Abstract ▼ AI-Helper

Objectives : Clinical data in traditional medicine, such as Korean medicine, traditional Chinese medicine have a long history of accumulating evidence and these rich data are recorded in classic literature. We have conducted a study of developing an algorithm that support clinical diagnosis with com...

Objectives : Clinical data in traditional medicine, such as Korean medicine, traditional Chinese medicine have a long history of accumulating evidence and these rich data are recorded in classic literature. We have conducted a study of developing an algorithm that support clinical diagnosis with composing both users knowledge and data obtained from literature. In order to define necessary information and required steps in diagnosis procedure, we have established a clinical diagnostic procedure including a step of collecting patients symptoms, a step of determining candidates, a step of diagnostic decisions, a step of deciding of treatment and a step of adjusting medicinal treatment. Methods : Our study have been based on the following premises. 1. Using data obtained from literature contributes to accurate diagnosis 2. Displaying the data before users request contributes to accurate conclusion. Displaying before users request enable users to recognize their overlooking a fact on purpose or not. 3. Checking symptoms that are commonly accompanied with a group of diseases that accompany symptoms appealed by a patient contributes to accurate conclusion. These symptoms are worthy of checking. 4. Comparing more than two candidates contributes to accurate conclusion. Users can compare their accompanied symptoms with patients symptoms and this helps users to make a decision. Results : Based on the above premises, we have developed an literature based algorithm to provide various functions, such as recommending symptoms to check, comparing groups of symptoms, differential diagnosis, recommending medicinal materials to prescribe, and more. Conclusions : By the results of simulation with virtual diagnostic scenario, we concluded this algorithm is useful helping clinician in diagnosis procedure.

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