CN115966292B - Intelligent hospital-based data automation management system and method - Google Patents
Intelligent hospital-based data automation management system and method Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent hospital data management, in particular to a data automation management system and method based on an intelligent hospital, comprising the steps of respectively constructing a history diagnosis and treatment database corresponding to each doctor; splitting all the historical diagnosis and treatment records in each historical diagnosis and treatment database based on the recorded information distribution characteristics of all the historical diagnosis and treatment records in each historical diagnosis and treatment database; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor to obtain event attributes of each diagnosis and treatment event; carrying out event classification processing on all diagnosis and treatment events based on event attributes; constructing a diagnosis and treatment experience dimension model for each doctor; the user registers and logs in the intelligent hospital platform to input department information of pre-diagnosis, the matching doctors are pushed to the user based on the department information, and meanwhile, the diagnosis and treatment experience dimension model of each matching doctor is subjected to data display to assist the user in selecting proper diagnosis and treatment doctors based on own disease conditions.
Description
Technical Field
The invention relates to the technical field of intelligent hospital data management, in particular to a data automation management system and method based on an intelligent hospital.
Background
For patients, how to find a suitable doctor to make a visit is the biggest problem; under the current large environment, a part of patients usually tend to choose a plurality of expert numbers with larger cards for hanging diagnosis in a hospital no matter the size or severity of the symptoms, so that the patients are more stable, the phenomenon of 'large materials and small materials' is avoided, and the phenomenon that the expert numbers are reserved to be hung up in the hospital, or the phenomenon that the patients need to be robbed to fix because the expert numbers are released in the hospital to be waited for is frequently rare; some patients can search through the network before visiting, make the choice based on patient's public praise, around public praise, and because of the Internet age, huge information often can make people feel everywhere, do not appear and let people ' pick up eyes ' phenomenon; for the above situations, medical resources or time spent for the user to visit are wasted to a certain extent; in the current large circumstances, the user needs to understand that a suitable doctor is not a doctor in the largest hospital or a doctor of the largest brand, but rather a doctor who is quite experienced in treating his own condition and who makes a visit convenient.
Disclosure of Invention
The invention aims to provide a data automation management system and method based on an intelligent hospital, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent hospital-based data automation management method comprises the following steps:
step S100: the method comprises the steps of retrieving historical diagnosis and treatment data of each doctor in a large database of an intelligent hospital, and respectively constructing a historical diagnosis and treatment database corresponding to each doctor, wherein the historical diagnosis and treatment data are data formed by gathering diagnosis and treatment data generated in each historical diagnosis and treatment record; the diagnosis and treatment data comprise self symptom description data of a patient, inquiry data of a doctor and diagnosis data prescribed by the doctor; the data types in the historical diagnosis and treatment database comprise audio and video data and text data;
step S200: based on the record information distribution characteristics of all the historical diagnosis and treatment records in each historical diagnosis and treatment database, splitting all the historical diagnosis and treatment records in each historical diagnosis and treatment database respectively to obtain a plurality of independent diagnosis and treatment record subsets and a plurality of independent historical diagnosis and treatment records;
step S300: the diagnosis and treatment data generated in each independent diagnosis and treatment record subset or each independent historical diagnosis and treatment record in each historical diagnosis and treatment database are respectively used as diagnosis and treatment data generated by a doctor corresponding to each historical diagnosis and treatment database under one diagnosis and treatment event; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor to obtain event attributes of each diagnosis and treatment event;
step S400: in a historical diagnosis and treatment database of each doctor, carrying out event classification processing on all diagnosis and treatment events based on event attributes; constructing a diagnosis and treatment experience dimension model for each doctor;
step S500: the user registers and logs in an intelligent hospital platform, department information of pre-diagnosis is input in the intelligent hospital platform, the intelligent hospital platform pushes matched doctors to the user based on the department information, and meanwhile, the diagnosis and treatment experience dimension model of each matched doctor is subjected to data display to assist the user in selecting proper diagnosis and treatment doctors based on the disease condition of the user.
