CN115862896B - Doctor-patient collaborative management method and system based on perioperative period - Google Patents
Doctor-patient collaborative management method and system based on perioperative period Download PDFInfo
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Abstract
The invention relates to the field of intelligent medical treatment, and discloses a doctor-patient collaborative management method and system based on perioperative period, which are used for realizing intelligent management on perioperative users and improving collaborative management efficiency between doctors and users. The method comprises the following steps: performing state monitoring on a plurality of users in the perioperative period to obtain a first associated data set, and acquiring second associated data sets of a plurality of doctors; according to the user state analysis model, carrying out user state analysis on the first associated data set to obtain first user state index data; according to the data classification model, extracting the characteristics of the first user state index data to obtain second user state index data; inputting the second user state index data and the second associated data set into a doctor-patient collaborative analysis model for doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index; generating a doctor-patient collaborative management list according to the doctor-patient collaborative management index; and respectively performing perioperative monitoring on a plurality of users according to the doctor-patient cooperative management list.
Description
Technical Field
The invention relates to the field of intelligent medical treatment, in particular to a doctor-patient collaborative management method and system based on perioperative period.
Background
Perioperative is a whole process around the operation, starting from the user's decision to receive the surgical treatment, to the surgical treatment until substantial recovery, including a period of time before, during and after the operation, specifically from the time of determining the surgical treatment until the substantial end of the treatment associated with this operation, the period of time is about 5-7 days before the operation to 7-12 days after the operation, thus requiring a high degree of cooperative coordination between the doctor and the user during the perioperative period.
At present, for health status monitoring of users in the perioperative period, an intelligent management system is lacking, so that doctors cannot monitor the status of the users in place, and further, the efficiency of doctor-patient collaborative management is low, and medical resources are wasted greatly.
Disclosure of Invention
The invention provides a doctor-patient collaborative management method and system based on perioperation, which are used for realizing intelligent management on perioperative users and improving collaborative management efficiency between doctors and users.
The first aspect of the invention provides a perioperative-based doctor-patient collaborative management method, which comprises the following steps: performing state monitoring on a plurality of users in the perioperative period to obtain a first associated data set, and acquiring a second associated data set corresponding to a plurality of doctors; according to a preset user state analysis model, carrying out user state analysis on the first associated data set to obtain first user state index data; according to a preset data classification model, extracting characteristics of the first user state index data to obtain second user state index data; inputting the second user state index data and the second associated data set into a preset doctor-patient collaborative analysis model to perform doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index; generating doctor-patient collaborative management lists corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index; and transmitting the doctor-patient collaborative management list to a preset medical display terminal for visual list display, and respectively performing perioperative monitoring on the plurality of users according to the doctor-patient collaborative management list.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the performing state monitoring on the plurality of users in the perioperative period to obtain a first associated data set, and obtaining second associated data sets corresponding to a plurality of doctors includes: acquiring user identifiers of a plurality of users from a preset doctor-patient cooperative management platform, and acquiring doctor identifiers corresponding to a plurality of doctors; respectively matching first data tag information of the plurality of users according to the user identification, and respectively matching second data tag information of the plurality of doctors according to the doctor identification; acquiring state monitoring data of the plurality of users according to the first data tag information, and inquiring doctor characteristic data of the plurality of doctors according to the second data tag information; and constructing a first associated data set corresponding to the plurality of users according to the state monitoring data, and constructing a second associated data set corresponding to the plurality of doctors according to the doctor characteristic data.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the performing, according to a preset user state analysis model, user state analysis on the first associated data set to obtain first user state index data includes: invoking a preset user state analysis model, wherein the user state analysis model comprises: mapping relation between characteristic attribute words and user state indexes; extracting characteristic attribute words from the first associated data set to obtain a plurality of characteristic attribute words in the first associated data set; inputting a plurality of characteristic attribute words in the first associated data set and the first associated data set into the user state analysis model for mapping relation matching, and generating a user state index corresponding to each characteristic attribute word; and carrying out index data fusion on the user state indexes corresponding to each characteristic attribute word to generate first user state index data.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the feature extracting, according to a preset data classification model, the first user state index data to obtain second user state index data includes: calling a preset data classification model, and performing super-parameter setting on the data classification model to obtain corresponding target super-parameters; based on the target super-parameters, carrying out data group division on the first user state index data according to the data classification model to obtain a plurality of index data groups; calculating a characteristic value corresponding to each index data group, calculating an average value corresponding to the index data groups, and taking the average value as a target value corresponding to the first user state index data; comparing the characteristic value with the target value to obtain a comparison result, and generating a characteristic index abnormal value according to the comparison result; and extracting an abnormal user state index from the first user state index data according to the characteristic index abnormal value, and generating second user state index data according to the abnormal user state index.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, inputting the second user state index data and the second associated data set into a preset doctor-patient collaborative analysis model to perform doctor-patient collaborative management analysis, to obtain a doctor-patient collaborative management index, includes: carrying out data set fusion on the second user state index data and the second associated data set to generate a target data set; respectively setting second user state index data in the target data set and weight data corresponding to the second associated data set; inputting the target data set into the doctor-patient collaborative analysis model; and carrying out doctor-patient collaborative management calculation on the target data set and the weight data through the doctor-patient collaborative analysis model to obtain a doctor-patient collaborative management index, wherein the doctor-patient collaborative management index is used for indicating a correlation coefficient between a user and a doctor.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the generating a doctor-patient collaborative management list corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index includes: matching the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index to generate a plurality of target triples, wherein the target triples comprise: doctor-patient cooperative management index, user and doctor; performing list conversion on each target triplet according to the doctor-patient cooperative management index to generate an initial management list; and performing list checking on the initial management list to generate a doctor-patient collaborative management list.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the transmitting the doctor-patient coordination management list to a preset medical display terminal for visual list display, and performing perioperative monitoring on the multiple users according to the doctor-patient coordination management list respectively includes: transmitting the doctor-patient collaborative management list to a preset medical display terminal, and constructing a multi-screen display unit through the medical display terminal; performing list display layout on the doctor-patient collaborative management list according to the multi-screen display unit, and generating a list display layout scheme; and performing visual list display on the doctor-patient collaborative management list according to the list display layout scheme, and performing perioperative monitoring on the plurality of users according to the doctor-patient collaborative management list.
