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  1. Fulltext "Free Hand" or Wire Guide: To the Editor
    Sinha NK, Bhardwaj A
    Clin Orthop Surg, 2019 12;11(4):495.
    PMID: 31788175 DOI: 10.4055/cios.2019.11.4.495
    Matched MeSH terms: Learning Curve
  2. "It kinda helped us to be there": students' perspectives on the use of virtual patient software in psychiatry posting
    Woon LS, Mohd Daud TI, Tong SF
    BMC Med Educ, 2023 Nov 09;23(1):851.
    PMID: 37946151 DOI: 10.1186/s12909-023-04834-9
    BACKGROUND: At the Faculty of Medicine of the National University of Malaysia, a virtual patient software program, DxR Clinician, was utilised for the teaching of neurocognitive disorder topics during the psychiatry posting of undergraduate medical students in a modified team-based learning (TBL) module. This study aimed to explore medical students' learning experiences with virtual patient.

    METHODS: Ten students who previously underwent the learning module were recruited through purposive sampling. The inclusion criteria were: (a) Fourth-year medical students; and (b) Completed psychiatry posting with the new module. Students who dropped out or were unable to participate in data collection were excluded. Two online focus group discussions (FGDs) with five participants each were conducted by an independent facilitator, guided by a questioning route. The data were transcribed verbatim and coded using the thematic analysis approach to identify themes.

    RESULTS: Three main themes of their learning experience were identified: (1) fulfilment of the desired pedagogy (2), realism of the clinical case, and (3) ease of use related to technical settings. The pedagogy theme was further divided into the following subthemes: level of entry for students, flexibility of presentation of content, provision of learning guidance, collaboration with peers, provision of feedback, and assessment of performance. The realism theme had two subthemes: how much the virtual patient experience mimicked an actual patient and how much the case scenario reflected real conditions in the Malaysian context. The technical setting theme entailed two subthemes: access to the software and appearance of the user interface. The study findings are considered in the light of learning formats, pedagogical and learning theories, and technological frameworks.

    CONCLUSIONS: The findings shed light on both positive and negative aspects of using virtual patients for medical students' psychiatry posting, which opens room for further improvement of their usage in undergraduate psychiatry education.

    Matched MeSH terms: Learning
  3. "Tell me what is 'better'!" How medical students experience feedback, through the lens of self-regulatory learning
    Spooner M, Larkin J, Liew SC, Jaafar MH, McConkey S, Pawlikowska T
    BMC Med Educ, 2023 Nov 22;23(1):895.
    PMID: 37993832 DOI: 10.1186/s12909-023-04842-9
    INTRODUCTION: While feedback aims to support learning, students frequently struggle to use it. In studying feedback responses there is a gap in explaining them in relation to learning theory. This study explores how feedback experiences influence medical students' self-regulation of learning.

    METHODS: Final-year medical students across three campuses (Ireland, Bahrain and Malaysia) were invited to share experiences of feedback in individual semi-structured interviews. The data were thematically analysed and explored through the lens of self-regulatory learning theory (SRL).

    RESULTS: Feedback interacts with learners' knowledge and beliefs about themselves and about learning. They use feedback to change both their cognitive and behavioural learning strategies, but how they choose which feedback to implement is complex. They struggle to generate learning strategies and expect teachers to make sense of the "how" in addition to the "what"" in planning future learning. Even when not actioned, learners spend time with feedback and it influences future learning.

    CONCLUSION: By exploring our findings through the lens of self-regulation learning, we advance conceptual understanding of feedback responses. Learners' ability to generate "next steps" may be overestimated. When feedback causes negative emotions, energy is diverted from learning to processing distress. Perceived non-implementation of feedback should not be confused with ignoring it; feedback that is not actioned often impacts learning.

    Matched MeSH terms: Learning
  4. Fulltext "VisionTouch Phone" for the Blind
    Yong R
    Malays J Med Sci, 2013 Oct;20(5):1-4.
    PMID: 24643391
    Our objective is to enable the blind to use smartphones with touchscreens to make calls and to send text messages (sms) with ease, speed, and accuracy. We believe that with our proposed platform, which enables the blind to locate the position of the keypads, new games and education, and safety applications will be increasingly developed for the blind. This innovative idea can also be implemented on tablets for the blind, allowing them to use information websites such as Wikipedia and newspaper portals.
    Matched MeSH terms: Learning
  5. 'We work together as a group': implications of jigsaw cooperative learning
    Jeppu AK, Kumar KA, Sethi A
    BMC Med Educ, 2023 Oct 06;23(1):734.
    PMID: 37803418 DOI: 10.1186/s12909-023-04734-y
    BACKGROUND: Modern clinical practice increasingly relies on collaborative, cooperative and team-based approaches for effective patient care. Recently, Jigsaw cooperative learning has gained attention in medical education. There is a need for studies in Southeast Asian context to establish its effectives in developing various core competencies expected of health professionals such as interpersonal, communication, collaborative, and teamwork skills. This current study explores the impact of using Jigsaw Cooperative Learning on undergraduate medical students.

