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Drug recommendation method, electronic device and storage medium

A recommendation method and technology for drugs, applied in the fields of drugs or prescriptions, drug reference, and business, can solve the problems of recommendation, disease connection consideration, lack of transparency in decision-making, etc., and achieve the effect of more and more accurate recommendation results.

Inactive Publication Date: 2018-06-29
KANGMEI PHARMA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Some of the existing drug recommendation methods simply match the main symptoms of the drug with the symptoms of the drug demander to make a decision, and some are based on big data to train the recommendation model; the former cannot take the relationship between diseases into account in drug recommendation In the future, it may pay attention to some less important diseases and ignore the problem of important diseases. The latter also has the problem that new drug data cannot be implanted in time, and the decision-making is not transparent enough.

Method used

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  • Drug recommendation method, electronic device and storage medium
  • Drug recommendation method, electronic device and storage medium
  • Drug recommendation method, electronic device and storage medium

Examples

Experimental program
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Effect test

Embodiment 1

[0045] Such as figure 1 A method is recommended for a drug comprising the following steps:

[0046] Step S110, acquiring a collection of symptoms.

[0047] In this embodiment, the medicine demander may input symptoms, and the symptoms may be symptoms such as fever, headache, sore limbs, sneezing, and runny nose, or the names of diseases such as colds and rhinitis.

[0048] As a preferred embodiment, operations such as consent conversion, merging, and decomposition can also be performed on the illnesses input by drug demanders, and all illnesses can also be numbered uniformly, and the input illnesses input by drug demanders can be converted into numbers.

[0049] Step S120, acquiring disease-associated data according to the disease set.

[0050] As a preferred embodiment, the disease correlation data includes at least two diseases and correlation coefficients between different diseases.

[0051] Such as figure 2 For the disease correlation data shown, the circles represent...

Embodiment 2

[0072] Such as Figure 4 The drug recommendation method shown includes the following steps:

[0073] Step S210, acquiring a collection of symptoms.

[0074] Step S220, adjusting the disease set according to the disease association set.

[0075] As a preferred implementation, step S220 adjusts the disease set according to the disease-associated aggregate, specifically including the following steps:

[0076] Step S221 , extracting the disease to be confirmed from the disease-associated total set according to the disease set.

[0077] Such as Figure 5 As shown, the total set of disease associations includes disease 1 - disease 11, and the disease set obtained in step S210 includes disease 1 - disease 5. According to the correlation coefficients between different diseases in the disease correlation total set, it can be obtained that the correlation coefficients of disease 10 and disease 1 and disease 2 are both high, then the patient may also have disease 10, and it is necess...

Embodiment 3

[0099] Such as Figure 6 An electronic device shown includes a memory 200, a processor 300, and a program stored in the memory 200. The program is configured to be executed by the processor 300. When the processor 300 executes the program, the steps of the above drug recommendation method are implemented. .

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Abstract

The invention discloses a drug recommendation method, an electronic device and a storage medium. The method comprises the following steps: acquiring a disease set; acquiring disease association data according to the disease set; acquiring disease matching data of each drug; calculating a drug matching coefficient of each drug according to the disease association data and the disease matching dataof each drug; recommending drugs whose matching coefficients meet recommending conditions. Based on the correlation coefficients between different diseases and the disease matching coefficients between specific drugs and various diseases, the drug matching coefficients between the drugs and the disease set input by a demander are comprehensively evaluated, the link between the diseases is taken into account, the effect of the drugs on various diseases is also considered, and recommending results are more accurate. Moreover, the disease association data and the disease matching data of the drugs can pool wisdom and are scientific and transparent, and new drug data can be conveniently populated.

Description

technical field [0001] The invention relates to the field of medicines, in particular to a medicine recommendation method, electronic equipment and storage media. Background technique [0002] With the increase of physical pharmacies and online pharmacies, there is a shortage of doctors and pharmacists. How to recommend drugs for specific diseases has become an important research direction. Some of the existing drug recommendation methods simply match the main symptoms of the drug with the symptoms of the drug demander to make a decision, and some are based on big data to train the recommendation model; the former cannot take the relationship between diseases into account in drug recommendation In the future, it may pay attention to some less important diseases and ignore the problem of important diseases. The latter also has the problem that new drug data cannot be implanted in time, and the decision-making is not transparent enough. Contents of the invention [0003] In...

Claims

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Application Information

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IPC IPC(8): G16H20/10G16H70/40G06Q30/06
CPCG06Q30/0631
Inventor 许冬瑾洪雪娟颜正耀余莹莹李西
Owner KANGMEI PHARMA
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