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Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion

Zhang Y, Feng Y, Ren Z, Zuo R, Zhang T, Li Y, et al.

Bioresour Technol, 2023 Apr;374:128746.
PMID: 36813050 DOI: 10.1016/j.biortech.2023.128746

The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.

Matched MeSH terms: Machine Learning
Evidence for commonly used teaching, learning and assessment methods in contact lens clinical skills education

Woods C, Naroo S, Zeri F, Bakkar M, Barodawala F, Evans V, et al.

Cont Lens Anterior Eye, 2023 Apr;46(2):101821.
PMID: 36805277 DOI: 10.1016/j.clae.2023.101821

INTRODUCTION: Evidence based practice is now an important part of healthcare education. The aim of this narrative literature review was to determine what evidence exists on the efficacy of commonly used teaching and learning and assessment methods in the realm of contact lens skills education (CLE) in order to provide insights into best practice. A summary of the global regulation and provision of postgraduate learning and continuing professional development in CLE is included.
METHOD: An expert panel of educators was recruited and completed a literature review of current evidence of teaching and learning and assessment methods in healthcare training, with an emphasis on health care, general optometry and CLE.
RESULTS: No direct evidence of benefit of teaching and learning and assessment methods in CLE were found. There was evidence for the benefit of some teaching and learning and assessment methods in other disciplines that could be transferable to CLE and could help students meet the intended learning outcomes. There was evidence that the following teaching and learning methods helped health-care and general optometry students meet the intended learning outcomes; clinical teaching and learning, flipped classrooms, clinical skills videos and clerkships. For assessment these methods were; essays, case presentations, objective structured clinical examinations, self-assessment and formative assessment. There was no evidence that the following teaching and learning methods helped health-care and general optometry students meet the intended learning outcomes; journal clubs and case discussions. Nor was any evidence found for the following assessment methods; multiple-choice questions, oral examinations, objective structured practical examinations, holistic assessment, and summative assessment.
CONCLUSION: Investigation into the efficacy of common teaching and learning and assessment methods in CLE are required and would be beneficial for the entire community of contact lens educators, and other disciplines that wish to adapt this approach of evidence-based teaching.

Matched MeSH terms: Learning*
Fulltext An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals

Khare SK, Acharya UR

Comput Biol Med, 2023 Mar;155:106676.
PMID: 36827785 DOI: 10.1016/j.compbiomed.2023.106676

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and timely medication can help individuals with ADHD perform daily tasks without difficulty. Electroencephalogram (EEG) signals can help neurologists to detect ADHD by examining the changes occurring in it. The EEG signals are complex, non-linear, and non-stationary. It is difficult to find the subtle differences between ADHD and healthy control EEG signals visually. Also, making decisions from existing machine learning (ML) models do not guarantee similar performance (unreliable).
METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.

Matched MeSH terms: Machine Learning
When is POEM truly equivalent to LHM? A comparison of complication rates during the learning curve

Wong WJ, Affendi NANM, Siow SL, Mahendran HA, Lau PC, Ho SH, et al.

Surg Endosc, 2023 Mar;37(3):1735-1741.
PMID: 36214914 DOI: 10.1007/s00464-022-09680-2

INTRODUCTION: Per-Oral Endoscopic Myotomy (POEM) is an effective treatment for Esophageal Achalasia Cardia (EAC) but the endoscopic technique required is complex. As competency is crucial for patient safety, we believe that its' competency can be demonstrated when the complication rate equals that of an established procedure such as Laparoscopic Heller's Myotomy with Fundoplication (LHM + F).
METHODS: A multicentre, ambi-directional, non-randomized comparison of intra-procedural complications during the learning curve of POEM was performed against a historical cohort of LHM + F. Demographic, clinicopathological, procedural data and complications were collected. A direct head-to-head comparison was performed, followed by a population pyramid of complication frequency. Case sequence was then divided into blocks of 5, and the complication rates during each block was compared to the historical cohort.
RESULTS: From January 2010 to April 2021, 60 patients underwent LHM + F and 63 underwent POEM. Mean age was lower for the POEM group (41.7 years vs 48.1 years, p = 0.03), but there was no difference in gender nor type of Achalasia. The POEM group recorded a shorter overall procedural time (125.9 min vs 144.1 min, p = 0.023) and longer myotomies (10.1 cm vs 6.2 cm, p = 0.023). The overall complication rate of POEM was 20.6%, whereas the historical cohort of LHM + F had a rate of 10.0%. On visual inspection of the population pyramid, complications were more frequent in the earlier procedures. On block sequencing, complication frequency could be seen tapering off dramatically after the 25th case, and subsequently equalled that of LHM + F.
CONCLUSION: POEM is challenging even for experienced endoscopists. From our data, complication rates between POEM and LHM + F equalize after approximately 25 POEMs.