The second aspect of the present invention provides a perioperative-based doctor-patient cooperative management system, which comprises:
the acquisition module is used for carrying out state monitoring on a plurality of users in the perioperative period to obtain a first associated data set and acquiring a second associated data set corresponding to a plurality of doctors;
the analysis module is used for carrying out user state analysis on the first associated data set according to a preset user state analysis model to obtain first user state index data;
the extraction module is used for carrying out feature extraction on the first user state index data according to a preset data classification model to obtain second user state index data;
the processing module is used for inputting the second user state index data and the second associated data set into a preset doctor-patient cooperative analysis model to perform doctor-patient cooperative management analysis to obtain a doctor-patient cooperative management index;
the generation module is used for generating doctor-patient collaborative management lists corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index;
the monitoring module is used for transmitting the doctor-patient cooperative management list to a preset medical display terminal for visual list display, and respectively carrying out perioperative monitoring on the plurality of users according to the doctor-patient cooperative management list.
With reference to the second aspect, in a first implementation manner of the second aspect of the present invention, the acquiring module is specifically configured to: acquiring user identifiers of a plurality of users from a preset doctor-patient cooperative management platform, and acquiring doctor identifiers corresponding to a plurality of doctors; respectively matching first data tag information of the plurality of users according to the user identification, and respectively matching second data tag information of the plurality of doctors according to the doctor identification; acquiring state monitoring data of the plurality of users according to the first data tag information, and inquiring doctor characteristic data of the plurality of doctors according to the second data tag information; and constructing a first associated data set corresponding to the plurality of users according to the state monitoring data, and constructing a second associated data set corresponding to the plurality of doctors according to the doctor characteristic data.
With reference to the second aspect, in a second implementation manner of the second aspect of the present invention, the analysis module is specifically configured to: invoking a preset user state analysis model, wherein the user state analysis model comprises: mapping relation between characteristic attribute words and user state indexes; extracting characteristic attribute words from the first associated data set to obtain a plurality of characteristic attribute words in the first associated data set; inputting a plurality of characteristic attribute words in the first associated data set and the first associated data set into the user state analysis model for mapping relation matching, and generating a user state index corresponding to each characteristic attribute word; and carrying out index data fusion on the user state indexes corresponding to each characteristic attribute word to generate first user state index data.
With reference to the second aspect, in a third implementation manner of the second aspect of the present invention, the extraction module is specifically configured to: calling a preset data classification model, and performing super-parameter setting on the data classification model to obtain corresponding target super-parameters; based on the target super-parameters, carrying out data group division on the first user state index data according to the data classification model to obtain a plurality of index data groups; calculating a characteristic value corresponding to each index data group, calculating an average value corresponding to the index data groups, and taking the average value as a target value corresponding to the first user state index data; comparing the characteristic value with the target value to obtain a comparison result, and generating a characteristic index abnormal value according to the comparison result; and extracting an abnormal user state index from the first user state index data according to the characteristic index abnormal value, and generating second user state index data according to the abnormal user state index.
With reference to the second aspect, in a fourth implementation manner of the second aspect of the present invention, the processing module is specifically configured to: carrying out data set fusion on the second user state index data and the second associated data set to generate a target data set; respectively setting second user state index data in the target data set and weight data corresponding to the second associated data set; inputting the target data set into the doctor-patient collaborative analysis model; and carrying out doctor-patient collaborative management calculation on the target data set and the weight data through the doctor-patient collaborative analysis model to obtain a doctor-patient collaborative management index, wherein the doctor-patient collaborative management index is used for indicating a correlation coefficient between a user and a doctor.
