BioMedInformatics, Volume 5, Issue 3 (September 2025) – 8 articles

Background: Social media represents a unique opportunity to investigate the perspectives of people with eating disorders at scale. One forum alone, r/EatingDisorders, now has 113,000 members worldwide. In less than a day, where a manual analysis might sample a few dozen items, automatic classification using large language models (LLMs) can analyze thousands of posts. Methods: Here, we compare multiple strategies for invoking an LLM, including ones that include examples (few-shot) and annotation guidelines, to classify eating disorder content across 14 predefined themes using Llama3.1:8b on 6850 social media posts. In addition to standard metrics, we calculate four novel dimensions of classification quality: a Category Divergence Index, confidence scores (overall model certainty), focus scores (a measure of decisiveness for selected subsets of themes), and dominance scores (primary theme identification strength). Results: By every measure, invoking an LLM without extensive guidance and examples (zero-shot) is insufficient. Zero-shot had worse mean category divergence (7.17 versus 3.17). Whereas, few-shot yielded higher mean confidence, 0.42 versus 0.27, and higher mean dominance, 0.81 versus 0.46. Overall, a few-shot approach improved quality measures across nearly 90% of predictions. Conclusions: These findings suggest that LLMs, if invoked with expert instructions and helpful examples, can provide instantaneous high-quality annotation, enabling automated mental health content moderation systems or future clinical research. Full article

Lung-cancer incidence is projected to rise by 50% by 2035, underscoring the need for accurate yet accessible risk-stratification tools. We trained a Bayesian neural network on 300 annotated chest-CT scans from the public LIDC–IDRI cohort, integrating clinical metadata. Hamiltonian Monte-Carlo sampling (10 000 posterior draws) captured parameter uncertainty; performance was assessed with stratified five-fold cross-validation and on three independent multi-centre cohorts. On the locked internal test set, the model achieved 99.0% accuracy, AUC = 0.990 and macro-F1 = 0.987. External validation across 824 scans yielded a mean AUC of 0.933 and an expected calibration error $<0.034$, while eliminating false positives for benign nodules and providing voxel-level uncertainty maps. Uncertainty-aware Bayesian deep learning delivers state-of-the-art, well-calibrated lung-cancer risk predictions from a single CT scan, supporting personalised screening intervals and safe deployment in clinical workflows. Full article

Technological advancements and AI-based research have significantly influenced our daily lives. Human activity recognition (HAR) is a key area at the intersection of various AI technologies and application domains. In this study, we present our novel time series classification approach for monitoring the physical behaviors of the elderly and patients. This approach, which integrates supervised and unsupervised methods with generative models, has been validated for HAR, showing promising results. Our method was specifically adapted for healthcare and surveillance applications, enhancing the classification of physical behaviors in the elderly. The hybrid approach proved its effectiveness on the HAR70+ dataset, surpassing traditional recurrent convolutional network-based approaches. We further evaluated the surveillance system for the elderly (Surv-Sys-Elderly) model on the HARTH and HAR70+ datasets, achieving an accuracy of 94,3% on the HAR70+ dataset for recognizing elderly behaviors, highlighting its robustness and suitability for both clinical and domestic environments. Full article

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article

Food pattern recognition plays a crucial role in modern healthcare by enabling automated dietary monitoring and personalised nutritional interventions, particularly for vulnerable populations with complex dietary needs. Current food recognition systems struggle to balance high accuracy with interpretability and computational efficiency when analysing complex meal compositions in real-world settings. We developed a novel self-explaining neural architecture that integrates specialised attention mechanisms with temporal modules within a streamlined framework. Our methodology employs hierarchical feature extraction through successive convolution operations, multi-head attention mechanisms for pattern classification, and bidirectional LSTM networks for temporal analysis. Architecture incorporates self-explaining components utilising attention-based mechanisms and interpretable concept encoders to maintain transparency. We evaluated our model on the FOOD101 dataset using 5-fold cross-validation, ablation studies, and comprehensive computational efficiency assessments. Training employed multi-objective optimisation with adaptive learning rates and specialised loss functions designed for dietary pattern recognition. Experiments demonstrate our model’s superior performance, achieving 94.1% accuracy with only 29.3 ms inference latency and 3.8 GB memory usage, representing a 63.3% parameter reduction compared to baseline transformers. The system maintains detection rates above 84% in complex multi-item recognition scenarios, whilst feature attribution analysis achieved scores of 0.89 for primary components. Cross-validation confirmed consistent performance with accuracy ranging from 92.8% to 93.5% across all folds. This research advances automated dietary analysis by providing an efficient, interpretable solution for food recognition with direct applications in nutritional monitoring and personalised healthcare, particularly benefiting vulnerable populations who require transparent and trustworthy dietary guidance. Full article

