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Fulltext An accelerated dispensing course for grad entry students - Can we teach dispensing skills over a day?

Singh H, Mohammed AH, Stokes E, Malone D, Turner J, Hassan BAR, et al.

Curr Pharm Teach Learn, 2024 Jan;16(1):69-76.
PMID: 38158327 DOI: 10.1016/j.cptl.2023.12.007

BACKGROUND AND PURPOSE: This study aimed to evaluate an accelerated dispensing course for graduate entry (GE) pharmacy students with prior science-related degrees to join undergraduate (UG) students in year three of the Monash Pharmacy degree.
EDUCATIONAL ACTIVITY AND SETTING: A one day accelerated dispensing course using MyDispense software was delivered to 59 GE students. The accelerated dispensing course was identical to the standard three-week dispensing course delivered to UG students. The same assessment of dispensing skills was conducted after course completion for both UG and GE students and included dispensing four prescriptions of varying difficulty. The assessment scores of the UG and GE students were compared. Perception data from the accelerated course were also collected.
FINDINGS: The accelerated dispensing curriculum was well received by students. They found the simulation relevant to practice, easy to navigate, and helpful for preparing them for assessment. Overall, 5.1% of GE students failed the assessment, which was lower than the 32.6% failure rate in the UG cohort. Comparison of assessment grades between UG and GE students showed no notable disadvantage to attainment of learning outcomes with the accelerated curriculum. However, UG students were more likely to provide unsafe instructions compared to GE students in their labeling for three out of four prescriptions.
SUMMARY: An accelerated dispensing curriculum can be effectively delivered to mature learners with a prior science-related degree as no notable deficiencies were identified when comparing the assessment results of GE students against UG students when both student cohorts undertook the same dispensing assessment.

Matched MeSH terms: Learning
Evolution of a self-renewing, participant-centered workshop series in BMB assessment

Tyler L, Kennelly PJ, Engelman S, Block KF, Bobenko JC, Catalano J, et al.

Biochem Mol Biol Educ, 2024;52(1):58-69.
PMID: 37815098 DOI: 10.1002/bmb.21789

We present as a case study the evolution of a series of participant-centered workshops designed to meet a need in the life sciences education community-the incorporation of best practices in the assessment of student learning. Initially, the ICABL (Inclusive Community for the Assessment of Biochemistry and Molecular Biology/BMB Learning) project arose from a grass-roots effort to develop material for a national exam in biochemistry and molecular biology. ICABL has since evolved into a community of practice in which participants themselves-through extensive peer review and reflection-become integral stakeholders in the workshops. To examine this evolution, this case study begins with a pilot workshop supported by seed funding and thoughtful programmatic assessment, the results of which informed evidence-based changes that, in turn, led to an improved experience for the community. Using participant response data, the case study also reveals critical features for successful workshops, including participant-centered activities and the value of frequent peer review of participants' products. Furthermore, we outline a train-the-trainer model for creating a self-renewing community by bringing new perspectives and voices into an existing core leadership team. This case study, then, offers a blueprint for building a thriving, evolving community of practice that not only serves the needs of individual scientist-educators as they seek to enhance student learning, but also provides a pathway for elevating members to positions of leadership.

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
Fulltext Machine learning algorithm for ventilator mode selection, pressure and volume control

T A, G G, P AMD, Assaad M

PLoS One, 2024;19(3):e0299653.
PMID: 38478485 DOI: 10.1371/journal.pone.0299653

Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient's inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

Matched MeSH terms: Machine Learning
Fulltext An adaptive data-driven architecture for mental health care applications

Sundaram A, Subramaniam H, Ab Hamid SH, Mohamad Nor A

PeerJ, 2024;12:e17133.
PMID: 38563009 DOI: 10.7717/peerj.17133

BACKGROUND: In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders.
OBJECTIVE: Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated.
METHOD: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale.
RESULTS: Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.

