Ten simple rules for reporting machine learning methods implementation and evaluation on biomedical data

Seghier, Mohamed L.

doi:10.1002/ima.22674

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Although different types of ML will have distinct approaches to training the model, there are basic steps that most models utilize [ 68 ]. Figure 1 gives an overview of the process of the steps taken to create the Machine Learning models in the prediction of PP.…”

Section: Methodsmentioning

confidence: 99%

Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms

Seyedtabib

Mezerji

2023

BMC Med Inform Decis Mak

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Background Polypharmacy (PP) is increasingly common in Iran, and contributes to the substantial burden of drug-related morbidity, increasing the potential for drug interactions and potentially inappropriate medications. Machine learning algorithms (ML) can be employed as an alternative solution for the prediction of PP. Therefore, our study aimed to compare several ML algorithms to predict the PP using the health insurance claims data and choose the best-performing algorithm as a predictive tool for decision-making. Methods This population-based cross-sectional study was performed between April 2021 and March 2022. After feature selection, information about 550 thousand patients were obtained from National Center for Health Insurance Research (NCHIR). Afterwards, several ML algorithms were trained to predict PP. Finally, to assess the models’ performance, the metrics derived from the confusion matrix were calculated. Results The study sample comprised 554 133 adults with a median (IQR) age of 51 years (40 – 62) that nested in 27 cities within the Khuzestan province of Iran. Most of the patients were female (62.5%), married (63.5%), and employed (83.2%) during the last year. The prevalence of PP in all populations was about 36.0%. After performing the feature selection, out of 23 features, the number of prescriptions, Insurance coverage for prescription drugs, and hypertension were found as the top three predictors. Experimental results showed that Random Forest (RF) performed better than other ML algorithms with recall, specificity, accuracy, precision and F1-score of 63.92%, 89.92%, 79.99%, 63.92% and 63.92% respectively. Conclusion It was found that ML provides a reasonable level of accuracy in predicting polypharmacy. Therefore, the prediction models based on ML, especially the RF algorithm, performed better than other methods for predicting PP in Iranian people in terms of the performance criteria.

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Section: Methodsmentioning

confidence: 99%

Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms

Seyedtabib

Mezerji

2023

BMC Med Inform Decis Mak

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“…The developed machine learning models tested in terms of their performance against specific validation measures, such as accuracy, F1score etc. After the fine tuning of the parameters the best trained ML model is selected [38]. 6.…”

Section: Modeling -Involves the Development Of Machine Learning Metho...mentioning

confidence: 99%

Evaluation of Explainable Artificial Intelligence Methods in Language Learning Classification of Spanish Tertiary Education Students

Tzionis,

Antzoulatos,

Papaioannou

et al. 2024

Lecture Notes in Networks and Systems

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With the increasing prevalence of AI, significant advancements have been made across various domains, such as healthcare, learning, industry, etc. However, challenges persist in terms of trusting and comprehending the outcomes generated by these technologies. Specifically in the language learning domain, teachers face challenges regarding the classification of the students' learning capabilities and build the appropriate learning path for them. To address these challenges, the concept of Explainable Artificial Intelligence (XAI) was adopted, which is a set of processes and methods that allows human users to interpret, understand and trust the results derived from machine learning models. In this study, we adopt two well-known XAI algorithms, PFI and SHAP in a proposed Knowledge Generation Model equipped with ML models to derive hidden knowledge. The whole framework has been applied and evaluated on the Language Learning Classification of Spanish Tertiary Education Students acquired from the CEDEL2 database. The analysis concludes that in terms of explaining the black-box models, the SHAP model-agnostic method is the most comprehensive and dominant for visualizing feature interactions and feature importance and be applicable to any type of data.

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“…This section presents the top tips for reporting the dataset using DL. These tips have been derived from the literature and the author's experience in the field [621][622][623].…”

Section: Tips For Reporting the Datasetmentioning

confidence: 99%

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

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Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

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Ten simple rules for reporting machine learning methods implementation and evaluation on biomedical data

Cited by 10 publications

References 27 publications

Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms

Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms

Evaluation of Explainable Artificial Intelligence Methods in Language Learning Classification of Spanish Tertiary Education Students

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

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