Background:

The Tian Tan Hospital, affiliated with Capital Medical University, is ranked first nationwide in both neurology and neurosurgery. Specializing in the diagnosis and treatment of neurological diseases, the hospital handles more than 5,000 outpatient visits daily, with a large proportion of patients coming from outside the region. Issues such as incorrect appointments or patients not receiving the appropriate care are widespread, particularly due to a lack of understanding of specialized and sub-specialized services. This leads to inefficiencies in outpatient consultations. Auxiliary examination departments are overcrowded, with long waiting times for appointments, and the service model urgently requires optimization.

Solutions:

Establishing an intelligent triage theory based on neural networks and Bayesian decision-making, along with a decision tree model.

Achieving rapid learning and accurate triage for neurological diseases, and establishing an intelligent triage system.

Key Technologies:

Machine learning for the knowledge of neurological diseases.

Clinical experts assist in annotating and reviewing medical knowledge.

Complete the learning of electronic medical records to enhance model accuracy and continuous optimization.