NeoCareNet: A Deep Learning Framework for Assessing Legal Accessibility to Neonatal and Postnatal Health Care Services

Neonatal and postnatal healthcare accessibility faces crucial barriers because of intricate legal systems along with varied policy implementations. The authors developed NeoCareNet as a deep learning framework which evaluates legal access to neonatal and postnatal healthcare by processing legal documents including policies and court decisions and healthcare regulations. Through the combination of Transformer-based models (LegalBERT) and CNN-BiLSTM hybrid structures and Graph Neural Networks (GNNs) NeoCareNet provides effective interpretation of legal documents along with assessment of their healthcare accessibility impact. The model used a wide range of legal case records alongside healthcare policy documents and neonatal care accessibility reports when being trained. The system attained 94.5% accuracy in legal accessibility classification while achieving superior results beyond traditional methods which included Logistic Regression at 78.5% and Random Forest at 82.1%. Both effectiveness and robustness were strengths of NeoCareNet which showed a 5.5% decrease in accuracy when faced with major perturbations. The analytical framework demonstrated through data analysis that access to legal frameworks shows a direct negative relationship with newborn death rates thus proving the importance of legal frameworks for healthcare improvements. This research demonstrates deep learning technology provides policymakers and healthcare professionals with a powerful tool for legal policy evaluation and healthcare accessibility measurement to determine and execute effective reforms.