With reference to the second aspect, in a fifth implementation manner of the second aspect of the present invention, the generating module is specifically configured to: matching the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index to generate a plurality of target triples, wherein the target triples comprise: doctor-patient cooperative management index, user and doctor; performing list conversion on each target triplet according to the doctor-patient cooperative management index to generate an initial management list; and performing list checking on the initial management list to generate a doctor-patient collaborative management list.
With reference to the second aspect, in a sixth implementation manner of the second aspect of the present invention, the monitoring module further includes: the transmission unit is used for transmitting the doctor-patient cooperative management list to a preset medical display terminal, and constructing a multi-screen display unit through the medical display terminal; the layout unit is used for carrying out list display layout on the doctor-patient collaborative management list according to the multi-screen display unit, and generating a list display layout scheme; the display unit is used for displaying the visual list of the doctor-patient collaborative management list according to the list display layout scheme, and respectively carrying out perioperative monitoring on the plurality of users according to the doctor-patient collaborative management list.
In the technical scheme provided by the invention, a plurality of users in the perioperative period are subjected to state monitoring to obtain a first associated data set, and a second associated data set of a plurality of doctors is obtained; according to the user state analysis model, carrying out user state analysis on the first associated data set to obtain first user state index data; according to the data classification model, extracting the characteristics of the first user state index data to obtain second user state index data; inputting the second user state index data and the second associated data set into a doctor-patient collaborative analysis model for doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index; generating a doctor-patient collaborative management list according to the doctor-patient collaborative management index; according to the method, the system and the device, the surrounding operation period is monitored for a plurality of users according to the doctor-patient cooperative management list, intelligent state monitoring is conducted between the plurality of users and a plurality of doctors in the surrounding operation period, then the optimal doctor of each user is matched, the doctor-patient cooperative management list is further obtained, finally the doctor-patient cooperative management list is intelligently and visually displayed through an intelligent medical display terminal, intelligent management is conducted for the users in the surrounding operation period, and the cooperative management efficiency between the doctors and the users is improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a perioperative-based doctor-patient collaborative management method according to an embodiment of the present invention;
FIG. 2 is a flow chart of user state analysis in an embodiment of the present invention;
FIG. 3 is a flow chart of the extraction features of user status index data in an embodiment of the present invention;
FIG. 4 is a flowchart of the calculation of the index of traditional Chinese medicine suffering from cooperative management in the embodiment of the invention;
FIG. 5 is a schematic diagram of an embodiment of a perioperative based doctor-patient collaborative management system in accordance with an embodiment of the invention;
FIG. 6 is a schematic diagram of another embodiment of a perioperative-based doctor-patient collaborative management system in accordance with an embodiment of the invention.
Detailed Description
The embodiment of the invention provides a doctor-patient collaborative management method and system based on perioperative period, which are used for realizing intelligent management on perioperative users and improving collaborative management efficiency between doctors and users. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, and an embodiment of a perioperative-based doctor-patient collaborative management method in an embodiment of the present invention includes:
s101, performing state monitoring on a plurality of users in the perioperative period to obtain a first associated data set, and acquiring a second associated data set corresponding to a plurality of doctors;
it can be understood that the execution subject of the present invention may be a perioperative doctor-patient collaborative management system, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, the server performs state monitoring on a plurality of users in the perioperative period, redundantly stores state monitoring data in a distributed file, creates an index table for the state monitoring data file, inserts the index table into a structured data table, finishes state monitoring data query according to a query request to obtain a first associated data set, further, matches the identifications of all doctors in the perioperative period with the identifications of all doctors in an information storage database, establishes a one-to-one correspondence, merges all data objects according to the one-to-one correspondence of the identifications and the doctors, and obtains a second associated data set corresponding to the doctors.
S102, carrying out user state analysis on a first associated data set according to a preset user state analysis model to obtain first user state index data;
specifically, the server determines the state identification parameters through the first associated data set according to a preset user state analysis model, determines the sub-user state corresponding to the first associated data set according to the state identification parameters, and determines the user index data state according to the sub-user state, so that complete monitoring information can be timely acquired and analyzed, and the timeliness and accuracy of the acquired user state are improved.
S103, carrying out feature extraction on the first user state index data according to a preset data classification model to obtain second user state index data;
it should be noted that, the server extracts the status index feature from the first user status index data, pre-processes the feature, trains the user status classifier based on the decision tree algorithm by using the user history status index feature set, and determines the user status index feature according to the classifier model to obtain the user status marker sequence, and further, the server determines the second user status index data according to the status marker sequence.
S104, inputting the second user state index data and the second associated data set into a preset doctor-patient collaborative analysis model to carry out doctor-patient collaborative management analysis, so as to obtain a doctor-patient collaborative management index;
specifically, the server disassembles the second user state index data and the second associated data set into a plurality of sub-data; each sub data is distributed to a data processing terminal corresponding to the required original data for calculation, each data processing terminal finds the corresponding optimal influence weight according to the index value of the optimal influence weight, data fusion is carried out on the second user state index data and the second associated data set through the optimal influence weight, management analysis is carried out on the fused data, and a doctor-patient collaborative management index is obtained.