    METHOD: An explanatory mixed method research design was carried out on first year medical students at a private university in Malaysia. In Phase I, a survey was conducted to explore the effectiveness of jigsaw learning. Descriptive and inferential statistics were calculated using SPSS. In Phase II, a focus group interview was conducted to explore their in-depth experiences. Qualitative data were thematically analysed.

    RESULTS: Fifty-seven students participated in the survey and seven students took part in the focus group interview. Quantitative data analysis showed a statistically significant improvement in the student's individual accountability, promotive interaction, positive interdependence, interpersonal skill, communication skill, teamwork skill, critical thinking and consensus building after jigsaw learning sessions. Qualitative data explained their experiences in-depth.

    CONCLUSION: Jigsaw cooperative learning improves collaboration, communication, cooperation and critical thinking among the undergraduate medical students. Educators should use jigsaw learning methods to encourage effective collaboration and team working. Future studies should explore the effectiveness of the jigsaw cooperative learning technique in promoting interprofessional collaboration in the workplace.

    Matched MeSH terms: Learning
  6. Fulltext 21st century medical education in University of Malaya
    Tan, Christina Phoay Lay, Blitz, Julia J.
    JUMMEC, 2008;11(1):1-2.
    MyJurnal
    What does this term medical education conjure up? Does it refer to the teaching and learning of medicine and therefore relates to students and the curriculum? Does it refer to the process of teaching and therefore relates to teachers? Perhaps it is both, since teaching and learning go hand in hand.(Copied from article).
    Matched MeSH terms: Learning
  7. Fulltext 22q11.2 deletion syndrome in diverse populations
    Kruszka P, Addissie YA, McGinn DE, Porras AR, Biggs E, Share M, et al.
    Am J Med Genet A, 2017 Apr;173(4):879-888.
    PMID: 28328118 DOI: 10.1002/ajmg.a.38199
    22q11.2 deletion syndrome (22q11.2 DS) is the most common microdeletion syndrome and is underdiagnosed in diverse populations. This syndrome has a variable phenotype and affects multiple systems, making early recognition imperative. In this study, individuals from diverse populations with 22q11.2 DS were evaluated clinically and by facial analysis technology. Clinical information from 106 individuals and images from 101 were collected from individuals with 22q11.2 DS from 11 countries; average age was 11.7 and 47% were male. Individuals were grouped into categories of African descent (African), Asian, and Latin American. We found that the phenotype of 22q11.2 DS varied across population groups. Only two findings, congenital heart disease and learning problems, were found in greater than 50% of participants. When comparing the clinical features of 22q11.2 DS in each population, the proportion of individuals within each clinical category was statistically different except for learning problems and ear anomalies (P 
    Matched MeSH terms: Learning Disorders/diagnosis*; Learning Disorders/ethnology; Learning Disorders/genetics; Learning Disorders/physiopathology
  8. Fulltext 3D texture-based face recognition system using fine-tuned deep residual networks
    Zheng S, Rahmat RWO, Khalid F, Nasharuddin NA
    PeerJ Comput Sci, 2019;5:e236.
    PMID: 33816889 DOI: 10.7717/peerj-cs.236
    As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.
    Matched MeSH terms: Machine Learning
  9. A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression
    Mumtaz W, Malik AS
    Brain Topogr, 2018 09;31(5):875-885.
    PMID: 29860588 DOI: 10.1007/s10548-018-0651-x
    The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.
    Matched MeSH terms: Machine Learning
  10. Fulltext A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic
    Rahman MM, Khatun F, Uzzaman A, Sami SI, Bhuiyan MA, Kiong TS
    Int J Health Serv, 2021 10;51(4):446-461.
    PMID: 33999732 DOI: 10.1177/00207314211017469
    The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.
    Matched MeSH terms: Machine Learning
  11. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care
    Alanazi HO, Abdullah AH, Qureshi KN
    J Med Syst, 2017 Apr;41(4):69.
    PMID: 28285459 DOI: 10.1007/s10916-017-0715-6
    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.
    Matched MeSH terms: Machine Learning
  12. Fulltext A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention
    Majeed MA, Shafri HZM, Zulkafli Z, Wayayok A
    Int J Environ Res Public Health, 2023 Feb 25;20(5).
    PMID: 36901139 DOI: 10.3390/ijerph20054130
    This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.
    Matched MeSH terms: Machine Learning
  13. Fulltext A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression
    Chughtai JU, Haq IU, Islam SU, Gani A
    Sensors (Basel), 2022 Dec 12;22(24).
    PMID: 36560104 DOI: 10.3390/s22249735
    Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
    Matched MeSH terms: Machine Learning*
  14. Fulltext A Hybridised Intelligent Technique for the diagnosis of medical diseases
    Abdu Masanawa Sagir, Saratha Sathasivam
    Pertanika Journal of Science & Technology, 2017;25(2):425-440.
    MyJurnal
    Medical diagnosis is the process of determining which disease or medical condition explains a person’s determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient’s characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine’s (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior
    performance when compared to some other related existing methods.
    Matched MeSH terms: Machine Learning
  15. Fulltext A Malaysian version of learning space preferences: a validation study
    Sim JH, Foong CC, Pallath V, Hong WH, Vadivelu J
    Int J Med Educ, 2021 May 27;12:86-93.
    PMID: 34049286 DOI: 10.5116/ijme.6082.7c41
    Objectives: This study aimed to validate a Malaysian version of a revised learning space questionnaire, as well as to test the utility of the revised questionnaire as a tool to investigate learning space preferences in a Malaysian medical school.

