Data scarcity and class imbalance are common occurrences in healthcare datasets and have adverse effect on classification performance of machine learning models. Artificial data generation in various applications can be used to handle these challenges. This paper proposes Guided Evolutionary Synthesizer (GES), a tool derived from principles of genetic algorithm, and designed to generate artificial healthcare time series data for improving classification performance of machine learning models. We conducted a series of promising and confirmatory preliminary experiments performance using traditional machine learning and non-residual convolutional neural network models to evaluate the effectiveness of GES synthetic on data classification. Motivated by the preliminary results, we conducted eight detailed experiments using residual neural network (ResNet), which demonstrated the flexibility of GES and effectiveness of GES synthetic data in improving classification performance of deep neural networks. These experiments use GES generated electrocardiogram (ECG) and electroencephalogram (EEG) datasets. Our findings show that models trained with GES synthetic data i) performed better than models trained with regular perturbed data, ii) had better diagnostic performance for both EEG and ECG datasets, iii) achieved better performance with lower training volume for EEG dataset, iv) eased learning biases seen in literature for ECG normal sinus and premature ventricular complex rhythms, and v) had better classification outcomes than comparable related work models trained with similar ECG classes.

The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2019 PhysioNet Challenge. It has around 40,000 Intensive Care Unit (ICU) patients with 40 physiological variables. Using Long Short-Term Memory (LSTM) as the representative black-box machine learning model, we adapted the Multi-set Classifier to globally interpret the black-box model for concepts it learned about sepsis. To identify relevant features, the result is compared against: (i) features used by a computational sepsis expert, (ii) clinical features from clinical collaborators, (iii) academic features from literature, and (iv) significant features from statistical hypothesis testing. Random Forest was found to be the computational sepsis expert because it had high accuracies for solving both the detection and early detection, and a high degree of overlap with clinical and literature features. Using the proposed interpretation mechanism and the dataset, we identified 17 features that the LSTM used for sepsis classification, 11 of which overlaps with the top 20 features from the Random Forest model, 10 with academic features and 5 with clinical features. Clinical opinion suggests, 3 LSTM features have strong correlation with some clinical features that were not identified by the mechanism. We also found that age, chloride ion concentration, pH and oxygen saturation should be investigated further for connection with developing sepsis. Interpretation mechanisms can bolster the incorporation of state-of-the-art machine learning models into clinical decision support systems, and might help clinicians to address the issue of early sepsis detection. The promising results from this study warrants further investigation into creation of new and improvement of existing interpretation mechanisms for black-box models, and into clinical features that are currently not used in clinical assessment of sepsis.

Automation increasingly shapes modern society, requiring artificial intelligence (AI) systems to not only perform complex tasks but also provide clear, actionable explanations of their decisions, especially in high-stakes domains. However, most contemporary AI systems struggle to explain their runtime operations in specific instances, limiting their applicability in contexts demanding stringent outcome justification. Existing approaches have attempted to address this challenge but often fall short in terms of contextual relevance, human cognitive alignment, or scalability. This paper introduces System-of-Systems Machine Learning (SoS-ML) as a novel framework to advance explainable artificial intelligence (XAI) by addressing the limitations of current methods. Drawing from insights in philosophy, cognitive science, and social sciences, SoS-ML seeks to integrate human-like reasoning processes into AI, framing explanations as contextual inferences and justifications. The research demonstrates how SoS-ML addresses key challenges in XAI, such as enhancing explanation accuracy and aligning AI reasoning with human cognition. By leveraging a multi-agent, modular design, SoS-ML encourages collaboration among machine learning models, leading to more transparent, context-aware systems. The framework’s ability to generalize across domains is demonstrated through experiments on the Pima Indian Diabetes dataset and pie chart image-to-text interpretation, showcasing its transformative potential in improving both model accuracy and explainability. The findings emphasize SoS-ML’s role in advancing responsible AI, particularly in high-stakes environments where interpretability and social accountability are paramount.

Risk and uncertainty play a vital role in almost every significant economic decision, and an individual’s propensity to make riskier decisions also depends on various circumstances. This article aims to investigate the effects of social and economic covariates on an individual’s willingness to take general risks and extends the scope of existing works by using quantitative measures of risk-taking from the GPS and Gallup datasets (in addition to the qualitative measures used in the literature). Based on the available observed risk-taking data for one year, this article proposes a semi-supervised machine learning-based approach that can efficiently predict the observed risk index for those countries/individuals for years when the observed risk-taking index was not collected. We find that linear models are insufficient to capture certain patterns among risk-taking factors, and non-linear models, such as random forest regression, can obtain better root mean squared values than those reported in past literature. In addition to finding factors that agree with past studies, we also find that subjective well-being influences risk-taking behavior.

Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g., pharmaceutical advertising campaigns). However, currently there is no comprehensive computational or machine learning framework that allows systematic studies of substance abuse and its factors with majority of the works using subjective surveys questionnaires and focusing on classification techniques. Lacking standardized methods and/or measures to prescribe medication and to study substance abuse makes it difficult to advance through collective research efforts. Thus, in this paper, we propose to test the feasibility of developing a, objective substance effect index, SEI, that can measure the tendency of an individual towards substance abuse. To that end, we combine the benefits of Electronics Medical Records (EMR) with machine learning technology by defining SEI as a function of EMR data and using logistics regression to obtain a closed form expression for SEI. We conduct various evaluations to validate the proposed model, and the results show that further work towards the development of SEI will not only provide researchers with standard computational measure for substance abuse, but may also allow them to study certain attribute interactions to gain further insights into substance abuse tendencies.