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Fulltext Remote extemporaneous compounding lab practical for pharmacy students during the COVID-19 pandemic

Dujaili J, Ong WK, Kc B, Vordenberg SE, Mattingly AN, Lee RFS

Curr Pharm Teach Learn, 2023 Jun;15(6):624-632.
PMID: 37357124 DOI: 10.1016/j.cptl.2023.06.012

BACKGROUND AND PURPOSE: Due to COVID-19 movement restrictions, institutes of higher learning had to deliver pharmacy curricula remotely. One major challenge was teaching practical lab skills, such as extemporaneous compounding, remotely due to the need for hands-on learning and its associated logistical requirements.
EDUCATIONAL ACTIVITY AND SETTING: We present the approach to remote extemporaneous compounding teaching taken by three pharmacy schools: Monash University Malaysia, University of Michigan, and University of Maryland. Prior to delivery, students were either supplied with or asked to procure a set of easily accessible ingredients and equipment to conduct the extemporaneous practicals from home. We conducted lessons remotely using both synchronous and asynchronous delivery, and demonstrated, taught, and assessed practical lab skills using video conferencing modalities.
FINDINGS: We successfully conducted remote teaching of extemporaneous compounding, where similar learning outcomes to the face-to-face implementation were achieved. At Monash University Malaysia, > 90% of students responding to the post-activity surveys found the remote extemporaneous sessions useful for their learning, and qualitative comments supported these views. Mean scores from the remote extemporaneous labs in 2021 were similar to those when conducted physically in 2019, supporting the effectiveness of the approach. The different approaches attempted by the three institutions highlighted the flexibility in implementation that can be considered to achieve similar outcomes.
SUMMARY: Combining technology-based approaches with synchronous and asynchronous teaching and learning methods can successfully deliver extemporaneous compounding skills remotely.

Matched MeSH terms: Learning
Draw to learn: Removable partial denture designing through team-based learning

Sivakumar I, Arunachalam S, Buzayan MM

J Dent Educ, 2023 Jun;87 Suppl 1:892-894.
PMID: 36469857 DOI: 10.1002/jdd.13153
Matched MeSH terms: Learning
Fulltext Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

Dutta AK, Mageswari RU, Gayathri A, Dallfin Bruxella JM, Ishak MK, Mostafa SM, et al.

Comput Intell Neurosci, 2022;2022:7776319.
PMID: 35694571 DOI: 10.1155/2022/7776319

Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.

Matched MeSH terms: Machine Learning
A systematic review of behaviour change techniques in pharmacist-delivered self-management interventions towards patients with chronic obstructive pulmonary disease

Jamil N, Zainal ZA, Alias SH, Chong LY, Hashim R

Res Social Adm Pharm, 2023 Aug;19(8):1131-1145.
PMID: 37202279 DOI: 10.1016/j.sapharm.2023.05.006

BACKGROUND: Self-management interventions often employ behaviour change techniques in order to produce desired target behaviours that are necessary for day-to-day living with a chronic disease. Despite the large number of self-management interventions for patients with chronic obstructive pulmonary disease (COPD), previously reported interventions have been typically delivered by healthcare providers other than the pharmacist.
OBJECTIVE: This systematic review examined the components of pharmacists-delivered COPD self-management interventions according to an established taxonomy of behaviour change techniques (BCTs).
METHODS: A systematic search was conducted on PubMed, ScienceDirect, OVID, and Google Scholar from January 2011 to December 2021 for studies of pharmacist-delivered self-management interventions in COPD patients.
RESULTS: A total of seventeen studies of intervention were eligible for inclusion in the narrative review. Interventions were educational and were delivered individually and face-to-face for the first session. Across studies, pharmacists spent an average of 35 min on the first meeting and had an average of 6 follow-up sessions. Recurrent BCTs in pharmacist interventions were "Information on the health consequence", "Feedback on behaviour", "Instruction on how to perform a behaviour", "Demonstration of the behaviour" and "Behavioural practice/rehearsal".
CONCLUSIONS: Pharmacists have provided interventions towards improving health behaviours, especially on adherence and usage of inhaler devices for patients with COPD. Future self-management interventions should be designed using the identified BCTs for the improvement of COPD self-management and disease outcomes.

Matched MeSH terms: Learning
Using artificial intelligence methods for systematic review in health sciences: A systematic review

Blaizot A, Veettil SK, Saidoung P, Moreno-Garcia CF, Wiratunga N, Aceves-Martins M, et al.