Matched MeSH terms: Learning Curve
Perspectives on opportunities and challenges in a predominantly flipped classroom-based pharmacy curriculum: A qualitative study

Wong WJ, Lee SWH, White PJ, Efendie B, Lee RFS

Curr Pharm Teach Learn, 2023 Mar;15(3):242-251.
PMID: 37055316 DOI: 10.1016/j.cptl.2023.03.004

INTRODUCTION: To adapt to flipped classroom pedagogy in universities, factors such as the amount of the program that is flipped, students' pre-existing educational experiences, and cultural background may influence adjusting to the approach. We investigated students' perspectives across four years of a predominantly flipped classroom-based pharmacy curriculum in a low to middle income country.
METHODS: We conducted five semi-structured focus groups with 18 pharmacy students from years one to four of the bachelor of pharmacy program at Monash University Malaysia where students came from different pre-university backgrounds. Focus group recordings were transcribed verbatim and thematically analysed. Interrater reliability was performed to ascertain reliability of themes.
RESULTS: Three major themes were identified. Firstly, students cited issues moving past the initial barrier when starting flipped classrooms in terms of education background impacting adaptability and how/why they eventually adapted. Another theme was how flipped classrooms helped development of life skills such as adaptability, communication, teamwork, self-reflection, and time management. The final theme was on requiring a sufficient safety net and support system in flipped classrooms that included well designed pre-classroom materials and well-implemented feedback mechanisms.
CONCLUSIONS: We have identified students' perspectives on the benefits and challenges associated with a predominantly flipped classroom pharmacy curriculum in a low to middle income country setting. We suggest using scaffolding and effective feedback approaches to guide the implementation of flipped classrooms successfully. This work can aid future educational designers in preparation and supporting a more equitable learning experience regardless of student background.

Matched MeSH terms: Learning
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
Fulltext The effect of professional portfolio learning on nursing students' professional self-concepts in geriatric adult internship: a- quasi-experimental study

Mohajer S, Li Yoong T, Chan CM, Danaee M, Mazlum SR, Bagheri N

BMC Med Educ, 2023 Feb 15;23(1):114.
PMID: 36793032 DOI: 10.1186/s12909-023-04097-4

BACKGROUND: Professional self-concept is one of the important outcomes of nursing professionalism. There is a lack of adequately planned curriculum may limit nursing students' practical knowledge, skills and professional self-concept in providing comprehensive geriatric-adult care and promoting nursing professionalism. Professional portfolio learning strategy has allowed nursing students to continue professional development and enhance nursing professionalism in professional clinical practice. However, there is little empirical evidence in nursing education to support the use of professional portfolios in blended learning modality among internship nursing students. Therefore, this study aims to examine the effect of the blended professional portfolio learning on professional self-concept among undergraduate nursing students during Geriatric-Adult internship.
METHODS: A quasi-experimental study two-group pre-test post-test design. A total of 153 eligible senior undergraduate students completed the study (76 in the intervention group and 77 in the control group). They were recruited from two Bachelor of Sciences in Nursing (BSN) cohorts from nursing schools at Mashhad University of Medical Sciences (MUMS), in Iran, in January 2020. Randomization was undertaken at the level of school via a simple lottery method. The intervention group received the professional portfolio learning program as a holistic blended learning modality, though the control group received conventional learning during professional clinical practice. A demographic questionnaire and the Nurse Professional Self-concept questionnaire were used for data collection.
RESULTS: The findings imply the effectiveness of the blended PPL program. Results of Generalized Estimating Equation (GEE) analysis was indicated significantly improved professional self-concept development and its dimensions (self-esteem, caring, staff relation, communication, knowledge, leadership) with high effect size. The results of the between-group comparison for professional self-concept and its dimensions at different time points (pre, post and follow up test) showed a significant difference between groups at post-test and follow up test (p 0.05).The results of within-group comparison for both control and intervention showed that there were significant differences in professional self-concept and for all its dimensions across the time from pre-test to post-test and follow-up (p

Matched MeSH terms: Learning
Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Singh NK, Yadav M, Singh V, Padhiyar H, Kumar V, Bhatia SK, et al.