Further, step S200 includes:
step S201: the method comprises the steps of respectively extracting the generation time of records and the identity information of a patient to be diagnosed from all historical diagnosis and treatment records in all historical diagnosis and treatment databases; capturing the longest re-diagnosis period of the doctor diagnosing the same disease type for the patient to be diagnosed from all the historical diagnosis and treatment databases, and setting the longest re-diagnosis period as the re-diagnosis period corresponding to each disease type;
step S202: acquiring a re-diagnosis period T corresponding to each historical diagnosis and treatment database according to the disease type which is responsible for diagnosis by a doctor corresponding to each historical diagnosis and treatment database; in each historical diagnosis and treatment database, the historical diagnosis and treatment records with the same identity information of the patient to be diagnosed are collected according to the sequence generated by the records, and each historical diagnosis and treatment database is split into a plurality of diagnosis and treatment record sets and a plurality of independent historical diagnosis and treatment records; wherein, each diagnosis and treatment record set corresponds to identity information of a patient to be treated;
step S203: capturing interval record generation time between every two adjacent historical diagnosis records in each diagnosis record set in sequence; sequentially comparing the generation time of each interval record with the re-diagnosis period T corresponding to each historical diagnosis and treatment database;
step S204: when the ith historical diagnosis and treatment record L is captured in a certain diagnosis and treatment record set i And (i+1) th historical diagnosis and treatment record L i+1 The interval between records the generation timeSatisfy->If i=1, the 1 st historical diagnosis and treatment record L 1 Removing from a certain diagnosis and treatment record set to obtain a new diagnosis and treatment recordRecord the collection, and record L 1 As a historical diagnostic record in each of the historical diagnosis and treatment databases independent of the corresponding one; if i is more than or equal to 2, integrating the 1 st historical diagnosis and treatment record L 1 Obtaining diagnosis and treatment record subsets { L ] from the ith historical diagnosis and treatment record 1 ,…,L i Rejecting all historical diagnosis and treatment records in the diagnosis and treatment record subsets from a certain diagnosis and treatment record set to obtain a new diagnosis and treatment record set, and collecting the diagnosis and treatment record subsets { L } 1 ,…,L i As a set of historical diagnostic records in each of the historical diagnosis and treatment databases independent of the correspondence;
the steps are to avoid independent analysis of a plurality of diagnosis and treatment records belonging to one disease treatment course, and improve the accuracy of data adopted in the subsequent construction process of the diagnosis and treatment experience dimension model of each doctor;
step S205: step S203-step S204 are repeated, and each historical diagnosis and treatment database is split into a plurality of independent diagnosis and treatment record subsets and a plurality of independent historical diagnosis and treatment records.
Further, step S300 includes:
step S301: if the independent diagnosis record subset is extracted from the historical diagnosis database of a doctor and comprises { W } 1 ,W 2 ,…,W n Extracting independent historic diagnosis records including { F } 1 ,F 2 ,…,F m -a }; wherein W is 1 ,…,W n Representing the 1 st, 2 nd, … th and n independent diagnosis and treatment record subsets in the historical diagnosis and treatment database; f (F) 1 ,F 2 ,…,F m Representing the 1 st, 2 nd, … th and m independent historical diagnosis and treatment records in the historical diagnosis and treatment database; judging the total number K=n+m of diagnosis and treatment events existing in a historical diagnosis and treatment database of a certain doctor;
step S302: extracting key information from self-symptom description data of a patient, inquiry data of a doctor and diagnosis data prescribed by the doctor, which are generated by each historical diagnosis record in each diagnosis record subset; setting key information extracted from self-symptom description data of a patient as first characteristic diagnosis and treatment data; setting key information extracted from doctor inquiry data as second characteristic diagnosis and treatment data; setting key information extracted from diagnostic data of a doctor as third characteristic diagnosis and treatment data;
step S303: summarizing all first characteristic diagnosis and treatment data, all second characteristic diagnosis and treatment data and all third characteristic diagnosis and treatment data extracted from all historical diagnosis and treatment records in each independent diagnosis and treatment record subset according to the generation sequence of the corresponding records respectively, and correspondingly obtaining a first characteristic diagnosis and treatment data sequence, a second characteristic diagnosis and treatment data sequence and a third characteristic diagnosis and treatment data sequence respectively; the first feature diagnosis and treatment data sequence, the second feature diagnosis and treatment data sequence and the third feature diagnosis and treatment data sequence are respectively used as a first event attribute, a second event attribute and a third event attribute of diagnosis and treatment events corresponding to each independent diagnosis and treatment record subset;
step S304: and respectively corresponding the first characteristic diagnosis and treatment data, the second characteristic diagnosis and treatment data and the third characteristic diagnosis and treatment data extracted from each independent history diagnosis and treatment record as a first event attribute, a second event attribute and a third event attribute of the diagnosis and treatment event corresponding to each independent history diagnosis and treatment record.
Further, the key information extracted from the patient's self-symptom description data and the doctor's inquiry data includes key information about a body part, key information about symptom descriptions, key information about symptom degree descriptions; the key information extracted from the diagnosis data prescribed by the doctor includes key information on diagnosis conclusion, medicine kind information in the prescription, medicine taking notice information in the prescription.