Res Synth Methods, 2022 May;13(3):353-362.
PMID: 35174972 DOI: 10.1002/jrsm.1553

The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods. A search was conducted in 4 databases (Medline, Embase, CDSR, and Epistemonikos) up to April 2021 for systematic reviews and other related reviews implementing AI methods. To be included, the review must use any form of AI method, including machine learning, deep learning, neural network, or any other applications used to enable the full or semi-autonomous performance of one or more stages in the development of evidence synthesis. Twelve reviews were included, using nine different tools to implement 15 different AI methods. Eleven methods were used in the screening stages of the review (73%). The rest were divided: two in data extraction (13%) and two in risk of bias assessment (13%). The ambiguous benefits of the data extractions, combined with the reported advantages from 10 reviews, indicating that AI platforms have taken hold with varying success in evidence synthesis. However, the results are qualified by the reliance on the self-reporting of the review authors. Extensive human validation still appears required at this stage in implementing AI methods, though further evaluation is required to define the overall contribution of such platforms in enhancing efficiency and quality in evidence synthesis.

Matched MeSH terms: Machine Learning
Fulltext A biomarker discovery of acute myocardial infarction using feature selection and machine learning

Mohd Faizal AS, Hon WY, Thevarajah TM, Khor SM, Chang SW

Med Biol Eng Comput, 2023 Oct;61(10):2527-2541.
PMID: 37199891 DOI: 10.1007/s11517-023-02841-y

Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.

Matched MeSH terms: Machine Learning
Fulltext Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan MJ, Mohd Rahim MS, Salim N, Rehman A, Nobanee H

J Healthc Eng, 2022;2022:2550120.
PMID: 35444781 DOI: 10.1155/2022/2550120

In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.

Matched MeSH terms: Machine Learning
Fulltext Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences

Yang S, Li X, Jiang Z, Xiao M

PLoS One, 2023;18(10):e0290126.
PMID: 37844110 DOI: 10.1371/journal.pone.0290126

Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.

Matched MeSH terms: Machine Learning
Special care dentistry education during the COVID-19 pandemic: The impact of online peer-assisted learning

Mohd Suria TYI, Omar AF, Wan Mokhtar I, Rahman ANAA, Kamaruddin AA, Ahmad MS

Spec Care Dentist, 2023;43(6):848-855.
PMID: 37013967 DOI: 10.1111/scd.12857

OBJECTIVES: This study aims to analyze the impact and students' perceptions of online peer-assisted learning (OPL), developed as an alternative and innovative approach to Special Care Dentistry (SCD) training during the COVID-19 pandemic. Online peer-assisted learning (OPL) is an alternative pedagogical approach that combines online education and peer-assisted teaching.
METHODS: The OPL session was conducted by two postgraduate students in SCD (as teachers), to final year undergraduate dental students (as learners) (n = 90), supervised by two specialists in SCD-related areas (as supervisors). Vetted online pre- and post-intervention quizzes were conducted before and after the session, respectively, followed by an online validated feedback survey of the students' learning experiences. Meanwhile, a reflective session was conducted between the postgraduate students and supervisors to explore their perceptions of OPL. Quantitative data was analyzed via paired t-test (significance level, P

Matched MeSH terms: Learning
Fulltext Analysis of the Association between Teachers' Classroom Teaching Behaviors and Students' Knowledge Acceptance Based on Psychological Data Analysis

Yao T, Yang X

Occup Ther Int, 2022;2022:2661398.
PMID: 35814354 DOI: 10.1155/2022/2661398

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology.

Matched MeSH terms: Learning
Fulltext Academics' continuance intention to use learning technologies during COVID-19 and beyond

Vasuthevan K, Vaithilingam S, Ng JWJ

PLoS One, 2024;19(1):e0295746.
PMID: 38166113 DOI: 10.1371/journal.pone.0295746

The COVID-19 pandemic has revolutionized the teaching pedagogy in higher education as universities are forecasted to increase investments in learning technology infrastructure to transition away from traditional teaching methods. Therefore, it is crucial to investigate whether academics intend to continually integrate learning technologies as part of a permanent pedagogical change beyond the COVID-19 pandemic. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT), and Expectation Confirmation Model (ECM), this study examines the salient determinants influencing the continuance intention of academics to use learning technologies in their teaching pedagogy during and after COVID-19. Primary data collected from a private university was analyzed using the partial least squares structural equation modelling technique (PLS-SEM). The findings revealed two sequential mediating relationships which serve as the mechanism linking the relationship between facilitating conditions and their continuance intention to use learning technologies during and beyond the COVID-19 pandemic.

