System and method for recommendation of over-the-counter products

Abstract

The present invention provides a system and a method for recommendation of over-the-counter products. The system comprises: a data storage module configured to store medical data of a plurality of patients, wherein the medical data comprises symptom combinations and information of a plurality of drug combinations corresponding to the symptom combinations; a data processing module connected with the data storage module and configured to perform processing and obtain one proportion of each drug combination corresponding to one patient's symptom combination in the total drug combinations corresponding to the symptom combinations according to the medical data; and a data recommendation module connected with the data processing module and configured to take a processing result of one proportionof each drug combination in the total drug combinations corresponding to the symptom combinations as drug recommendation information. The system and the method for recommendation of over-the-counterproducts perform analysis of medical data to obtain one proportion of each drug combination in the total drug combinations according to the historical record information of the drug combination selected by the patient so as to recommend the best matching drug combination for the patients.

Description

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AI Technical Summary

Benefits of technology

This technology helps healthcare providers recommend which drugs are best suited based on their history with similar medications being used together. By analyzing this database from patients' past experiences, it can help clinicians make informed decisions about how much medicine they should use next time.

Problems solved by technology

This patented technology describes how current methods used for recommending new medicine involve comparing actual patient conditions (such as pain) against predetermined criteria set forth in advance. These techniques have limitations due to factors like lacking knowledge about specific diseases associated with these treatments, which makes them difficult to use effectively outside physician offices. Additionally, some treatment options require different doses depending upon what type of disease an individual's condition affects. Existing system also requires manual input during trial periods when selecting appropriate therapy, further adding complexity to providing accurate results through analysis of clinic data alone.

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