Multilingual medical speech recognition datasets are critical resources for building robust speech recognition systems that can reduce documentation workload and costs in healthcare while supporting clinical communication across diverse languages. In this study, we introduce the MultiMed Translation and Speech Synthesis (MultiMed-TSS) dataset, an extension of the original MultiMed dataset, where source languages were translated into multiple target languages in a many-to-many manner and paired with speech synthesized using a Text-toSpeech (TTS) model. The dataset comprises$\mathbf{1 6 5, 5 3 7}$speech-text pairs totaling 472 hours of audio across six languages: Chinese, English, French, German, Vietnamese, and Korean. To assess the effect of augmentation, we compared models fine-tuned with and without the TSS data. We observed that Vietnamese showed the largest gains while improvements in the other languages were modest. We have publicly released the MultiMed-TSS dataset at https://huggingface.co/datasets/SehwanMoon/MultiMed-TSS, to facilitate further research on medical speech recognition.

We propose an end-to-end Heart Rate Variability (HRV)-aware multi-task learning (MTL) framework for non-contact stress recognition from facial videos. Unlike approaches that rely on engineered rPPG features or separate preprocessing pipelines, our method jointly performs HRV regression and stress classification within a unified framework. Predicted HRV indices (SDNN, RMSSD, pNN50) are used as auxiliary signals to enhance both stress recognition accuracy and physiological interpretability. Experiments on the UBFC-Phys dataset demonstrate that our HRV-aware MTL model achieves 0.86 accuracy in 3-class classification and 0.94 in 2-class classification, comparable-to-better than a single-task baseline. While HRV regression alone yields only moderate correlation with ground truth, incorporating HRV as an auxiliary feature stabilizes classification and enhances physiological interpretability. These results highlight the effectiveness of combining physiological regression with psychological classification, paving the way for interpretable, non-contact stress assessment systems.

Recent advancements in large language models (LLMs) have opened new avenues in psychological counseling. This study leverages LLMs to develop chatbots capable of conducting empathetic and personalized counseling sessions by applying various prompt engineering techniques, including zero-shot, few-shot, meta-learning, Chain of Thought, and our newly developed Empathetic Meta-Chain (EMC) method. The EMC method demonstrated superior performance in empathy, response accuracy, interaction continuity, fluency, and understanding, as confirmed by expert evaluations. By integrating advanced empathetic strategies, the EMC chatbot significantly enhances its ability to support users' mental well-being through natural and engaging counseling interactions. These findings highlight the potential of LLM-based counseling chatbots to serve as effective tools in mental health support.

Classifying depressive symptoms using actigraphy data presents challenges, as highlighted by previous research. In this study, we constructed the TREND-P dataset, comprising 3,313 subjects from Chonnam National University Hospital (2021-2023), to analyze the impact of varying depressive symptom ratios. The dataset includes actigraphy, demographic, and HRV (Heart Rate Variability) data.We assessed the classification performance on two tasks: a challenging task differentiating subjects with PHQ-9 scores below 5 from those with scores of 5 or higher, and an easier task distinguishing subjects with PHQ-9 scores of 10 or higher from those below 5, excluding mild symptoms.Our results reveal that integrating multimodal data (actigraphy, HRV, and demographic data) slightly enhanced performance, particularly in easier tasks, with improvements. These findings underscore the potential of multimodal data integration in improving the classification of depressive symptoms. However, there are still performance limitations in the more challenging task of distinguishing between subjects with PHQ-9 scores below 5 and those with scores of 5 or higher.

Recently, there has been a significant amount of research conducted on classifying depression by extracting features from voice data. In this study, we enhanced the dataset by segmenting the voice data into 4-second intervals, followed by the extraction of voice features using MFCC (Mel Frequency Cepstral Coefficients) and Mel-spectrogram. After extracting these voice features, we compared the performance depending on the models and found that using MFCC features and classifying depression with the XGBoost model yielded the best performance. Looking forward, we aim to enhance the model’s performance by utilizing multimodal data, including voice, video, and text data.

Monitoring human behavior through wearable devices has potential in psychiatry. Among them, actigraphy data has been used to classify depression and detect depressive symptoms. We aim to collect a larger number of data to measure classification performance. This study evaluates the performance of classifying depressive symptoms solely on actigraphy data using both public (n=1549) and collected (n=3145) datasets. We found that there are challenges in classifying depressive symptoms from actigraphy data.

MOTIVATION: Accurate diagnostic classification and biological interpretation are important in biology and medicine, which are data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy of clinical phenotypes, and more comprehensive analyses for predicting the prognosis of complex diseases are required. RESULTS: Here, we propose a novel multi-task attention learning algorithm for multi-omics data, termed MOMA, which captures important biological processes for high diagnostic performance and interpretability. MOMA vectorizes features and modules using a geometric approach and focuses on important modules in multi-omics data via an attention mechanism. Experiments using public data on Alzheimer's disease and cancer with various classification tasks demonstrated the superior performance of this approach. The utility of MOMA was also verified using a comparison experiment with an attention mechanism that was turned on or off and biological analysis. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/dmcb-gist/MOMA. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.