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  1. 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
  2. Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS
    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
  3. 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
  4. Sharif-Nia H, Arslan G, Reardon J, Allen KA, Ma L, She L, et al.
    Nurs Open, 2024 Mar;11(3):e2130.
    PMID: 38486130 DOI: 10.1002/nop2.2130
    AIM: This study explored the influence of student computer competency on e-learning outcomes among Iranian nursing students and examined its mediating role in the relationship between virtual learning infrastructure, student collaboration, access to electronic facilities, and e-learning outcomes.

    DESIGN: A cross sectional study.

    METHOD: A self-administered online survey was used from August to October 2022, with a sample size of 417 nursing students selected through convenience sampling. Descriptive statistics, correlation analyses, and PROCESS macro v4.1 (Model 4) were used for data analysis.

    RESULTS: The results revealed that virtual learning infrastructure, access to electronic facilities, and student collaboration, significantly predict student computer competency and e-learning outcomes. Virtual learning infrastructure and access to electronic facilities were found to be the strongest predictors of student computer competency, while student collaboration had a smaller but still significant effect. Student computer competency was found to mediate the relationship between virtual learning infrastructure, access to electronic facilities, student collaboration, and e-learning outcomes.

    Matched MeSH terms: Learning
  5. Modi S, Kasmiran KA, Mohd Sharef N, Sharum MY
    J Biomed Inform, 2024 Mar;151:104603.
    PMID: 38331081 DOI: 10.1016/j.jbi.2024.104603
    BACKGROUND: An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and patient medication studies.

    OBJECTIVE: From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods.

    METHODOLOGY: Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis.

    RESULTS: We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018.

    CONCLUSION: Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.

    Matched MeSH terms: Machine Learning*
  6. 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
  7. Mahmud SMH, Goh KOM, Hosen MF, Nandi D, Shoombuatong W
    Sci Rep, 2024 Feb 05;14(1):2961.
    PMID: 38316843 DOI: 10.1038/s41598-024-52653-9
    DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
    Matched MeSH terms: Machine Learning
  8. Zain E, Talreja N, Hesarghatta Ramamurthy P, Muzaffar D, Rehman K, Khan AA, et al.
    Eur J Dent Educ, 2024 Feb;28(1):358-369.
    PMID: 37864324 DOI: 10.1111/eje.12957
    INTRODUCTION: Simulation-based education is of paramount importance in a dental pre-clinical setting. Hence, continuous quality improvement is crucial to optimize students' knowledge and clinical skills. This study aimed to evaluate the impact of evidence-based simulation learning (EBSL) compared with traditional-based simulation learning (TBSL) using Plan-Do-Study-Act (PDSA) model.

    MATERIALS AND METHODS: This quality improvement project was undertaken at a private university. Guided by the PDSA model, rubber dam application tasks were conducted in the simulation lab in 2 phases. Phase 1 included TBSL and phase 2 included EBSL comprising of 2 PDSA cycles. 'Plan' stage involved obtaining feedback from students and the concerned staff. 'Do' stage included implementation of EBSL in eight steps adopted from Higgins's framework. 'Study' stage evaluated the outcomes and in 'Act' stage amendments were made to the first EBSL cycle. In the second PDSA cycle re-implementation and evaluation of the rubber dam application exercises were carried out. Descriptive data were presented as percentages and mean scores were compared using paired t-test.

    RESULTS: Thirty-seven year 2 students participated in this study. A significant improvement in the mean scores was observed between TBSL and EBSL (3.02 + 0.16 and 3.91 + 0.27, respectively, p 

    Matched MeSH terms: Learning
  9. 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
  10. 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
  11. Mohd Sahini SN, Mohd Nor Hazalin NA, Srikumar BN, Jayasingh Chellammal HS, Surindar Singh GK
    Neurobiol Learn Mem, 2024 Feb;208:107880.
    PMID: 38103676 DOI: 10.1016/j.nlm.2023.107880
    Environmental enrichment (EE) is a process of brain stimulation by modifying the surroundings, for example, by changing the sensory, social, or physical conditions. Rodents have been used in such experimental strategies through exposure to diverse physical, social, and exploration conditions. The present study conducted an extensive analysis of the existing literature surrounding the impact of EE on dementia rodent models. The review emphasised the two principal aspects that are very closely related to dementia: cognitive function (learning and memory) as well as psychological factors (anxiety-related behaviours such as phobias and unrealistic worries). Also highlighted were the mechanisms involved in the rodent models of dementia showing EE effects. Two search engines, PubMed and Science Direct, were used for data collection using the following keywords: environmental enrichment, dementia, rodent model, cognitive performance, and anxiety-related behaviour. Fifty-five articles were chosen depending on the criteria for inclusion and exclusion. The rodent models with dementia demonstrated improved learning and memory in the form of hampered inflammatory responses, enhanced neuronal plasticity, and sustained neuronal activity. EE housing also prevented memory impairment through the prevention of amyloid beta (Aβ) seeding formation, an early stage of Aβ plaque formation. The rodents subjected to EE were observed to present increased exploratory activity and exert less anxiety-related behaviour, compared to those in standard housing. However, some studies have proposed that EE intervention through exercise would be too mild to counteract the anxiety-related behaviour and risk assessment behaviour deficits in the Alzheimer's disease rodent model. Future studies should be conducted on old-aged rodents and the duration of EE exposure that would elicit the greatest benefits since the existing studies have been conducted on a range of ages and EE durations. In summary, EE had a considerable effect on dementia rodent models, with the most evident being improved cognitive function.
    Matched MeSH terms: Maze Learning/physiology
  12. Caliph SM, Lee CY
    Curr Pharm Teach Learn, 2024 Feb;16(2):119-123.
    PMID: 38158334 DOI: 10.1016/j.cptl.2023.12.017
    BACKGROUND AND PURPOSE: Pharmacy students' perception of the effectiveness of remote online learning experienced during the pandemic, and their learning expectations post-pandemic were unknown. The main purpose of this study was to examine students' perceived effectiveness of online teaching and learning activities developed for active learning and pharmacy professional skills development, and the feasibility of online assessments.

    EDUCATIONAL ACTIVITY AND SETTING: A cross-sectional online survey involving second-year pharmacy students of Monash Malaysia (MA) and Monash Australia (PA) campuses was conducted. The survey consisted of 15 Likert-scale multiple-choice questions and an open-ended question. Data were analysed statistically.

    FINDINGS: Students at both MA and PA campuses were satisfied with the remote online learning experienced during the pandemic but indicated a preference for a blended learning approach. Students at the MA campus felt that on-campus face-to-face classes were more engaging and advantageous for their learning and skills development (P 

    Matched MeSH terms: Problem-Based Learning
  13. Alkhamis MA, Al Jarallah M, Attur S, Zubaid M
    Sci Rep, 2024 Jan 12;14(1):1243.
    PMID: 38216605 DOI: 10.1038/s41598-024-51604-8
    The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
    Matched MeSH terms: Machine Learning
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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*
  19. 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
  20. 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
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