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
Fulltext Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and Graph Neural Network-Enhanced user profiling

Zhou S, Hudin NS

PLoS One, 2024;19(4):e0299087.
PMID: 38635519 DOI: 10.1371/journal.pone.0299087

In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.

Matched MeSH terms: Learning*
Fulltext Machine learning in internet financial risk management: A systematic literature review

Tian X, Tian Z, Khatib SFA, Wang Y

PLoS One, 2024;19(4):e0300195.
PMID: 38625972 DOI: 10.1371/journal.pone.0300195

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.

Matched MeSH terms: Machine Learning*
Fulltext BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients

Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, et al.

Sci Rep, 2023 Dec 18;13(1):22874.
PMID: 38129433 DOI: 10.1038/s41598-023-48486-7

Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.

Matched MeSH terms: Machine Learning*
Impact of air pollutants on climate change and prediction of air quality index using machine learning models

Ravindiran G, Rajamanickam S, Kanagarathinam K, Hayder G, Janardhan G, Arunkumar P, et al.

Environ Res, 2023 Dec 15;239(Pt 1):117354.
PMID: 37821071 DOI: 10.1016/j.envres.2023.117354

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

Matched MeSH terms: Machine Learning
PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI

Kaplan E, Chan WY, Altinsoy HB, Baygin M, Barua PD, Chakraborty S, et al.

J Digit Imaging, 2023 Dec;36(6):2441-2460.
PMID: 37537514 DOI: 10.1007/s10278-023-00889-8

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

Matched MeSH terms: Machine Learning
Exploratory study on student perception on the use of chat AI in process-driven problem-based learning

Hamid H, Zulkifli K, Naimat F, Che Yaacob NL, Ng KW

Curr Pharm Teach Learn, 2023 Dec;15(12):1017-1025.
PMID: 37923639 DOI: 10.1016/j.cptl.2023.10.001

INTRODUCTION: With the increasing prevalence of artificial intelligence (AI) technology, it is imperative to investigate its influence on education and the resulting impact on student learning outcomes. This includes exploring the potential application of AI in process-driven problem-based learning (PDPBL). This study aimed to investigate the perceptions of students towards the use of ChatGPT) build on GPT-3.5 in PDPBL in the Bachelor of Pharmacy program.
METHODS: Eighteen students with prior experience in traditional PDPBL processes participated in the study, divided into three groups to perform PDPBL sessions with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data searching and problem-solving. Questionnaires were used to collect data on the impact of ChatGPT on students' satisfaction, engagement, participation, and learning experience during the PBL sessions.
RESULTS: The survey revealed that ChatGPT improved group collaboration and engagement during PDPBL, while increasing motivation and encouraging more questions. Nevertheless, some students encountered difficulties understanding ChatGPT's information and questioned its reliability and credibility. Despite these challenges, most students saw ChatGPT's potential to eventually replace traditional information-seeking methods.
CONCLUSIONS: The study suggests that ChatGPT has the potential to enhance PDPBL in pharmacy education. However, further research is needed to examine the validity and reliability of the information provided by ChatGPT, and its impact on a larger sample size.

Matched MeSH terms: Problem-Based Learning*
"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
Clinical supervisors' and students' perspectives on preparedness for veterinary workplace clinical training: An international study

Routh J, Paramasivam SJ, Cockcroft P, Wood S, Remnant J, Westermann C, et al.

