Obstructive sleep apnea-hypopnea syndrome (OSAHS) [1] Our key contributions are as follows: (1) Introducing VTA-affects about 27% of adults [2], causing poor sleep, daytime OSAHS, a multimodal framework for diagnosing OSAHS dysfunction, and higher risks of cardiovascular diseases and diabetes severity by combining visual and language data, and using [3]. The standard diagnostic method, polysomnography a pre-trained language model to extract key information from (PSG) [4], is complex, costly, and uncomfortable, requiring basic physiological data for improved classification accuracy; multi-channel monitoring (EEG, ECG, heart rate [5]) and (2) Developing a visual encoder that focuses on specific facial trained technicians (Figure 1). Data-driven methods for automated features associated with OSAHS, employing attention mesh OSAHS diagnosis can improve efficiency and reduce and stochastic gates for better clinical decision alignment; (3) costs. Facial features like a flat nasal bridge, wide jawbone, Implementing a data pre-processing strategy to handle imbalanced thick neck, and mandibular retrognathia correlate with OSAHS samples and ordinal classification, using randomOver-severity [6], providing visual indicators of airway obstruction Sampler (ROS) [17] and an ordinal regression loss function and sleep disturbances. Deep learning can analyze these features [18] to enhance accuracy and robustness; (4) Demonstrating for early diagnosis and personalized treatment.