An Advanced Transfer Learning Framework for Early Prediction of Mental Health Disabilities with Deep Feature Extraction

Predictive mental disability is an essential factor in maximizing the outcome of the patient by facilitating early intervention and better management of emergencies of mental illness. Identification of high risk of severe mental illness by the patient at an early phase can prevent hospitalization risk, suicide, and other adverse effects to a great degree. However, existing approaches to forecasting mental health disabilities are marred by a chain of defects that involve low accuracy, failure to process sequential EHR data sequentially well, and low generalizability across patient populations of different heterogeneity. These defects reduce the performance of existing models in forecasting effectively patients at risk before an episode of mental health crisis. In response to such problems, This work proposes a new method employing a Bidirectional Long Short-Term Memory (BiLSTM) network and a transformer-based model to forecast mental health disability. Implementation of the BiLSTM network and the transformer-based model enables the model to accurately forecast the incidence of mental health disabilities at the early stage and provide meaningful information to clinicians so that they can maximize care and prevent crisis development. The novelty of the new method is that it uses BiLSTM and transformer networks to manage temporal and feature-based relationships among EHR data. The ability of the model to learn from and manage complex, high-dimensional data makes it outperform the existing methods based on prediction accuracy, precision, recall, and F1-score. The study results validate the effectiveness of the suggested framework, showing that it has the potential to transform the mental health disability prediction and crisis intervention status.