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A Drug Recommendation Method Based on Hidden Semi-Markov Model

A technology for recommending methods and medicines, applied in data processing applications, buying/selling/lease transactions, instruments, etc., to achieve the effect of improving correlation

Active Publication Date: 2021-11-30
GUANGDONG POLYTECHNIC NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method firstly predicts the diseases concerned by users based on the observable network behavior sequence of users on the cloud platform, and then recommends relevant drugs to users according to the diseases most concerned by users; it solves the problem of precise drug recommendation for users on the pharmaceutical aggregation supply chain collaboration platform The problem

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  • A Drug Recommendation Method Based on Hidden Semi-Markov Model
  • A Drug Recommendation Method Based on Hidden Semi-Markov Model
  • A Drug Recommendation Method Based on Hidden Semi-Markov Model

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Embodiment Construction

[0019] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, but not as a limitation of the present invention.

[0020] The so-called observation value space is the user's online behavior sequence on the pharmaceutical aggregation supply chain collaboration platform, expressed as x=x 1 ,x 2 ,...,x T , including pages browsed, resources accessed, or questions raised by users on systems or platforms such as apps, medical cloud platforms, and robots. The value space of the so-called state is the condition concerned by the user, expressed as y=y 1 ,y 2 ,...y n .

[0021] The parameter model of the drug recommendation model is expressed as: θ={π,A,B}; wherein, π is the initial state probability of the initial model, A is the state transition probability, and B is the observation probability.

[0022] In order to describe the model conveniently, the present invention adopts the following symbols:

[002...

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Abstract

The invention relates to a drug recommendation method based on a hidden semi-Markov model, which is characterized in that it comprises the following steps: 1. Preprocessing the training data to generate a training data set of user behavior sequences; 2. Estimating the parameters of the drug recommendation model ; 3. Collect the user's online behavior sequence on the medical platform; 4. Take the user's online behavior sequence as the observed value, use the trained drug recommendation model to infer the user's state sequence; 5. Calculate the expected duration of each state of the state sequence 6. Sort the obtained expected duration of each state in descending order to obtain the first plural states most concerned by the user; 7. Recommend corresponding medicines to the user according to the first plural states most concerned by the user. According to the online behavior of the user on the cloud platform, the present invention accurately predicts the illness concerned by the user, and then recommends relevant medicines to the user according to the illness most concerned by the user, thereby improving the relevance of the medicine recommendation results.

Description

technical field [0001] The invention relates to a drug recommendation method based on a hidden semi-Markov model. Background technique [0002] The pharmaceutical aggregation supply chain collaboration platform based on cloud computing and big data technology is a big data resource and public service cloud platform for the entire drug industry chain, with functions such as big data fusion and storage, platform big data mining and application, drug supervision and industry information integration, etc. , Integrating the resources of pharmaceutical supply chain enterprises is conducive to standardizing the economic order of the pharmaceutical online trading market and promoting the healthy and benign development of the entire pharmaceutical industry chain. In the context of the rapid development of medical big data application services, how to provide users with precise medical services has become a key problem that major medical platforms need to solve urgently. However, exis...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06Q30/02
CPCG06Q30/0201G06Q30/0631
Inventor 戴青云罗建桢蔡君魏文国雷方元
Owner GUANGDONG POLYTECHNIC NORMAL UNIV