Prenatal alcohol exposure (PAE) refers to the exposure of the developing fetus due to alcohol consumption during pregnancy and can have life-long consequences for learning, behavior, and health. Understanding the impact of PAE on the developing brain manifests challenges due to its complex structural and functional attributes, which can be addressed by leveraging machine learning (ML) and deep learning (DL) approaches. While most ML and DL models have been tailored for adult-centric problems, this work focuses on applying DL to detect PAE in the pediatric population. This study integrates the pre-trained simple fully convolutional network (SFCN) as a transfer learning approach for extracting features and a newly trained classifier to distinguish between unexposed and PAE participants based on T1-weighted structural brain magnetic resonance (MR) scans of individuals aged 2-8 years. Among several varying dataset sizes and augmentation strategy during training, the classifier secured the highest sensitivity of 88.47% with 85.04% average accuracy on testing data when considering a balanced dataset with augmentation for both classes. Moreover, we also preliminarily performed explainability analysis using the Grad-CAM method, highlighting various brain regions such as corpus callosum, cerebellum, pons, and white matter as the most important features in the model\'s decision-making process. Despite the challenges of constructing DL models for pediatric populations due to the brain\'s rapid development, motion artifacts, and insufficient data, this work highlights the potential of transfer learning in situations where data is limited. Furthermore, this study underscores the importance of preserving a balanced dataset for fair classification and clarifying the rationale behind the model\'s prediction using explainability analysis.

Users of social platforms often perceive these sites as supportive spaces to post about their mental health issues. Those conversations contain important traces about individuals\' health risks. Recently, researchers have exploited this online information to construct mental health detection models, which aim to identify users at risk on platforms like Twitter, Reddit or Facebook. Most of these models are focused on achieving good classification results, ignoring the explainability and interpretability of the decisions. Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals. In this paper, we introduce transformer-based architectures designed to detect and explain the appearance of depressive symptom markers in user-generated content from social media. We present two approaches: (i) train a model to classify, and another one to explain the classifier\'s decision separately and (ii) unify the two tasks simultaneously within a single model. Additionally, for this latter manner, we also investigated the performance of recent conversational Large Language Models (LLMs) utilizing both in-context learning and finetuning. Our models provide natural language explanations, aligning with validated symptoms, thus enabling clinicians to interpret the decisions more effectively. We evaluate our approaches using recent symptom-focused datasets, using both offline metrics and expert-in-the-loop evaluations to assess the quality of our models\' explanations. Our findings demonstrate that it is possible to achieve good classification results while generating interpretable symptom-based explanations.

Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visualizing such confounding factors. We propose DiffChest, a self-conditioned diffusion model trained on 515,704 chest radiographs from 194,956 patients across the US and Europe. DiffChest provides patient-specific explanations and visualizes confounding factors that might mislead the model. The high inter-reader agreement, with Fleiss\' kappa values of 0.8 or higher, validates its capability to identify treatment-related confounders. Confounders are accurately detected with 10%-100% prevalence rates. The pretraining process optimizes the model for relevant imaging information, resulting in excellent diagnostic accuracy for 11 chest conditions, including pleural effusion and heart insufficiency. Our findings highlight the potential of diffusion models in medical image classification, providing insights into confounding factors and enhancing model robustness and reliability.

Crohn\'s disease (CD) is a chronic inflammatory bowel disease of unknown origin that can cause significant disability and morbidity with its progression. Due to the unique nature of CD, surgery is often necessary for many patients during their lifetime, and the incidence of postoperative complications is high, which can affect the prognosis of patients. Therefore, it is essential to identify and manage postoperative complications. Machine learning (ML) has become increasingly important in the medical field, and ML-based models can be used to predict postoperative complications of intestinal resection for CD. Recently, a valuable article titled \"Predicting short-term major postoperative complications in intestinal resection for Crohn\'s disease: A machine learning-based study\" was published by Wang et al. We appreciate the authors\' creative work, and we are willing to share our views and discuss them with the authors.

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Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor\'s contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability.

The objective of this study was to develop explainable AI modeling in the prediction of cardiovascular disease. The XGBoost algorithm was used followed by rule extraction and argumentation theory that provides interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas. Our findings are in agreement with previous research utilizing the XGBoost machine learning algorithm for prediction of cardiovascular risk, however it is supported by rule based explainability, offering significant advantages in terms of providing both global and local explainability. Further work is needed to enhance the argumentation-based rule interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas.

In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research project, machine learning methods are used for individual risk prediction of hospital-onset bacteremia (HOB). This paper presents a vision on a step-wise process for implementation and evaluation of user-centered XAI for risk prediction of HOB. An initial requirement analysis revealed first insights on the users\' needs of explainability to use and trust such risk prediction applications. The findings were then used to propose step-wise process towards a user-centered evaluation.

Feature attribution methods stand as a popular approach for explaining the decisions made by convolutional neural networks. Given their nature as local explainability tools, these methods fall short in providing a systematic evaluation of their global meaningfulness. This limitation often gives rise to confirmation bias, where explanations are crafted after the fact. Consequently, we conducted a systematic investigation of feature attribution methods within the realm of electrocardiogram time series, focusing on R-peak, T-wave, and P-wave. Using a simulated dataset with modifications limited to the R-peak and T-wave, we evaluated the performance of various feature attribution techniques across two CNN architectures and explainability frameworks. Extending our analysis to real-world data revealed that, while feature attribution maps effectively highlight significant regions, their clarity is lacking, even under the simulated ideal conditions, resulting in blurry representations.

Extensive research has been conducted on time series and tabular data in the context of classification tasks, considering their distinct data domains. While feature extraction enables the transformation of series into tabular data, direct comparative comparisons between these data types remain scarce. Especially in the domain of medical data, such as electrocardiograms (ECGs), deep learning faces challenges due to its lack of easy and fast interpretability and explainability. However, these are crucial aspects for a wide and reliable adoption in the field. In our study, we assess the performance of XGBoost and InceptionTime on ECG features and time series data respectively. Our findings reveal that features extracted from ECG signals not only achieve competitive performance but also retain advantages during training and inference. These advantages encompass accuracy, resource efficiency, stability, and a high level of explainability.