S105, generating doctor-patient collaborative management lists corresponding to a plurality of users and a plurality of doctors according to the doctor-patient collaborative management indexes;
s106, transmitting the doctor-patient collaborative management list to a preset medical display terminal for visual list display, and respectively performing perioperative monitoring on a plurality of users according to the doctor-patient collaborative management list.
Specifically, the server obtains a data display request, obtains identification information of a visual list, obtains at least one index list associated with the identification information, determines respective corresponding data acquisition modes of each characteristic index, obtains characteristic data information of the corresponding characteristic index based on the obtained respective data acquisition modes, and finally adds the obtained respective characteristic data information to a preset visual list display template to generate a corresponding visual list so as to display the visual list.
According to the embodiment of the invention, intelligent state monitoring is carried out between a plurality of users and a plurality of doctors in the perioperative period, then the optimal doctor of each user is matched, so that a doctor-patient collaborative management list is obtained, and finally intelligent visual display is carried out on the doctor-patient collaborative management list through an intelligent medical display terminal.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Acquiring user identifiers of a plurality of users from a preset doctor-patient cooperative management platform, and acquiring doctor identifiers corresponding to a plurality of doctors;
(2) Respectively matching first data tag information of a plurality of users according to the user identification, and respectively matching second data tag information of a plurality of doctors according to the doctor identification;
(3) Acquiring state monitoring data of a plurality of users according to the first data tag information, and inquiring doctor characteristic data of a plurality of doctors according to the second data tag information;
(4) And constructing a plurality of first associated data sets corresponding to the users according to the state monitoring data, and constructing a plurality of second associated data sets corresponding to the doctors according to the doctor characteristic data.
Specifically, the server acquires user identifications of a plurality of users from a preset doctor-patient cooperative management platform, acquires doctor identifications corresponding to a plurality of doctors,
uploading the user identification and the corresponding first data tag information to a data processing terminal, collecting the first data tag information and the state data by a server, simultaneously respectively matching the second data tag information of a plurality of doctors according to the doctor identification and the doctor identification by the server,
the acquired data, the acquired first data tag information, the acquired state data and the acquired second data tag information are sent to a data processing terminal, so that the server acquires state monitoring data of a plurality of users according to the first data tag information, inquires doctor characteristic data of a plurality of doctors according to the second data tag information, constructs a first associated data set corresponding to the plurality of users according to the state monitoring data, and constructs a second associated data set corresponding to the plurality of doctors according to the doctor characteristic data.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, a preset user state analysis model is called, wherein the user state analysis model comprises: mapping relation between characteristic attribute words and user state indexes;
S202, extracting characteristic attribute words from a first associated data set to obtain a plurality of characteristic attribute words in the first associated data set;
s203, inputting a plurality of characteristic attribute words in the first associated data set and the first associated data set into a user state analysis model for mapping relation matching, and generating a user state index corresponding to each characteristic attribute word;
s204, carrying out index data fusion on the user state indexes corresponding to each characteristic attribute word, and generating first user state index data.
Specifically, the server invokes a preset user state analysis model, where the user state analysis model includes: for each associated data in a first associated data set, searching a mapping relation corresponding to the attribute words in the associated data in the mapping relation to form a candidate mapping relation set, selecting a group of mapping relation with highest collocation weight from the candidate mapping relation set, taking the corresponding target attribute words as the characteristic attribute words of the associated data, wherein the server extracts the attribute words of each data in the first associated data set, annotates and identifies the attribute words, searches out the attribute words with incomplete annotation, carries out keyword matching on the attribute words with incomplete annotation, determines the attribute characteristics of the attribute words with incomplete annotation, simultaneously inputs a plurality of characteristic attribute words in the first associated data set and the first associated data set into a user state analysis model to carry out mapping relation matching, generates the user state index corresponding to each characteristic attribute word, and finally the server carries out index data fusion on the user state index corresponding to each characteristic attribute word to generate the first user state index data.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, calling a preset data classification model, and performing super-parameter setting on the data classification model to obtain corresponding target super-parameters;
s302, dividing data groups of the first user state index data according to a data classification model based on target super parameters to obtain a plurality of index data groups;
s303, respectively calculating a characteristic value corresponding to each index data group, calculating an average value corresponding to a plurality of index data groups, and taking the average value as a target value corresponding to the first user state index data;
s304, comparing the characteristic value with the target value to obtain a comparison result, and generating a characteristic index abnormal value according to the comparison result;
s305, extracting abnormal user state indexes from the first user state index data according to the abnormal values of the characteristic indexes, and generating second user state index data according to the abnormal user state indexes.
