Abstract
In healthcare, diagnoses usually rely on physician expertise. However, complex cases may benefit from consulting similar past clinical reports cases. In this paper, we present MedLink (http://medlink.inesctec.pt), a tool that given a free-text medical report, retrieves and ranks relevant clinical case reports published in health conferences and journals, aiming to support clinical decision-making, particularly in challenging or complex diagnoses. To this regard, we trained two BERT models on the sentence similarity task: a bi-encoder for retrieval and a cross-encoder for reranking. To evaluate our approach, we used 10 medical reports and asked a physician to rank the top 10 most relevant published case reports for each one. Our results show that MedLink’s ranking model achieved NDCG@10 of 0.747. Our demo also includes the visualization of clinical entities (using a NER model) and the production of a textual explanation (using a LLM) to ease comparison and contrasting between reports. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Abstract
Alzheimer's disease (AD) places a profound global challenge, driven by its escalating prevalence and the multifaceted strain it places on individuals, families, and societies. Family caregivers (FCs), who are pivotal in supporting family members with AD, frequently endure substantial emotional, physical, and psychological demands. To better understand the determinants of family caregiving strain, this study employed machine learning (ML) to develop predictive models identifying factors that contribute to caregiver burden over time. Participants were evaluated across sociodemographic clinical, psychophysiological, and psychological domains at baseline (T1; N = 130), six months (T2; N = 114), and twelve months (T3; N = 92). Results revealed three distinct risk profiles, with the first focusing on T2 data, highlighting the importance of distress, forgiveness, age, and heart rate variability. The second profile integrated T1 and T2 data, emphasizing additional factors like family stress. The third profile combined T1 and T2 data with sociodemographic and clinical features, underscoring the importance of both assessment moments on distress at T2 and forgiveness at T1 and T2, as well as family stress at T1. By employing computational methods, this research uncovers nuanced patterns in caregiver burden that conventional statistical approaches might overlook. Key drivers include psychological factors (distress, forgiveness), physiological markers (heart rate variability), contextual stressors (familial dynamics, sociodemographic disparities). The insights revealed enable early identification of FCs at higher risk of burden, paving the way for personalized interventions. Such strategies are urgently needed as AD rates rise globally, underscoring the imperative to safeguard both patients and the caregivers who support them.