Vet Rec, 2023 Nov 18;193(10):e3504.
PMID: 37955283 DOI: 10.1002/vetr.3504

BACKGROUND: The alignment of student and workplace supervisors' perspectives on student preparedness for veterinary workplace clinical training (WCT) is unknown, yet misalignment could negatively impact workplace learning. The aim of this study was to quantify the relative importance of WCT preparedness characteristics according to students and supervisors and to identify differences.
METHODS: A survey was completed by 657 veterinary students and 244 clinical supervisors from 25 veterinary schools, from which rankings of the preparedness characteristics were derived. Significant rank differences were assessed using confidence intervals and permutation tests.
RESULTS: 'Honesty, integrity and dependability' was the most important characteristic according to both groups. The three characteristics with the largest rank differences were: students' awareness of their own and others' mental wellbeing and the importance of self-care; being willing to try new practical skills with support (students ranked both of these higher); and having a clinical reasoning framework for common problems (supervisors ranked higher).
LIMITATIONS: Using pooled data from many schools means that the results are not necessarily representative of the perspectives at any one institution.
CONCLUSION: There are both similarities and differences in the perspectives of students and supervisors regarding which characteristics are more important for WCT. This provides insights that can be used by educators, curriculum developers and admissions tutors to improve student preparedness for workplace learning.

Matched MeSH terms: Learning
"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
Fulltext Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Ong SQ, Isawasan P, Ngesom AMM, Shahar H, Lasim AM, Nair G

Sci Rep, 2023 Nov 05;13(1):19129.
PMID: 37926755 DOI: 10.1038/s41598-023-46342-2

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.

Matched MeSH terms: Machine Learning*
Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, et al.

Methods, 2023 Nov;219:82-94.
PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Matched MeSH terms: Machine Learning*
Fulltext Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm

Tehrany PM, Zabihi MR, Ghorbani Vajargah P, Tamimi P, Ghaderi A, Norouzkhani N, et al.

Int Wound J, 2023 Nov;20(9):3768-3775.
PMID: 37312659 DOI: 10.1111/iwj.14275

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.

Matched MeSH terms: Machine Learning
A new attempt of applying hybrid team-based and case-based learning approach in dental materials science: Does it work?

Lin GSS, Tan WW, Foong CC

Eur J Dent Educ, 2023 Nov;27(4):956-962.
PMID: 36527313 DOI: 10.1111/eje.12887

INTRODUCTION: Limited studies have been conducted on the use of a hybrid team-based learning (TBL) and case-based learning (CBL) approach in dental education. The present study aims to evaluate students' experience of the hybrid TBL-CBL in learning dental materials science subjects.
METHODS: All second-year undergraduate Bachelor of Dental Surgery (BDS) students were invited to participate in a TBL-CBL session. These participants were randomly allocated to six different groups of 10-12 students, and the session was conducted by one lecturer as the facilitator. A 23-item questionnaire assessing four domains (perceptions of effectiveness, teacher, team interaction and learning environment) was administered at the end of the TBL-CBL session.
RESULTS: The response rate was 91.9% (n = 68). Mean scores for the questionnaire items ranged from 4.13 to 4.60 suggesting a positive perception among the students towards the hybrid TBL-CBL approach. Regarding the open-response questions, students emphasised that the TBL-CBL session was effective for team interaction and group discussions. However, students wished to have a better venue for future sessions.
CONCLUSION: Positive perceptions of the students encourage future educators to consider the use of TBL-CBL approach in teaching dental materials science and to avoid the reliance on standalone conventional lectures. Future research could consider examining its effects on students' academic achievement as well as the perspectives of teachers regarding its adoption in different dental specialities.

Matched MeSH terms: Learning; Problem-Based Learning*
Teaching clinical toxinology in medical schools: The need, challenges and opportunities

Chew KS

Med J Malaysia, 2023 Nov;78(6):845-846.
PMID: 38031229

Clinical toxinology is an essential subject that should be included in undergraduate medical curricula. By equipping students with the knowledge and skills to identify and treat venomous animals and use antivenom appropriately reduces the risk of medical negligence and delays in treating and transporting these patients. Unfortunately, given the packed curriculum of undergraduate medical programs, it is important to focus on providing students with essential knowledge and skills to function as competent house officers. Student-centered learning approaches, such as gamification and community service projects, can be effective in enhancing learning and promoting awareness of appropriate toxin-related public measures.

Matched MeSH terms: Learning

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