Specifically, the server calls a preset data classification model, performs super parameter setting on the data classification model to obtain corresponding target super parameters, divides data groups of first user state index data according to the data classification model to obtain a plurality of index data groups, wherein the server obtains a first preset number of super parameter groups, respectively takes the value of each super parameter group as the value of the super parameter in a preset classification algorithm, classifies the data in the first user state index data to obtain the confidence coefficient corresponding to each super parameter group, calculates the weight of each super parameter group according to the confidence coefficient corresponding to each super parameter group, estimates one value of each super parameter according to the obtained value of each super parameter group and the weight of each super parameter group by using a Bayesian optimizing algorithm, obtains a new super parameter group, accumulates the number of the super parameter groups, and takes the value of the super parameter group with the maximum weight as the value of the super parameter in the preset classification algorithm when the number of the accumulated super parameter groups reaches a second preset number. The method provided by the embodiment of the invention can improve the efficiency of determining the super parameter, further, the server calculates the characteristic value corresponding to each index data group and calculates the average value corresponding to a plurality of index data groups, the average value is used as the target value corresponding to the first user state index data, the characteristic value and the target value are compared to obtain the comparison result, the characteristic index abnormal value is generated according to the comparison result, the abnormal user state index is extracted from the first user state index data according to the characteristic index abnormal value, and the second user state index data is generated according to the abnormal user state index.
In a specific embodiment, as shown in fig. 4, the process of executing step S104 may specifically include the following steps:
s401, carrying out data set fusion on the second user state index data and the second associated data set to generate a target data set;
s402, respectively setting second user state index data in the target data set and weight data corresponding to the second associated data set;
s403, inputting the target data set into a doctor-patient collaborative analysis model;
s404, performing doctor-patient collaborative management calculation on the target data set and the weight data through a doctor-patient collaborative analysis model to obtain a doctor-patient collaborative management index, wherein the doctor-patient collaborative management index is used for indicating a correlation coefficient between a user and a doctor.
Specifically, the server performs data set fusion on the second user state index data and the second associated data set to generate a target data set, acquires data type information corresponding to the second user state index data and the second associated data set, adopts a multi-attribute combined data fusion algorithm on the acquired data, groups all data according to different data type information, determines a selected data set by using a data optimization algorithm, performs data fusion on the selected data to generate the target data set, respectively sets the second user state index data in the target data set and weight data corresponding to the second associated data set, and further inputs the target data set into a doctor-patient cooperative analysis model, and performs doctor-patient cooperative management calculation on the target data set and the weight data through the doctor-patient cooperative analysis model to obtain a doctor-patient cooperative management index, wherein the doctor-patient cooperative management index is used for indicating a correlation coefficient between a user and a doctor.
When the collaborative management calculation is performed, the prestored index calculation data is called through the data operation function, and the doctor-patient collaborative management calculation is performed according to the index calculation data to obtain a doctor-patient collaborative management index, wherein the doctor-patient collaborative management index is used for indicating a correlation coefficient between a user and a doctor.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Matching a plurality of users with a plurality of doctors according to the doctor-patient cooperative management index to generate a plurality of target triples, wherein the target triples comprise: doctor-patient cooperative management index, user and doctor;
(2) Performing list conversion on each target triplet according to the doctor-patient cooperative management index to generate an initial management list;
(3) And performing list checking on the initial management list to generate a doctor-patient collaborative management list.
Specifically, a doctor-patient collaborative management index is obtained, wherein the doctor-patient collaborative management index comprises a symptom data index and a characteristic data index, the characteristic data index comprises gender data and age data, and a doctor-patient matching coefficient of a patient and each candidate doctor in a plurality of candidate doctors is determined according to the symptom data index and the characteristic data index and is used for reflecting the matching degree of the patient and the candidate doctor. Generating a plurality of target triples in a plurality of candidate doctors according to the doctor-patient matching coefficient of the patient and each candidate doctor, wherein the target triples comprise: and carrying out list conversion on each target triplet according to the doctor-patient cooperative management index, the user and the doctor to generate an initial management list, and carrying out list correction on the initial management list to generate a doctor-patient cooperative management list.
In a specific embodiment, the process of executing step S106 may specifically include the following steps:
(1) Transmitting the doctor-patient cooperative management list to a preset medical display terminal, and constructing a multi-screen display unit through the medical display terminal;
(2) Performing list display layout on the doctor-patient collaborative management list according to the multi-screen display unit, and generating a list display layout scheme;
(3) And performing visual list display on the doctor-patient collaborative management list according to the list display layout scheme, and performing perioperative monitoring on a plurality of users according to the doctor-patient collaborative management list.
Specifically, preset display addresses of a plurality of list units in the doctor-patient collaborative management list are obtained, memory head addresses of a plurality of display units are respectively obtained based on the preset display addresses of the plurality of list units, a multi-screen display unit is constructed, list display layout is carried out on the doctor-patient collaborative management list according to the multi-screen display unit, a list display layout scheme is generated, visual list display is carried out on the doctor-patient collaborative management list according to the list display layout scheme, and perioperative monitoring is carried out on a plurality of users according to the doctor-patient collaborative management list.
