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Suicidality and depression are major global health burdens, with over 700,000 suicide deaths annually and ~280 million people affected by major depressive disorder. Early risk prediction could support prevention, but traditional methods show limited accuracy. This PRISMA-compliant systematic review evaluated machine learning models for predicting suicidality and depression across electronic health records, social media, and wearable sensor data, focusing on performance, unimodal vs multimodal approaches, and ethical reporting. Searches of PubMed, PsycINFO, IEEE Xplore, arXiv, and ACM Digital Library identified eligible studies. EHR-based models showed AUROC 0.70–0.85 for suicide attempt prediction, social media models 0.70–0.80 for suicidal ideation, and wearable sensor models lower performance (0.65–0.75). Multimodal approaches improved performance by 5–10% over unimodal models. However, fewer than 20% of studies reported ethical considerations such as privacy, bias, or deployment safeguards. Overall, machine learning shows moderate-to-good predictive performance, with multimodal models performing best, but ethical reporting remains critically insufficient for clinical translation.

Clinicians often need rapid, evidence-based answers that integrate patient-specific electronic health records (EHRs) with clinical guidelines, but existing decision support tools are limited in real-time personalization. While large language models (LLMs) offer strong medical reasoning, they are prone to hallucinations and lack direct access to local EHR data, making them unsafe for standalone clinical use; meanwhile, traditional retrieval systems cannot synthesize coherent, context-aware responses. This paper proposes a retrieval-augmented generation (RAG) framework that combines dual-source retrieval from both institutional EHRs and clinical guideline databases. The system includes an EHR indexer, a guideline repository, a semantic retriever, an LLM-based generator, and a safety filter for hallucination mitigation. By grounding outputs in retrieved patient data and evidence-based recommendations, the model improves factual reliability, explainability, and clinical trustworthiness. Overall, the framework enables safe, real-time clinical question answering by integrating LLM reasoning with verified medical sources, with future validation planned on public EHR and guideline datasets.

Rare diseases collectively affect over 300 million people globally, yet individual conditions are often missed due to low clinician familiarity and non-specific presenting symptoms that mimic common disorders. Supervised machine learning requires large numbers of labeled examples for training, but rare diseases have too few diagnosed cases to develop condition-specific predictive models using traditional approaches. We propose a multimodal foundation model pretrained on 10 million de-identified electronic health records (EHRs) combining clinical notes and laboratory values for zero-shot rare disease diagnosis without requiring labeled training examples. The framework comprises four components: a clinical note encoder based on a large language model, a laboratory value encoder using a time-series transformer, a multimodal fusion module with cross-attention, and a zero-shot classifier that compares patient embeddings to disease descriptions. Pretraining on large-scale EHR data enables the model to learn general medical knowledge and disease patterns, allowing diagnosis of rare conditions by recognizing manifestations even when no labeled examples of that specific disease were used for training.

Long COVID (post-acute sequelae of SARS-CoV-2 infection, PASC) affects roughly 10–30% of COVID-19 survivors and is marked by persistent symptoms such as fatigue, cognitive dysfunction (“brain fog”), shortness of breath, loss of smell, and post-exertional malaise that can last for months or years, while its underlying biological mechanisms and validated diagnostic biomarkers remain unclear. The condition is highly heterogeneous, with patients showing different recovery patterns and no clearly defined clinical subtypes, and the scarcity of labeled datasets further limits the use of supervised machine learning methods for phenotyping. To address this, we propose a self-supervised contrastive multi-view learning framework that integrates three temporal data modalities—pre-infection electronic health records, acute-phase clinical and biomarker data (e.g., CRP, ferritin, D-dimer, lymphocyte counts), and post-acute symptom trajectories—using separate encoders and a shared latent space aligned through contrastive learning without requiring phenotype labels, followed by unsupervised clustering to identify potential subtypes. By exploiting the natural temporal linkage within each patient and contrasts across patients, this approach enables data-driven discovery of long COVID phenotypes, supports early prediction of subgroup membership, and may ultimately inform personalized treatment strategies, clinical trial design, and improved understanding of disease mechanisms.

Hypertension affects over 1.4 billion adults worldwide, and antihypertensive dose titration is a common but complex clinical decision. Although electronic health records contain longitudinal data on medication adjustments and blood pressure outcomes, determining optimal individualized dosing remains challenging due to confounding in observational data, where patients receiving higher doses often have worse baseline health. We propose a transformer-based model with causal attention masking to estimate counterfactual blood pressure outcomes under alternative dose regimens. The architecture ensures temporal validity by preventing information leakage from future events and encodes medication dose changes in a continuous representation. It includes a dose encoder, outcome predictor, and counterfactual contrastive loss to distinguish between competing treatment paths. This framework learns patient-specific dose–response relationships and enables personalized predictions for antihypertensive adjustments. While it supports individualized treatment planning from observational EHR data, prospective validation is still required before clinical deployment.