Intelligent inquiry method and device and electronic equipment

By calculating the posterior probability and uncertainty entropy of candidate diseases using the Bayesian algorithm and combining it with information gain scoring, the consultation path is dynamically adjusted, solving the problem of balancing efficiency and completeness in existing intelligent consultation systems and realizing an efficient and standardized consultation process.

CN121922334APending Publication Date: 2026-04-24SHANGHAI SUCCESSFULL TELECOMM TECH CO LTD
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Patent Information

Application Number
CN202511986761.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-25
Publication Date
2026-04-24

AI Technical Summary

Technical Problem

Existing intelligent consultation systems struggle to effectively balance consultation efficiency and completeness. They cannot dynamically adjust their focus based on the patient's actual complaints and lack reasonable objective termination conditions, resulting in low consultation efficiency or incomplete information collection.

Method used

The Bayesian algorithm is used to calculate the posterior probability and uncertainty entropy of candidate diseases. Combined with a preset entropy threshold, the termination condition of the consultation is determined. The next round of consultation questions is selected through information gain scoring, so as to achieve dynamic adjustment of the consultation path.

Benefits of technology

It improves consultation efficiency while ensuring consultation completeness, effectively balancing consultation efficiency and consultation completeness, reducing the number of invalid consultations, and generating medical records that conform to clinical standards.

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Abstract

The invention discloses an intelligent inquiry method and device and electronic equipment, and relates to the technical field of intelligent inquiry and the technical field of data processing.The intelligent inquiry method comprises the steps that symptom information provided by a patient in the current round of inquiry is obtained, and the symptom information serves as latest symptom information; generating a current candidate disease set based on the latest symptom information; for each candidate disease in the current candidate disease set, calculating the posterior probability of the candidate disease after the current round of inquiry based on the latest symptom information by adopting a Bayesian algorithm; based on the posterior probability of each candidate disease in the current candidate disease set, the uncertainty entropy of the current candidate disease set is calculated, and the uncertainty entropy represents the uncertainty of the disease diagnosis result of the current candidate disease set; and determining whether to end the intelligent inquiry or not based on a size relationship between the uncertainty entropy and a preset entropy threshold value. By adopting the scheme, the inquiry efficiency and the inquiry integrity in intelligent inquiry are effectively balanced.
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