The method for co-management of doctor and patient based on perioperative period in the embodiment of the present invention is described above, and the following describes a co-management system of doctor and patient based on perioperative period in the embodiment of the present invention, referring to fig. 5, one embodiment of the co-management system of doctor and patient based on perioperative period in the embodiment of the present invention includes:
The acquiring module 501 is configured to perform state monitoring on a plurality of users in a perioperative period to obtain a first associated data set, and acquire second associated data sets corresponding to a plurality of doctors;
the analysis module 502 is configured to perform user state analysis on the first association data set according to a preset user state analysis model, so as to obtain first user state index data;
an extracting module 503, configured to perform feature extraction on the first user status index data according to a preset data classification model, so as to obtain second user status index data;
the processing module 504 is configured to input the second user status index data and the second associated data set into a preset doctor-patient cooperative analysis model to perform doctor-patient cooperative management analysis, so as to obtain a doctor-patient cooperative management index;
a generating module 505, configured to generate a doctor-patient collaborative management list corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index;
the monitoring module 506 is configured to transmit the doctor-patient coordination management list to a preset medical display terminal for displaying a visual list, and perform perioperative monitoring on the multiple users according to the doctor-patient coordination management list.
Through the cooperative cooperation of the components, the state of a plurality of users in the perioperative period is monitored to obtain a first associated data set, and a second associated data set of a plurality of doctors is obtained; according to the user state analysis model, carrying out user state analysis on the first associated data set to obtain first user state index data; according to the data classification model, extracting the characteristics of the first user state index data to obtain second user state index data; inputting the second user state index data and the second associated data set into a doctor-patient collaborative analysis model for doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index; generating a doctor-patient collaborative management list according to the doctor-patient collaborative management index; according to the method, the system and the device, the surrounding operation period is monitored for a plurality of users according to the doctor-patient cooperative management list, intelligent state monitoring is conducted between the plurality of users and a plurality of doctors in the surrounding operation period, then the optimal doctor of each user is matched, the doctor-patient cooperative management list is further obtained, finally the doctor-patient cooperative management list is intelligently and visually displayed through an intelligent medical display terminal, intelligent management is conducted for the users in the surrounding operation period, and the cooperative management efficiency between the doctors and the users is improved.
Referring to fig. 6, another embodiment of the perioperative-based doctor-patient collaborative management system according to the present invention includes:
the acquiring module 501 is configured to perform state monitoring on a plurality of users in a perioperative period to obtain a first associated data set, and acquire second associated data sets corresponding to a plurality of doctors;
the analysis module 502 is configured to perform user state analysis on the first association data set according to a preset user state analysis model, so as to obtain first user state index data;
an extracting module 503, configured to perform feature extraction on the first user status index data according to a preset data classification model, so as to obtain second user status index data;
the processing module 504 is configured to input the second user status index data and the second associated data set into a preset doctor-patient cooperative analysis model to perform doctor-patient cooperative management analysis, so as to obtain a doctor-patient cooperative management index;
a generating module 505, configured to generate a doctor-patient collaborative management list corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index;
the monitoring module 506 is configured to transmit the doctor-patient coordination management list to a preset medical display terminal for displaying a visual list, and perform perioperative monitoring on the multiple users according to the doctor-patient coordination management list.
Optionally, the obtaining module 501 is specifically configured to: acquiring user identifiers of a plurality of users from a preset doctor-patient cooperative management platform, and acquiring doctor identifiers corresponding to a plurality of doctors; respectively matching first data tag information of the plurality of users according to the user identification, and respectively matching second data tag information of the plurality of doctors according to the doctor identification; acquiring state monitoring data of the plurality of users according to the first data tag information, and inquiring doctor characteristic data of the plurality of doctors according to the second data tag information; and constructing a first associated data set corresponding to the plurality of users according to the state monitoring data, and constructing a second associated data set corresponding to the plurality of doctors according to the doctor characteristic data.
Optionally, the analysis module 502 is specifically configured to: invoking a preset user state analysis model, wherein the user state analysis model comprises: mapping relation between characteristic attribute words and user state indexes; extracting characteristic attribute words from the first associated data set to obtain a plurality of characteristic attribute words in the first associated data set; inputting a plurality of characteristic attribute words in the first associated data set and the first associated data set into the user state analysis model for mapping relation matching, and generating a user state index corresponding to each characteristic attribute word; and carrying out index data fusion on the user state indexes corresponding to each characteristic attribute word to generate first user state index data.
Optionally, the extracting module 503 is specifically configured to: calling a preset data classification model, and performing super-parameter setting on the data classification model to obtain corresponding target super-parameters; based on the target super-parameters, carrying out data group division on the first user state index data according to the data classification model to obtain a plurality of index data groups; calculating a characteristic value corresponding to each index data group, calculating an average value corresponding to the index data groups, and taking the average value as a target value corresponding to the first user state index data; comparing the characteristic value with the target value to obtain a comparison result, and generating a characteristic index abnormal value according to the comparison result; and extracting an abnormal user state index from the first user state index data according to the characteristic index abnormal value, and generating second user state index data according to the abnormal user state index.
