Medical word meaning identification method and device, computer equipment and storage medium
A technology of medicine and word meaning, applied in the field of data processing, can solve problems such as not being able to hit the correct concept, and achieve the effect of technology sharing, precise medical diagnosis, and improvement of accuracy
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Embodiment 1
[0039] Example 1, such as figure 2 As shown, a medical word meaning recognition method is provided, which is applied to figure 1 The server in , as an example, includes the following steps:
[0040] Step 202, acquire the sentence to be analyzed, and search out the medical words related to the sentence to be analyzed from the preset medical word list according to the sentence to be analyzed, wherein the number of medical words is at least one.
[0041] The execution subject adopted in this embodiment can be a server or a computer device. This embodiment uses the server as the execution subject to perform medical word meaning recognition for illustration; the above-mentioned sentence to be analyzed can be a sentence input by a patient or a doctor in the terminal, and the above-mentioned medical The word list is a form pre-stored on the server. In this embodiment, the medical semantic segmentation in the SNOMED-CT knowledge base is used, and the medical features of 11 medical c...
Embodiment 2
[0052] Example 2, such as image 3 As shown, before step 202, it also includes:
[0053] Step 302, obtain the first training sample B1 from the case database, and obtain the second training sample B2 from the medical word list. The server can obtain the first training sample B through the platform 1 , Obtain the second training sample B through the medical word list 2 , specifically, the platform can be records of patients' illness conditions recorded by medical associations such as hospitals and clinics.
[0054] Step 304, establishing a similarity label between the first training sample B1 and at least one second training sample B2, wherein the second training sample B2 is a medical word related to the text of the first training sample B1. Through the first training sample B 1 A second training sample B related to a plurality of words related to the first training sample B1 2 Create a similarity label, for example, the first training sample: B1 = "glaucoma filtering ble...
Embodiment 3
[0058] Example 3, such as Figure 4 As shown, based on Embodiment 2, step 202 includes:
[0059] Step 402, identifying the analyzed text in the sentence to be analyzed.
[0060] Step 404, determine the similarity label corresponding to the analyzed text in the medical word list.
[0061] Step 406, according to the similarity labels, obtain medical words that are relevant to the text of the sentence to be analyzed.
[0062] Based on the similarity tags obtained in Example 2, after the server obtains the patient or doctor's sentence to be analyzed from the terminal, it can find the corresponding medical word from the medical word list. Specifically, the server obtains the sentence to be analyzed, then identifies the text of the sentence to be analyzed, and determines the similarity label corresponding to the sentence to be analyzed in the medical word list through comparison. medical word.
[0063] This embodiment can directly obtain the medical words that have a correlation...
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