Bioresour Technol, 2023 Feb;369:128486.
PMID: 36528177 DOI: 10.1016/j.biortech.2022.128486

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Matched MeSH terms: Machine Learning
Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

Sulaiman R, Azeman NH, Abu Bakar MH, Ahmad Nazri NA, Masran AS, Ashrif A Bakar A

Appl Spectrosc, 2023 Feb;77(2):210-219.
PMID: 36348500 DOI: 10.1177/00037028221140924

Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.

Matched MeSH terms: Machine Learning
Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques

Nilashi M, Abumalloh RA, Yusuf SYM, Thi HH, Alsulami M, Abosaq H, et al.

Comput Biol Chem, 2023 Feb;102:107788.
PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.

Matched MeSH terms: Machine Learning
Review on machine learning-based bioprocess optimization, monitoring, and control systems

Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, et al.

Bioresour Technol, 2023 Feb;370:128523.
PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Matched MeSH terms: Machine Learning*
Application of machine learning on understanding biomolecule interactions in cellular machinery

Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, et al.

Bioresour Technol, 2023 Feb;370:128522.
PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Matched MeSH terms: Machine Learning*
Fulltext Machine learning assisted hepta band THz metamaterial absorber for biomedical applications

Jain P, Chhabra H, Chauhan U, Prakash K, Gupta A, Soliman MS, et al.

Sci Rep, 2023 Jan 31;13(1):1792.
PMID: 36720922 DOI: 10.1038/s41598-023-29024-x

A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest operational frequency, with multiple absorption peaks at 1.89, 4.15, 5.32, 5.84, 7.04, 8.02, and 8.13 THz. The impedance matching theory and electric field distribution are investigated to understand the physical mechanism of hepta-band absorption. The sensing functionality is evaluated using a surrounding medium with a refractive index between 1 and 1.1, resulting in good Quality factor (Q) value of 117. The proposed sensor has the highest sensitivity of 4.72 THz/RIU for glucose detection. Extreme randomized tree (ERT) model is utilized to predict absorptivities for intermediate frequencies with unit cell dimensions, substrate thickness, angle variation, and refractive index values to reduce simulation time. The effectiveness of the ERT model in predicting absorption values is evaluated using the Adjusted R2 score, which is close to 1.0 for nmin = 2, demonstrating the prediction efficiency in various test cases. The experimental results show that 60% of simulation time and resources can be saved by simulating absorber design using the ERT model. The proposed MMA sensor with an ERT model has potential applications in biomedical fields such as bacterial infections, malaria, and other diseases.

Matched MeSH terms: Machine Learning*
Fulltext Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques

ELKarazle K, Raman V, Then P, Chua C

Sensors (Basel), 2023 Jan 20;23(3).
PMID: 36772263 DOI: 10.3390/s23031225

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

Matched MeSH terms: Machine Learning
Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review

Kee OT, Harun H, Mustafa N, Abdul Murad NA, Chin SF, Jaafar R, et al.

Cardiovasc Diabetol, 2023 Jan 19;22(1):13.
PMID: 36658644 DOI: 10.1186/s12933-023-01741-7

Prediction model has been the focus of studies since the last century in the diagnosis and prognosis of various diseases. With the advancement in computational technology, machine learning (ML) has become the widely used tool to develop a prediction model. This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) patients using machine learning. A systematic search on Scopus and Web of Science (WoS) was conducted to look for relevant articles based on the research question. The risk of bias (ROB) for all articles were assessed based on the Prediction model Risk of Bias Assessment Tool (PROBAST) statement. Neural network with 76.6% precision, 88.06% sensitivity, and area under the curve (AUC) of 0.91 was found to be the most reliable algorithm in developing prediction model for cardiovascular disease among type 2 diabetes patients. The overall concern of applicability of all included studies is low. While two out of 10 studies were shown to have high ROB, another studies ROB are unknown due to the lack of information. The adherence to reporting standards was conducted based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) standard where the overall score is 53.75%. It is highly recommended that future model development should adhere to the PROBAST and TRIPOD assessment to reduce the risk of bias and ensure its applicability in clinical settings. Potential lipid peroxidation marker is also recommended in future cardiovascular disease prediction model to improve overall model applicability.