Optionally, the processing module 504 is specifically configured to: carrying out data set fusion on the second user state index data and the second associated data set to generate a target data set; respectively setting second user state index data in the target data set and weight data corresponding to the second associated data set; inputting the target data set into the doctor-patient collaborative analysis model; and carrying out doctor-patient collaborative management calculation on the target data set and the weight data through the doctor-patient collaborative analysis model to obtain a doctor-patient collaborative management index, wherein the doctor-patient collaborative management index is used for indicating a correlation coefficient between a user and a doctor.
Optionally, the generating module 505 is specifically configured to: matching the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index to generate a plurality of target triples, wherein the target triples comprise: doctor-patient cooperative management index, user and doctor; performing list conversion on each target triplet according to the doctor-patient cooperative management index to generate an initial management list; and performing list checking on the initial management list to generate a doctor-patient collaborative management list.
Optionally, the monitoring module 506 further includes:
the transmission unit 5061 is configured to transmit the doctor-patient collaborative management list to a preset medical display terminal, and construct a multi-screen display unit through the medical display terminal;
the layout unit 5062 is configured to perform list display layout on the doctor-patient collaborative management list according to the multi-screen display unit, and generate a list display layout scheme;
the display unit 5063 is configured to perform visual list display on the doctor-patient coordination management list according to the list display layout scheme, and perform perioperative monitoring on the multiple users according to the doctor-patient coordination management list.
In the embodiment of the invention, a plurality of users in the perioperative period are subjected to state monitoring to obtain a first associated data set, and a second associated data set of a plurality of doctors is obtained; according to the user state analysis model, carrying out user state analysis on the first associated data set to obtain first user state index data; according to the data classification model, extracting the characteristics of the first user state index data to obtain second user state index data; inputting the second user state index data and the second associated data set into a doctor-patient collaborative analysis model for doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index; generating a doctor-patient collaborative management list according to the doctor-patient collaborative management index; according to the method, the system and the device, the surrounding operation period is monitored for a plurality of users according to the doctor-patient cooperative management list, intelligent state monitoring is conducted between the plurality of users and a plurality of doctors in the surrounding operation period, then the optimal doctor of each user is matched, the doctor-patient cooperative management list is further obtained, finally the doctor-patient cooperative management list is intelligently and visually displayed through an intelligent medical display terminal, intelligent management is conducted for the users in the surrounding operation period, and the cooperative management efficiency between the doctors and the users is improved.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The utility model provides a doctor-patient collaborative management method based on perioperation, which is characterized in that the doctor-patient collaborative management method based on perioperation includes:
performing state monitoring on a plurality of users in the perioperative period to obtain a first associated data set, and acquiring a second associated data set corresponding to a plurality of doctors;
according to a preset user state analysis model, carrying out user state analysis on the first associated data set to obtain first user state index data;
according to a preset data classification model, extracting characteristics of the first user state index data to obtain second user state index data; calling a preset data classification model, and performing super-parameter setting on the data classification model to obtain corresponding target super-parameters; based on the target super-parameters, carrying out data group division on the first user state index data according to the data classification model to obtain a plurality of index data groups; calculating a characteristic value corresponding to each index data group, calculating an average value corresponding to the index data groups, and taking the average value as a target value corresponding to the first user state index data; comparing the characteristic value with the target value to obtain a comparison result, and generating a characteristic index abnormal value according to the comparison result; extracting an abnormal user state index from the first user state index data according to the characteristic index abnormal value, and generating second user state index data according to the abnormal user state index;
Inputting the second user state index data and the second associated data set into a preset doctor-patient collaborative analysis model to perform doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index;
generating doctor-patient collaborative management lists corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index;
and transmitting the doctor-patient collaborative management list to a preset medical display terminal for visual list display, and respectively performing perioperative monitoring on the plurality of users according to the doctor-patient collaborative management list.
2. The perioperative doctor-patient collaborative management method according to claim 1, wherein the performing state monitoring on the perioperative users to obtain a first association data set and obtaining a second association data set corresponding to a plurality of doctors comprises:
acquiring user identifiers of a plurality of users from a preset doctor-patient cooperative management platform, and acquiring doctor identifiers corresponding to a plurality of doctors;
respectively matching first data tag information of the plurality of users according to the user identification, and respectively matching second data tag information of the plurality of doctors according to the doctor identification;
acquiring state monitoring data of the plurality of users according to the first data tag information, and inquiring doctor characteristic data of the plurality of doctors according to the second data tag information;
And constructing a first associated data set corresponding to the plurality of users according to the state monitoring data, and constructing a second associated data set corresponding to the plurality of doctors according to the doctor characteristic data.
3. The perioperative doctor-patient collaborative management method according to claim 1, wherein the performing user state analysis on the first associated data set according to a preset user state analysis model to obtain first user state index data comprises:
invoking a preset user state analysis model, wherein the user state analysis model comprises: mapping relation between characteristic attribute words and user state indexes;
extracting characteristic attribute words from the first associated data set to obtain a plurality of characteristic attribute words in the first associated data set;
inputting a plurality of characteristic attribute words in the first associated data set and the first associated data set into the user state analysis model for mapping relation matching, and generating a user state index corresponding to each characteristic attribute word;
and carrying out index data fusion on the user state indexes corresponding to each characteristic attribute word to generate first user state index data.
