The rapid advancement of technology has created new opportunities to improve the accuracy and efficiency of medical diagnoses, treatments, and overall patient care in several medical domains, including mental health. One promising novel approach is federated learning, a machine learning approach that allows multiple devices to train a shared model without exchanging raw data. Instead of centralizing the data in one location, each device or machine holds a portion of the data and collaborates with other devices to update the shared model. In this way, federated learning enables training on more extensive and diverse datasets than would be possible with centralized training while preserving the privacy and security of individual data. In the mental health domain, federated learning has the potential to improve mental disorders’ detection, diagnosis, and treatment. By pooling data from multiple sources while maintaining patient privacy by keeping data secure and ensuring that they are not used for unauthorized purposes. This literature survey reviews recent studies that have exploited federated learning in the psychiatric domain, covering multiple data resources and different machine-learning techniques. Furthermore, we formulate the gap in the current methodologies and propose new research directions.

The incidences of mental health illnesses, such as suicidal ideation and depression, are increasing, which highlights the urgent need for early detection methods. There is a growing interest in using natural language processing (NLP) models to analyze textual data from patients, but accessing patients’ data for research purposes can be challenging due to privacy concerns. Federated learning (FL) is a promising approach that can balance the need for centralized learning with data ownership sensitivity. In this study, we examine the effectiveness of FL models in detecting depression by using a simulated multilingual dataset. We analyzed social media posts in five different languages with varying sample sizes. Our findings indicate that FL achieves strong performance in most cases while maintaining clients’ privacy for both independent and non-independent client partitioning.

Crying is a newborn’s main way of communicating. Despite their apparent similarity, newborn cries are physically generated and have distinct characteristics. Experienced medical professionals, nurses, and parents are able to recognize these variations based on their prior interactions. Nonetheless, interpreting a baby’s cries can be challenging for carers, first-time parents, and inexperienced paediatricians. This paper uses advanced deep learning techniques to propose a novel approach for baby cry classification. This study aims to accurately classify different cry types associated with everyday infant needs, including hunger, discomfort, pain, tiredness, and the need for burping. The proposed model achieves an accuracy of 98.33%, surpassing the performance of existing studies in the field. IoT-enabled sensors are utilized to capture cry signals in real time, ensuring continuous and reliable monitoring of the infant’s acoustic environment. This integration of IoT technology with deep learning enhances the system’s responsiveness and accuracy. Our study highlights the significance of accurate cry classification in understanding and meeting the needs of infants and its potential impact on improving infant care practices. The methodology, including the dataset, preprocessing techniques, and architecture of the deep learning model, is described. The results demonstrate the performance of the proposed model, and the discussion analyzes the factors contributing to its high accuracy.

Breast cancer is the second leading cause of cancer-related mortality among women. Early detection enables timely treatment, improving survival outcomes. This paper presents a comparative evaluation of federated learning (FL) frameworks for multiclass breast cancer classification using ultrasound images drawn from three datasets: BUSI, BUS-UCLM, and BCMID, which include 600, 38, and 323 patients, respectively. Five state-of-the-art networks were tested, with MobileNet, ResNet and InceptionNet identified as the most effective for FL deployment. Two aggregation strategies, FedAvg and FedProx, were assessed under varying levels of data heterogeneity in two and three client settings. Results from experiments indicate that the FL models outperformed local and centralized training, bypassing the adverse impacts of data isolation and domain shift. In the two-client federations, FL achieving up to 8% higher accuracy and almost 6% higher macro-F1 scores on average that local and centralized training. FedProx on MobileNet maintained a stable performance in the three-client federation with best average accuracy of 73.31%, and macro-F1 of 67.3% despite stronger heterogeneity. Consequently, these results suggest that the proposed multiclass model has the potential to support clinical workflows by assisting in automated risk stratification. If deployed, such a system could allow radiologists to prioritize high-risk patients more effectively. The findings emphasize the potential of federated learning as a scalable, privacy-preserving infrastructure for collaborative medical imaging and breast cancer diagnosis.

The uniqueness of each human genetic structure motivated the shift from the current practice of medicine to a more tailored one. This personalized medicine revolution would not be possible today without the genetics data collected from genome-wide association studies (GWASs) that investigate the relation between different phenotypic traits and single-nucleotide polymorphisms (SNPs). The huge increase in the literature publication space imposes a challenge on the conventional manual curation process which is becoming more and more expensive. This research aims at automatically extracting SNP associations of any given disease and its reported statistical significance (P-value) and odd ratio as well as cohort information such as size and ethnicity. Our evaluation illustrates that SNPcurator was able to replicate a large number of SNP-disease associations that were also reported in the NHGRI-EBI Catalog of published GWASs. SNPcurator was also tested by eight external genetics experts, who queried the system to examine diseases of their choice, and was found to be efficient and satisfactory. We conclude that the text-mining-based system has a great potential for helping researchers and scientists, especially in their preliminary genetics research. SNPcurator is publicly available at http://snpcurator.science.uu.nl/. Database URL: http://snpcurator.science.uu.nl/