel comprises ten gastroenterologists, nutritionists, psychologists, gynecologists, and nurses. The total suitability score is 79.5%. The final draft version of the educational self-management material was presented to 30 IBD patients who were satisfied (n = 24; 80%) with the material. Conclusions: This study shows the development process and is validated for face and content validity by the academic multidisciplinary expert panel and target group. Patients and their caregivers can use this content to cope with their disease.
Sprache	Englisch
Erscheinungsdatum	2023-12-13
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm12247659
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles.

Hoseini, Benyamin / Jaafari, Mahmoud Reza / Golabpour, Amin / Momtazi-Borojeni, Amir Abbas / Karimi, Maryam / Eslami, Saeid

Scientific reports

2023 Band 13, Heft 1, Seite(n) 18012

Abstract: Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing ... ...

Abstract	Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.
Mesh-Begriff(e)	Liposomes ; Drug Delivery Systems ; Nanoparticles ; Particle Size
Chemische Substanzen	Liposomes
Sprache	Englisch
Erscheinungsdatum	2023-10-21
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-43689-4
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Assessing the quality of electronic medical records in academic hospitals: A multi-center study in Iran.

Zabolinezhad, Hedieh / Eslami, Saeid / Hassibian, Mohammad Reza / Dorri, Sara

Frontiers in digital health

2022 Band 4, Seite(n) 856010

Abstract: Objective: The present study aimed to assess the quality of electronic medical records (EMR) retrieved from hospital information systems (HIS) of three educational hospitals in Mashhad, Iran.: Methods: In this multi-center, cross-sectional study, ... ...

Abstract	Objective: The present study aimed to assess the quality of electronic medical records (EMR) retrieved from hospital information systems (HIS) of three educational hospitals in Mashhad, Iran. Methods: In this multi-center, cross-sectional study, inpatient electronic records collected from three academic hospitals were categorized into five data groups, namely demographics (D); care handler (CH), indicating the doers of the medical actions; diagnosis and treatment (DT); administrative and financial (AF); and laboratory and Para clinic (LP). Next, we asked 25 physicians from the three academic hospitals to determine data elements of medical research and education value (called research and educational data) in every group. Flowingly, the quality of the five data groups (completeness * accuracy) was reported for entire sampled data and those specified as research and educational data, based on the exact concordance between electronic medical records and corresponding paper records. HISRA, standing for HIS recording ability, was also assessed compared to data elements of standard paper forms. Results: For entire data, HISRA was 58.5%. In all hospitals, the highest data quality (more than 90%) belongs to D and AF data groups, and the lowest quality goes to CH and DT groups (less than 50%, and 60%, respectively). For research and educational data, HISRA was 47%, and the quality of D and AF data groups were the highest (nearly 100%), while CH and DT stood around 50% and 60% in order. The quality of the LP data group was almost 85% in all hospitals but hospital C (well over 30%). Total data quality for the hospitals was almost less than 70%. Conclusions: The low quality of electronic medical records was mostly a result of incompleteness, while the accuracy was relatively good. Results showed that the HIS application development mainly focused on administrative and financial aspects rather than academic and clinical goals.
Sprache	Englisch
Erscheinungsdatum	2022-11-23
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2022.856010
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Telemedicine Based on Human Activity Recognition in Elderly Healthcare.

Ghouchan Nezhad Noor Nia, Raheleh / Arzehgar, Afrooz / Dehdeleh, Vajiheh / Eslami, Saeid

Studies in health technology and informatics

2023 Band 302, Seite(n) 987–991

Abstract: Nowadays, telemedicine can provide remote clinical services for the elderly, using smart devices like embedded sensors, via real-time communication with the healthcare provider. In particular, inertial measurement sensors such as accelerometers embedded ... ...

Abstract	Nowadays, telemedicine can provide remote clinical services for the elderly, using smart devices like embedded sensors, via real-time communication with the healthcare provider. In particular, inertial measurement sensors such as accelerometers embedded in smartphones can provide sensory data fusion for human activities. Thus, the technology of Human Activity Recognition can be applied to handle such data. In recent studies, the three-dimensional axis has been used to detect human activities. Since most changes in individual activities occur in the x- and y-axis, the label of each activity is determined using a new two-dimensional Hidden Markov Mode based on these two axes. To evaluate the proposed method, we use the WISDM dataset which is based on an accelerometer. The proposed strategy is compared to General Model and User-Adaptive Model. The results indicate that the proposed model is more accurate than the others.
Mesh-Begriff(e)	Humans ; Aged ; Human Activities ; Telemedicine/methods ; Smartphone ; Health Facilities
Sprache	Englisch
Erscheinungsdatum	2023-05-19
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI230323
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Prediction of medical sciences students' performance on high-stakes examinations using machine learning models: a protocol for a systematic review.

Mastour, Haniye / Dehghani, Toktam / Jajroudi, Mahdie / Moradi, Ehsan / Zarei, Mitra / Eslami, Saeid

BMJ open

2023 Band 13, Heft 5, Seite(n) e064956

Abstract: Introduction: Predicting medical science students' performance on high-stakes examinations has received considerable attention. Machine learning (ML) models are well-known approaches to enhance the accuracy of determining the students' performance. ... ...

Abstract	Introduction: Predicting medical science students' performance on high-stakes examinations has received considerable attention. Machine learning (ML) models are well-known approaches to enhance the accuracy of determining the students' performance. Accordingly, we aim to provide a comprehensive framework and systematic review protocol for applying ML in predicting medical science students' performance on high-stakes examinations. Improving the current understanding of the input and output features, preprocessing methods, setting of ML models and required evaluation metrics seems essential. Methods and analysis: A systematic review will be conducted by searching the electronic bibliographic databases of MEDLINE/PubMed, EMBASE, SCOPUS and Web of Science. The search will be limited to studies published from January 2013 to June 2023. Studies explicitly predicting student performance in high-stakes examinations and referencing their learning outcomes and use of ML models will be included. Two team members will first screen literature meeting the inclusion criteria at the title, abstract and full-text levels. Second, the Best Evidence Medical Education quality framework rates the included literature. Later, two team members will extract data, including the studies' general data and the ML approach's details. Finally, the information consensus will be reached and submitted for analysis. The synthesised evidence from this review provides helpful information for medical education policy-makers, stakeholders and other researchers in adopting the ML models to evaluate medical science students' performance in high-stakes exams. Ethics and dissemination: This systematic review protocol summarises findings of existing publications rather than primary data and does not require an ethics review. The results will be disseminated in publications of peer-reviewed journals.
Mesh-Begriff(e)	Humans ; Students, Medical ; Education, Medical/methods ; Medicine ; Publications ; Systematic Reviews as Topic
Sprache	Englisch
Erscheinungsdatum	2023-05-04
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2022-064956
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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