Patient portrait drawing method and device based on medical big data
A big data and patient technology, applied in the field of medical big data, can solve problems such as inability to quickly understand patients, achieve strong professionalism, solve calculation deviation, and improve accuracy
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Embodiment 1
[0030] The embodiment of the present invention provides a patient portrait method based on medical big data, and the method of the present invention will be explained in detail in conjunction with the flow chart. see figure 1 , the flow of the method provided in this embodiment includes:
[0031] Step S1: Obtain pre-processed data and extract patient-related information from data sources. The main data sources and extracted information include the following parts:
[0032] Citizen card, medical insurance card, extraction includes patient's personal demographic information, including name, gender, age, occupation, place of birth, place of residence, family members, and work unit.
[0033] Health records: Extracted data include blood type, planned immunization, allergy history, maternal and child health care, chronic diseases, infectious diseases, physical examination information, etc.
[0034] Electronic medical records: Extracted data include outpatient records, emergency re...
Embodiment 2
[0044] An embodiment of the present invention provides a patient portrait device based on medical big data. The device is now described in detail in conjunction with the system architecture diagram of the device. See figure 2 .
[0045] At the bottom of the entire device is the data source, which includes structured data such as population information, HIS, LIS, electronic health records, electronic medical records, RIS / PACS, and unstructured data such as medical imaging, literature knowledge, and clinical guidelines. Since the organizational structure and storage methods of each data source are different, the technical methods adopted for data acquisition from different data sources include data probe, data capture, file analysis, database import, etc. Perform cleaning / filtering, transformation, structuring, normalization, vectorization. The offline data is stored in the group after preprocessing, and the online data is calculated and processed in real time using the Storm ...
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