Seizures pose an imperative risk due to their frequency rising after the COVID-19 pandemic, with between 25-30% of patients going misdiagnosed or undiagnosed for seizure related conditions such as epilepsy. Electroencephalogram data can determine the location and timing of seizures to prevent future ones from occurring, but needs to be filtered to reduce interference and noise from biosignals. Current hospital research focuses on implementing common filters such as the bandpass filter to filter EEG data, resulting in highly distorted signals that create inaccurate diagnosis. The current research gap is the lack of an effective filter in hospitals to identify seizures and the absence of coding for a filter to be implemented in wearable seizure devices such as IoT devices. The objective of this research is to evaluate the optimal digital filtering technique for isolating brain wave frequencies while minimizing signal distortion and construct code utilizing the most optimal filter to build a usable EEG system for IoT seizure detection devices and hospitals. It was hypothesized that the Butterworth Bandpass filter will be the most effective at filtering signals because it is designed to have a flat response and doesn’t distort frequencies that are needed while having a steep roll-off to remove unnecessary frequencies that can cause misinterpretation of the data. This research was conducted on an anonymous pediatric EEG dataset from the Children’s Hospital in Boston, with the dataset containing 22 subjects, each contributing 9-42 files, with 23 EEG signals per file recorded during a seizure. All signals were sampled at 256 samples per second with 16-bit resolution to capture the detailed data. Specific signals were identified and tested in R using the commonly used Butterworth bandpass filer, the moving average filter, and the zero-phase filter. The Butterworth bandpass filter was tested in three orders (2, 4, and 6) to determine the effects of different orders on highlighting specific seizure episodes. The moving average filter was compared with the Butterworth bandpass filter and calculated with a window size of 5 data samples. The zero-phase filter was used in comparison with conventional filters utilized in hospitals to determine its impacts on time preservation relative to current usage. The results were quantified using noise removal comparison via power spectral density and signal-to-noise ratio. Power spectral density was calculated to translate the data from the time-domain to the frequency-domain. Signal power was defined to be between 1 and 40 Hz and noise power was considered to be over 40 Hz. Order 4 Butterworth bandpass filters were found to have a SNR of 25.5 dB, moving average had an SNR of 27.37 dB, and zero-phase filtering had an SNR of 79.06 dB, over 3 times greater than the SNR for the Butterworth bandpass filter. The research concludes by providing novel data that the most commonly used Butterworth filter is ineffective in seizure patients and finds that Moving Average and Zero-Phase filters should be used. The research also constructs code that can directly be implemented in wearable IoT devices using the Moving Average filter to utilize a low-energy filter that can analyze data on command. Moreover, the zero-phase filter can be used in diagnosis of seizures and to analyze episodes in patients, and the research constructs an accessible website utilizing the filter that can be rolled out in hospitals. Doctors can upload the data, adjust which frequencies they consider to be noise, and generate a resulting graph to analyze results in patients.
Electronic cigarettes, i.e., vaping, remain a major public-health concern, and Reddit posts provide naturalistic information about vaping cessation. In this study, we evaluated whether a set of refined prompting strategies and a multi-agent consensus workflow improve automated behavioral stage classification of vaping cessation posts on Reddit. First, clinicians annotated sample Reddit posts using the original five Transtheoretical Model (TTM) stages including (1) precontemplation, (2) contemplation, (3) preparation, (4) action, and (5) maintenance, plus what they added a new stage, namely "no stage" for this study. Ultimately, the first three stages were deterministically collapsed into pre-Action while Action, Maintenance, and No Stage were retained. We then evaluated six large language models (LLMs) including GPT-5, GPT-5.1, OpenAI o1, DeepSeek R1, DeepSeek V3, and LLaMA-4 Maverick, across five prompting strategies, consisting of a basic prompt, a basic prompt with TTM definitions, a basic prompt with a clinical annotator role, a structured prompt with role, definitions, and few-shot examples, and a chain-of-thought prompt, as well as a four-agent workflow, i.e., extractor, classifier, critic, and judge. Using 1,184 vaping-related Reddit posts, the best configuration is DeepSeek R1 with agentic adjudication, achieving macro-F1 = 0.862 and weighted F1 = 0.875. Structured prompts and agentic adjudication outperformed minimal prompts, showing that simplified TTM staging and clearer decision rules can support large-scale annotation of vaping cessation discourse.
