The invention discloses a
drug identification and
curative effect analysis method based on a
large model and multi-round treatment data. Comprising the following steps: firstly, giving multiple rounds of doctor seeing records and a
disease diagnosis and treatment
knowledge base of a patient, and carrying out preprocessing and candidate
medicine extraction on text contents to obtain an initial
medicine set which is being taken by the patient; thirdly, identifying the
drug state to remove invalid drugs, and outputting a
drug standard category in combination with
disease diagnosis and treatment
knowledge base information and
large model reasoning ability; if the current doctor-seeing information is lost, automatically
backtracking historical doctor-seeing records; through semantic
verification and fact check, an accurate and credible recognition result is generated; and finally, according to an identification result, automatically constructing a real world research
queue so as to carry out drug
curative effect analysis. The method has high accuracy and high clinical adaptability, and
large model illusion is avoided. The method is suitable for scenes of
chronic disease management, clinical auxiliary
decision making, intelligent health follow-up visit,
curative effect analysis and the like.