4. The perioperative-based doctor-patient collaborative management method according to claim 1, wherein inputting the second user status index data and the second associated data set into a preset doctor-patient collaborative analysis model for doctor-patient collaborative management analysis to obtain a doctor-patient collaborative management index comprises:
carrying out data set fusion on the second user state index data and the second associated data set to generate a target data set;
respectively setting second user state index data in the target data set and weight data corresponding to the second associated data set;
inputting the target data set into the doctor-patient collaborative analysis model;
and carrying out doctor-patient collaborative management calculation on the target data set and the weight data through the doctor-patient collaborative analysis model to obtain a doctor-patient collaborative management index, wherein the doctor-patient collaborative management index is used for indicating a correlation coefficient between a user and a doctor.
5. The perioperative-based doctor-patient collaborative management method according to claim 1, wherein the generating doctor-patient collaborative management lists corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index comprises:
Matching the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index to generate a plurality of target triples, wherein the target triples comprise: doctor-patient cooperative management index, user and doctor;
performing list conversion on each target triplet according to the doctor-patient cooperative management index to generate an initial management list;
and performing list checking on the initial management list to generate a doctor-patient collaborative management list.
6. The perioperative-based doctor-patient collaborative management method according to claim 1, wherein the transmitting the doctor-patient collaborative management list to a preset medical display terminal for visual list display, and respectively performing perioperative monitoring on the plurality of users according to the doctor-patient collaborative management list comprises:
transmitting the doctor-patient collaborative management list to a preset medical display terminal, and constructing a multi-screen display unit through the medical display terminal;
performing list display layout on the doctor-patient collaborative management list according to the multi-screen display unit, and generating a list display layout scheme;
and performing visual list display on the doctor-patient collaborative management list according to the list display layout scheme, and performing perioperative monitoring on the plurality of users according to the doctor-patient collaborative management list.
7. The utility model provides a doctor-patient collaborative management system based on perioperative, its characterized in that, doctor-patient collaborative management system based on perioperative includes:
the acquisition module is used for carrying out state monitoring on a plurality of users in the perioperative period to obtain a first associated data set and acquiring a second associated data set corresponding to a plurality of doctors;
the analysis module is used for carrying out user state analysis on the first associated data set according to a preset user state analysis model to obtain first user state index data;
the extraction module is used for carrying out feature extraction on the first user state index data according to a preset data classification model to obtain second user state index data; calling a preset data classification model, and performing super-parameter setting on the data classification model to obtain corresponding target super-parameters; based on the target super-parameters, carrying out data group division on the first user state index data according to the data classification model to obtain a plurality of index data groups; calculating a characteristic value corresponding to each index data group, calculating an average value corresponding to the index data groups, and taking the average value as a target value corresponding to the first user state index data; comparing the characteristic value with the target value to obtain a comparison result, and generating a characteristic index abnormal value according to the comparison result; extracting an abnormal user state index from the first user state index data according to the characteristic index abnormal value, and generating second user state index data according to the abnormal user state index;
The processing module is used for inputting the second user state index data and the second associated data set into a preset doctor-patient cooperative analysis model to perform doctor-patient cooperative management analysis to obtain a doctor-patient cooperative management index;
the generation module is used for generating doctor-patient collaborative management lists corresponding to the plurality of users and the plurality of doctors according to the doctor-patient collaborative management index;
the monitoring module is used for transmitting the doctor-patient cooperative management list to a preset medical display terminal for visual list display, and respectively carrying out perioperative monitoring on the plurality of users according to the doctor-patient cooperative management list.
8. The perioperative based doctor-patient coordination management system of claim 7, wherein the acquisition module is specifically configured to:
acquiring user identifiers of a plurality of users from a preset doctor-patient cooperative management platform, and acquiring doctor identifiers corresponding to a plurality of doctors;
respectively matching first data tag information of the plurality of users according to the user identification, and respectively matching second data tag information of the plurality of doctors according to the doctor identification;
acquiring state monitoring data of the plurality of users according to the first data tag information, and inquiring doctor characteristic data of the plurality of doctors according to the second data tag information;
And constructing a first associated data set corresponding to the plurality of users according to the state monitoring data, and constructing a second associated data set corresponding to the plurality of doctors according to the doctor characteristic data.
9. The perioperative based doctor-patient collaborative management system according to claim 7, wherein the analysis module is specifically configured to:
invoking a preset user state analysis model, wherein the user state analysis model comprises: mapping relation between characteristic attribute words and user state indexes;
extracting characteristic attribute words from the first associated data set to obtain a plurality of characteristic attribute words in the first associated data set;
inputting a plurality of characteristic attribute words in the first associated data set and the first associated data set into the user state analysis model for mapping relation matching, and generating a user state index corresponding to each characteristic attribute word;
and carrying out index data fusion on the user state indexes corresponding to each characteristic attribute word to generate first user state index data.
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