The adoption of artificial intelligence (AI) in hospitals requires systematic assessment of institutional readiness. This study presents a domain-specific, readiness-oriented AI maturity model developed using a Design Science Research methodology. The model comprises six conceptual dimensions, namely Technology, Data, Strategy, People, Organization and Regulations. These are linked to 36 critical success factors and operationalized through 107 measurable items. All components are embedded in a web-based artefact that enables structured self-assessment, quantitative scoring and rule-based generation of improvement recommendations. The tool produces dimension-specific maturity profiles and identifies targeted areas for advancement based on Likert-scale responses. By translating abstract constructs into a practical evaluation instrument, this research offers a transparent and context-sensitive framework for guiding strategic AI development in hospitals. The artefact supports organizational reflection, benchmarking and capability building, contributing to the structured advancement of AI readiness in healthcare environments.
Drug development is notoriously costly and slow, with most candidates failing before reaching patients. Drug repurposing is the discovery of new therapeutic uses for existing compounds. It offers a faster and lower risk alternative by leveraging known safety profiles. Computational approaches, particularly machine learning, are increasingly applied to drug repurposing to accelerate candidate discovery. However, many machine learning approaches still lack transparency, limiting their adoption in translational research.We present RePurposeX, a mixed-initiative decision support system that integrates linked evidence, expert feedback mechanisms, and intuitive visual modules to support usability and trust in repurposing workflows. In a think-aloud study with 20 participants, RePurposeX achieved strong System Usability Scale (SUS = 84.3) and enabled diverse expert strategies, including visual-first, model validation, and clinical-priority approaches. Our findings yield design implications for health informatics, demonstrating how visual-first evidence, override mechanisms, and linked knowledge graphs improve usability and trust in AI predictions.
Recent advances in artificial intelligence have substantially improved histopathological image analysis. However, reliance on a single deep learning paradigm-such as convolutional neural networks (CNNs) or vision transformers (ViTs)-often constrains model robustness, amplifies correlated errors, and results in poorly calibrated confidence estimates. CNNs effectively capture fine-grained cellular morphology, while transformers encode global tissue context, yet neither alone adequately addresses the heterogeneity and uncertainty inherent in clinical pathology data. To address these limitations, we propose a reinforcement learning (RL)–guided adaptive fusion framework that integrates heterogeneous experts, including ConvNeXt, ResNet-50, ViT, and a hybrid classical–quantum model based on low-qubit parametrized quantum circuits (PQCs). Fusion is formulated as a confidence-aware decision-making process in which a policy network observes batch-level uncertainty statistics from each expert and learns adaptive fusion weights via policy-gradient optimization. This design enables dynamic weighting of expert predictions while directly optimizing a recall-sensitive clinical objective, thereby reducing false negatives in tumor detection. The PQC component is incorporated as a peer expert producing probabilistic predictions, contributing complementary quantum-derived decision patterns that enhance ensemble diversity rather than acting as a standalone classifier. Fusion is performed deterministically in probability space during inference to ensure stability and interpretability, while stochastic policy updates are confined to training. We evaluate the proposed framework on the BreaKHis breast cancer histopathology dataset (7,909 images from 82 patients across 40×, 100×, 200×, and 400× magnifications) using patient-level stratified splits. The fusion model achieves 99.4% accuracy, with macro-averaged precision, recall, and F1-score of 0.99, and malignant recall of 1.00 on the held-out test set. Ablation studies demonstrate that RL–based fusion performed better than the strongest individual backbone, with quantum-derived features providing higher performance.