Matched MeSH terms: Learning
A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data

Xu M, Abdullah NA, Md Sabri AQ

Comput Biol Chem, 2024 Feb;108:107997.
PMID: 38154318 DOI: 10.1016/j.compbiolchem.2023.107997

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

Matched MeSH terms: Machine Learning
An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Sultan G, Zubair S

Comput Biol Chem, 2024 Feb;108:107999.
PMID: 38070457 DOI: 10.1016/j.compbiolchem.2023.107999

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

Matched MeSH terms: Machine Learning
Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L

Environ Monit Assess, 2024 Feb 24;196(3):302.
PMID: 38401024 DOI: 10.1007/s10661-024-12454-z

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

Matched MeSH terms: Machine Learning
Groundwater level forecasting with machine learning models: A review

Boo KBW, El-Shafie A, Othman F, Khan MMH, Birima AH, Ahmed AN

Water Res, 2024 Mar 15;252:121249.
PMID: 38330715 DOI: 10.1016/j.watres.2024.121249

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

Matched MeSH terms: Machine Learning
Artificial neural network and convolutional neural network for prediction of dental caries

Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS

Spectrochim Acta A Mol Biomol Spectrosc, 2024 May 05;312:124063.
PMID: 38394882 DOI: 10.1016/j.saa.2024.124063

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.

Matched MeSH terms: Machine Learning
Fulltext Risky business: medical students' feedback-seeking behaviours: a mixed methods study

Spooner M, Reinhardt C, Boland F, McConkey S, Pawlikowska T

Med Educ Online, 2024 Dec 31;29(1):2330259.
PMID: 38529848 DOI: 10.1080/10872981.2024.2330259

There are differing views on how learners' feedback-seeking behaviours (FSB) develop during training. With globalisation has come medical student migration and programme internationalisation. Western-derived educational practices may prove challenging for diverse learner populations. Exploring undergraduate activity using a model of FSB may give insight into how FSB evolves and the influence of situational factors, such as nationality and site of study. Our findings seek to inform medical school processes that support feedback literacy. Using a mixed methods approach, we collected questionnaire and interview data from final-year medical students in Ireland, Bahrain, and Malaysia. A validated questionnaire investigated relationships with FSB and goal orientation, leadership style preference, and perceived costs and benefits. Interviews with the same student population explored their FSB experiences in clinical practice, qualitatively, enriching this data. The data were integrated using the 'following the thread' technique. Three hundred and twenty-five of a total of 514 completed questionnaires and 57 interviews were analysed. Learning goal orientation (LGO), instrumental leadership and supportive leadership related positively to perceived feedback benefits (0.23, 0.2, and 0.31, respectively, p

Matched MeSH terms: Learning
Transformative learning in clinical reasoning: a meta-synthesis in undergraduate primary care medical education

Abdul Rahman NF, Davies N, Suhaimi J, Idris F, Syed Mohamad SN, Park S

Educ Prim Care, 2023 Jul;34(4):211-219.
PMID: 37742228 DOI: 10.1080/14739879.2023.2248070

Clinical reasoning is a vital medical education skill, yet its nuances in undergraduate primary care settings remain debated. This systematic review explores clinical reasoning teaching and learning intricacies within primary care. We redefine clinical reasoning as dynamically assimilating and prioritising synthesised patient, significant other, or healthcare professional information for diagnoses or non-diagnoses. This focused meta-synthesis applies transformative learning theory to primary care clinical reasoning education. A comprehensive analysis of 29 selected studies encompassing various designs made insights into clinical reasoning learning dimensions visible. Primary care placements in varying duration and settings foster diverse instructional methods like bedside teaching, clinical consultations, simulated clinics, virtual case libraries, and more. This review highlights the interplay between disease-oriented and patient-centred orientations in clinical reasoning learning. Transformative learning theory provides an innovative lens, revealing stages of initiation, persistence, time and space, and competence and confidence in students' clinical reasoning evolution. Clinical teachers guide this transformation, adopting roles as fortifiers, connoisseurs, mediators, and monitors. Patient engagement spans passive to active involvement, co-constructing clinical reasoning. The review underscores theoretical underpinnings' significance in shaping clinical reasoning pedagogy, advocating broader diversity. Intentional student guidance amid primary care complexities is vital. Utilising transformative learning, interventions bridging cognitive boundaries enhance meaningful clinical reasoning learning experiences. This study contributes insights for refining pedagogy, encouraging diverse research, and fostering holistic clinical reasoning development.

Matched MeSH terms: Learning
Fulltext Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder KK, Chowdhury MEH, Tahir AM, Mahbub ZB, Khandakar A, Hossain MS, et al.

Sensors (Basel), 2022 Jan 12;22(2).
PMID: 35062533 DOI: 10.3390/s22020574

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.

Matched MeSH terms: Machine Learning
Fulltext An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Mandala S, Rizal A, Adiwijaya, Nurmaini S, Suci Amini S, Almayda Sudarisman G, et al.

PLoS One, 2024;19(4):e0297551.
PMID: 38593145 DOI: 10.1371/journal.pone.0297551

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.

Matched MeSH terms: Machine Learning

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