Background: Anemia, a common health disorder affecting populations globally, demands timely and accurate diagnosis for treatment to be effective. The aim of this paper is to detect and classify four types of anemia: hgb, iron-deficiency, folate-deficiency, and B12-deficiency anemia. Methods: This paper proposes an ontology-enhanced machine learning (ML) framework to classify types of anemia from CBC data obtained from Kaggle, which contains 15,300 patient records. It evaluates the effects of classical versus deep classifiers on imbalanced and oversampled training samples. Tests include KNN, SVM, DT, RF, CNN, CNN+SVM, CNN+RF, and XGBoost. Another interesting contribution is the use of ontological reasoning via SPARQL queries to semantically enrich clinical features with categories like “Low Hemoglobin” or “Macrocytic MCV”. These semantic features were then used in both classical (SVM) and deep hybrid models (CNN+SVM). Results: Ontology-enhanced and CNN hybrid models perform competitively when paired with ROS or ADASYN, but their performance degrades significantly on the original dataset. There were tremendous performance gains with ontology-enhanced models in that Onto-CNN+SVM achieved an F1-score (1.00) for all the four types of anemia under ROS sampling, while Onto-SVM exhibited more than 20% improvement in F1-scores for minority categories like folate and B12 when compared to baseline models, except XGBoost. Conclusions: Ontology-driven knowledge coalescence has been shown to improve classification results; however, XGBoost consistently outperformed all other classifiers across all data conditions, making it the most robust and reliable model for clinically relevant decision-support systems in anemia diagnosis. Full article

Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by identifying the most relevant genetic and clinical biomarkers while demonstrating clinical utility. Methods: A dataset from The Cancer Genome Atlas (TCGA) containing 23 features was analyzed using an integrative approach combining Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and SHapley Additive exPlanations (SHAP) for feature selection. The refined feature set was used to train four machine learning models: Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. Comprehensive evaluation included class distribution analysis, calibration assessment, and decision curve analysis. Results: The feature selection approach identified 13 key predictors, including IDH1, TP53, ATRX, PTEN, NF1, EGFR, NOTCH1, PIK3R1, MUC16, CIC mutations, along with Age at Diagnosis and race. XGBoost achieved the highest AUC (0.93), while Logistic Regression recorded the highest testing accuracy (88.09%). Class distribution analysis revealed excellent GBM detection (Average Precision 0.840–0.880) with minimal false negatives (5–7 cases). Calibration analysis demonstrated reliable probability estimates (Brier scores 0.103–0.124), and decision curve analysis confirmed substantial clinical utility with net benefit values of 0.36–0.39 across clinically relevant thresholds. Conclusions: The integration of feature selection techniques with machine learning models enhances diagnostic precision, interpretability, and clinical utility in glioma classification, providing a clinically ready framework that bridges computational predictions with evidence-based medical decision-making. Full article

Background: Chronic diseases significantly burden healthcare systems due to the need for long-term treatment. Early diagnosis is critical for effective management and minimizing risk. The current traditional diagnostic approaches face various challenges regarding efficiency and cost. Digitized healthcare demonstrates several opportunities for reducing human errors, increasing clinical outcomes, tracing data, etc. Artificial Intelligence (AI) has emerged as a transformative tool in healthcare. Subsequently, the evolution of Generative AI represents a new wave. Large Language Models (LLMs), such as ChatGPT, are promising tools for enhancing diagnostic processes, but their potential in this domain remains underexplored. Methods: This study represents the first systematic evaluation of ChatGPT’s performance in chronic disease prediction, specifically targeting heart disease and diabetes. This study compares the effectiveness of zero-shot, few-shot, and CoT reasoning with feature selection techniques and prompt formulations in disease prediction tasks. The two latest versions of GPT4 (GPT-4o and GPT-4o-mini) are tested. Then, the results are evaluated against the best models from the literature. Results: The results indicate that GPT-4o significantly beat GPT-4o-mini in all scenarios regarding accuracy, precision, and F1-score. Moreover, a 5-shot learning strategy demonstrates superior performance to zero-shot, few-shot (3-shot and 10-shot), and various CoT reasoning strategies. The 5-shot learning strategy with GPT-4o achieved an accuracy of 77.07% in diabetes prediction using the Pima Indian Diabetes Dataset, 75.85% using the Frankfurt Hospital Diabetes Dataset, and 83.65% in heart disease prediction. Subsequently, refining prompt formulations resulted in notable improvements, particularly for the heart dataset (5% performance increase using GPT-4o), emphasizing the importance of prompt engineering. Conclusions: Even though ChatGPT does not outperform traditional machine learning and deep learning models, the findings highlight its potential as a complementary tool in disease prediction. Additionally, this work provides value by setting a clear performance baseline for future work on these tasks Full article