Matched MeSH terms: Machine Learning
Fulltext A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Adnan MSG, Siam ZS, Kabir I, Kabir Z, Ahmed MR, Hassan QK, et al.

J Environ Manage, 2023 Jan 15;326(Pt B):116813.
PMID: 36435143 DOI: 10.1016/j.jenvman.2022.116813

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Matched MeSH terms: Machine Learning*
Fulltext Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

Yuan CJ, Varathan KD, Suhaimi A, Ling LW

J Rehabil Med, 2023 Jan 09;55:jrm00348.
PMID: 36306152 DOI: 10.2340/jrm.v54.2432

OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.
SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.
METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.
RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.
CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

Matched MeSH terms: Machine Learning
An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach

Nordin N, Zainol Z, Mohd Noor MH, Chan LF

Asian J Psychiatr, 2023 Jan;79:103316.
PMID: 36395702 DOI: 10.1016/j.ajp.2022.103316

Machine learning approaches have been used to develop suicide attempt predictive models recently and have been shown to have a good performance. However, those proposed models have difficulty interpreting and understanding why an individual has suicidal attempts. To overcome this issue, the identification of features such as risk factors in predicting suicide attempts is important for clinicians to make decisions. Therefore, the aim of this study is to propose an explainable predictive model to predict and analyse the importance of features for suicide attempts. This model can also provide explanations to improve the clinical understanding of suicide attempts. Two complex ensemble learning models, namely Random Forest and Gradient Boosting with an explanatory model (SHapley Additive exPlanations (SHAP)) have been constructed. The models are used for predictive interpretation and understanding of the importance of the features. The experiment shows that both models with SHAP are able to interpret and understand the nature of an individual's predictions with suicide attempts. However, compared with Random Forest, the results show that Gradient Boosting with SHAP achieves higher accuracy and the analyses found that history of suicide attempts, suicidal ideation, and ethnicity as the main predictors for suicide attempts.

Matched MeSH terms: Machine Learning
Team-based self-directed learning enhanced students' learning experience in undergraduate surgical teaching

Sim SK, Myo N, Sohail M

Med J Malaysia, 2023 Jan;78(1):61-67.
PMID: 36715193

INTRODUCTION: To evaluate the effectiveness of team-based self-directed learning (SDL) in the teaching of the undergraduate Year 5 surgical posting.
MATERIALS AND METHODS: A quasi-experimental study was conducted to develop and administer a team-based SDL versus a conventional SDL to teach undergraduate surgical topics. One hundred and seventy-four medical students who underwent the Year 5 surgical posting were recruited. They were assigned to two groups receiving either the teambased SDL or the conventional SDL. Pre- and post-SDL assessments were conducted to determine students' understanding of selected surgical topics. A selfadministered questionnaire was used to collect student feedback on the team-based SDL.
RESULTS: The team-based SDL group scored significantly higher than the conventional SDL group in the post-SDL assessment (74.70 ± 6.81 vs. 63.77 ± 4.18, t = -12.72, p < 0.01). The students agreed that the team-based SDL method facilitated their learning process.
CONCLUSION: The study demonstrated that the use of a teambased SDL is an effective learning strategy for teaching the Year 5 surgical posting. This method encouraged peer discussion and promoted teamwork in completing task assignments to achieve the learning objectives.

Matched MeSH terms: Learning
Fulltext A digital module-based experiential learning in protein biochemistry during the COVID-19 pandemic paradigm

Yap MKK

Biochem Mol Biol Educ, 2023 Jan;51(1):77-80.
PMID: 36194083 DOI: 10.1002/bmb.21680

Experiential learning is compromised in meeting the educational demands of our students during the challenging time of the COVID-19 pandemic. A more inclusive, flexible, and objective-oriented experiential learning environment is required. In this context, module-based experiential learning that is executable on a digital platform was designed. The learning module focused on protein biochemistry, contained a combination of asynchronous and synchronous activities categorized into 'Knowledge Hub' and 'Lab-based Movie', across 5 weeks. Digital and module-based experiential learning provides equitable, inclusive, and flexible access to students at remote locations. Furthermore, it is an objective-oriented and highly organized experiential learning framework that encourages students to engage and participate more in the learning process.

Matched MeSH terms: Learning; Problem-Based Learning*

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