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1. A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

   https://doi.org/10.2196/50328  

   Hendawi, Rasha; Li, Juan; Roy, Souradip (January 2023, JMIR Formative Research)

   BackgroundMachine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust. ObjectiveThis study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, designed to empower health care professionals with a clear understanding of AI-generated predictions and recommendations for diabetes care. XAI4Diabetes not only delivers diabetes risk predictions but also furnishes easily interpretable explanations for complex machine learning models and their outcomes. MethodsXAI4Diabetes features a versatile multimodule explanation framework that leverages machine learning, knowledge graphs, and ontologies. The platform comprises the following four essential modules: (1) knowledge base, (2) knowledge matching, (3) prediction, and (4) interpretation. By harnessing AI techniques, XAI4Diabetes forecasts diabetes risk and provides valuable insights into the prediction process and outcomes. A structured, survey-based user study assessed the app’s usability and influence on participants’ comprehension of machine learning predictions in real-world patient scenarios. ResultsA prototype mobile app was meticulously developed and subjected to thorough usability studies and satisfaction surveys. The evaluation study findings underscore the substantial improvement in medical professionals’ comprehension of key aspects, including the (1) diabetes prediction process, (2) data sets used for model training, (3) data features used, and (4) relative significance of different features in prediction outcomes. Most participants reported heightened understanding of and trust in AI predictions following their use of XAI4Diabetes. The satisfaction survey results further revealed a high level of overall user satisfaction with the tool. ConclusionsThis study introduces XAI4Diabetes, a versatile multi-model explainable prediction platform tailored to diabetes care. By enabling transparent diabetes risk predictions and delivering interpretable insights, XAI4Diabetes empowers health care professionals to comprehend the AI-driven decision-making process, thereby fostering transparency and trust. These advancements hold the potential to mitigate biases and facilitate the broader integration of AI in diabetes care. 

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2. Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers

   https://doi.org/10.3390/genes13091557  

   Gomes, Rahul; Paul, Nijhum; He, Nichol; Huber, Aaron Francis; Jansen, Rick J (September 2022, Genes)

   DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone. 

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3. Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Student Behavior and Affect

   Botelho, A. F.; Baker, R. S.; Heffernan, N. T. (July 2019, The Twelfth International Conference on Educational Data Mining)

   There is a long history of research on the development of models to detect and study student behavior and affect. Developing computer-based models has allowed the study of learning constructs at fine levels of granularity and over long periods of time. For many years, these models were developed using features based on previous educational research from the raw log data. More recently, however, the application of deep learning models has often skipped this feature-engineering step by allowing the algorithm to learn features from the fine-grained raw log data. As many of these deep learning models have led to promising results, researchers have asked which situations may lead to machine-learned features performing better than expert-generated features. This work addresses this question by comparing the use of machine-learned and expert-engineered features for three previously-developed models of student affect, off-task behavior, and gaming the system. In addition, we propose a third feature-engineering method that combines expert features with machine learning to explore the strengths and weaknesses of these approaches to build detectors of student affect and unproductive behaviors. 

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4. Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Student Behavior and Affect

   Botelho, A. F.; Baker, R. S.; Heffernan, N. T. (January 2019, Proceedings of the Twelfth International Conference on Educational Data Mining)

   There is a long history of research on the development of models to detect and study student behavior and affect. Developing computer-based models has allowed the study of learning constructs at fine levels of granularity and over long periods of time. For many years, these models were developed using features based on previous educational research from the raw log data. More recently, however, the application of deep learning models has often skipped this feature engineering step by allowing the algorithm to learn features from the fine-grained raw log data. As many of these deep learning models have led to promising results, researchers have asked which situations may lead to machine-learned features performing better than expert-generated features. This work addresses this question by comparing the use of machine-learned and expert-engineered features for three previously-developed models of student affect, off-task behavior, and gaming the system. In addition, we propose a third feature-engineering method that combines expert features with machine learning to explore the strengths and weaknesses of these approaches to build detectors of student affect and unproductive behaviors. 

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5. Personalized Deep Bi-LSTM RNN Based Model for Pain Intensity Classification Using EDA Signal

   https://doi.org/10.3390/s22218087  

   Pouromran, Fatemeh; Lin, Yingzi; Kamarthi, Sagar (November 2022, Sensors)

   Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment. 

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