    Methods:   This is a cross-sectional survey. A convenient sample of 310 preclinical students of a public medical school in Malaysia were invited to participate. Validation data were collected using a revised 40-item, 5-point Likert scale learning space questionnaire.  The questionnaires were administered online via a student e-learning platform.  Data analysis was conducted using IBM SPSS version 24.  Exploratory factor analysis was conducted to examine the factor structure of the revised questionnaire to provide evidence for construct validity.  To assess the internal consistency of the revised questionnaire, Cronbach's alpha coefficients (α) were computed across all the items as well as for items within each of the factor.

    Results: A total of 223 (71.94%) preclinical students completed and returned the questionnaire. In the final analysis, exploratory factor analysis with principal axis factoring and an oblimin rotation identified a six-factor, 20-item factor solution. Reliability analysis reported good internal consistency for the revised questionnaire, with an overall Cronbach's alpha of 0.845, and Cronbach's alpha ranging from 0.800 to 0.925 for the six factors.

    Conclusions:   This study established evidence for the construct validity and internal consistency of the revised questionnaire.  The revised questionnaire appears to have utility as an instrument to investigate learning space preferences in Malaysian medical schools.

    Matched MeSH terms: Learning*
  16. Fulltext A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things
    Khan ZA, Naz S, Khan R, Teo J, Ghani A, Almaiah MA
    Comput Intell Neurosci, 2022;2022:5112375.
    PMID: 35449734 DOI: 10.1155/2022/5112375
    Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.
    Matched MeSH terms: Machine Learning
  17. Fulltext A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data
    Hussain S, Mustafa MW, Al-Shqeerat KHA, Saeed F, Al-Rimy BAS
    Sensors (Basel), 2021 Dec 17;21(24).
    PMID: 34960516 DOI: 10.3390/s21248423
    This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.
    Matched MeSH terms: Machine Learning*
  18. A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images
    Letchumanan N, Wong JHD, Tan LK, Ab Mumin N, Ng WL, Chan WY, et al.
    J Digit Imaging, 2023 Aug;36(4):1533-1540.
    PMID: 37253893 DOI: 10.1007/s10278-022-00753-1
    This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision.
    Matched MeSH terms: Machine Learning
  19. A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder
    Rahman MM, Usman OL, Muniyandi RC, Sahran S, Mohamed S, Razak RA
    Brain Sci, 2020 Dec 07;10(12).
    PMID: 33297436 DOI: 10.3390/brainsci10120949
    Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
    Matched MeSH terms: Machine Learning
  20. A Scoping Review of Effective Teaching Strategies in Surface Anatomy
    Abu Bakar YI, Hassan A, Yusoff MSB, Kasim F, Abdul Manan Sulong H, Hadie SNH
    Anat Sci Educ, 2021 Mar 01.
    PMID: 33650315 DOI: 10.1002/ase.2067
    To become skilled physicians, medical students must master surface anatomy. However, the study of surface anatomy is less emphasized in medical and allied health science curricula, and the time devoted to direct engagement with the human body is limited. This scoping review was designed to answer one research question: "What are the elements and strategies that are effective in teaching surface anatomy?" The review was performed using a five-stage scoping review framework, including research question identification, relevant study identification, study selection, data charting, and result collating and reporting. Three databases were searched using two search terms combined with a Boolean operator: "teaching" and "surface anatomy." The initial pool of 3,294 sources was assessed for duplication, and study eligibility was evaluated using inclusion and exclusion criteria. Data were abstracted from 26 original articles by one researcher and verified by two other researchers. A thematic analysis was performed, and several elements of effective teaching strategies for surface anatomy were identified, namely contextualized teaching, embracing experiential learning, and learning facilitation. This review revealed that a multimodal approach was most commonly used in surface anatomy instruction. Hence, future research should explore the effectiveness of multimodal teaching strategies that adopt the three aforementioned primary elements of effective teaching in an authentic learning environment. It is pertinent to clarify the effectiveness of these teaching strategies by evaluating their impact on student learning, organizational changes, and benefits to other stakeholders.
    Matched MeSH terms: Problem-Based Learning
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