Further, step S400 includes:
step S401: randomly selecting a diagnosis and treatment event as a similar center in a history diagnosis and treatment database of a doctor respectively; respectively calculating the similarity of the first event attribute, the second event attribute and the third event attribute of other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as a similarity center to respectively obtain the similarity between the other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as the similarity centerAccumulating similarity values: r=a×r 1 +b*r 2 +c*r 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is 1 、r 2 、r 3 Respectively representing similarity between other diagnosis and treatment events in the historical diagnosis and treatment database and diagnosis and treatment events serving as similar centers on the first event attribute, the second event attribute and the third event attribute; wherein a is r 1 B is r 2 Weight value of c is r 3 Weight value of (2); wherein a is>b>c；
Will r 1 ，r 2 ，r 3 Corresponding weight values a, b and c are set to satisfy a>b>The invention is characterized in that the dimension of the diagnosis and treatment experience of a doctor is analyzed, namely, the history diagnosis and treatment records of the doctor are analyzed from the aspect of data, if the difference between the two diagnosis and treatment records is larger, the information showing that repeated diagnosis and treatment references appear in the two diagnosis and treatment records is smaller, the experience value of the doctor is required to be higher, and if the number of the difference diagnosis and treatment records appear in the history diagnosis and treatment record of one doctor is larger, the diagnosis and treatment experience of the doctor can be reflected to be richer to a certain extent, and the most suitable diagnosis and treatment scheme can be provided for the patient with individual difference more skillfully; because the diagnosis and treatment level of doctors can be correspondingly improved along with the increase of the time of the practise or the increase of the number of patients to be treated, the diagnosis and treatment level can be objectively reflected from a diagnosis and treatment scheme, namely, from diagnosis data prescribed by doctors through a plurality of medication habits or diagnosis and treatment habits, when the similarity of diagnosis and treatment events is calculated, a smaller weight value is given to the similarity of third event attributes related to inquiry data of doctors with larger variables, a higher weight value is given to the similarity of first event attributes and the similarity of second event attributes which can present disorder differences, and the accuracy in classifying the diagnosis and treatment events can be improved to a certain extent;
step S402: classifying other diagnosis and treatment events with the accumulated similarity value larger than the similarity threshold value between the diagnosis and treatment events serving as the similarity centers as the same type of diagnosis and treatment events with the diagnosis and treatment events serving as the similarity centers; classifying all diagnosis and treatment events contained in a history diagnosis and treatment database of a doctor respectively;
step S403: taking each type of diagnosis and treatment event appearing in a history diagnosis and treatment database of a doctor as a diagnosis and treatment experience dimension of the doctor, capturing the total number Q of the diagnosis and treatment events contained in each diagnosis and treatment experience dimension, and calculating the experience value corresponding to each diagnosis and treatment experience dimensionK represents the total number K of diagnosis and treatment events existing in a historical diagnosis and treatment database of a doctor, and diagnosis and treatment experience dimension models corresponding to each doctor are respectively constructed.
In order to better implement the method, a data automation management system is also provided, and the system comprises: the system comprises a historical diagnosis and treatment database construction module, a historical diagnosis and treatment database management module, a characteristic diagnosis and treatment data extraction management module, a diagnosis and treatment experience dimension model construction module and an intelligent hospital platform pushing management module;
the history diagnosis and treatment database building module is used for retrieving the history diagnosis and treatment data of each doctor in a large database of the intelligent hospital and respectively building a history diagnosis and treatment database corresponding to each doctor;
the history diagnosis and treatment database management module is used for receiving the data in the history diagnosis and treatment database construction module, and splitting all the history diagnosis and treatment records in each history diagnosis and treatment database based on the record information distribution characteristics of all the history diagnosis and treatment records in each history diagnosis and treatment database to obtain a plurality of independent diagnosis and treatment record subsets and a plurality of independent history diagnosis and treatment records;
the characteristic diagnosis and treatment data extraction management module is used for receiving the data in the history diagnosis and treatment database management module, and respectively taking diagnosis and treatment data generated in each independent diagnosis and treatment record subset or each independent history diagnosis and treatment record in each history diagnosis and treatment database as diagnosis and treatment data generated by a doctor corresponding to each history diagnosis and treatment database under one diagnosis and treatment event; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor to obtain event attributes of each diagnosis and treatment event;
the diagnosis and treatment experience dimension model construction module is used for carrying out event classification processing on all diagnosis and treatment events based on event attributes in a historical diagnosis and treatment database of each doctor; constructing a diagnosis and treatment experience dimension model for each doctor;
the intelligent hospital platform pushing management module pushes the matched doctors to the user according to department information of the pre-hanging diagnosis input by the user in the intelligent hospital platform, and meanwhile, performs data display on the diagnosis and treatment experience dimension model of each matched doctor to assist the user in selecting proper diagnosis and treatment doctors based on the disease conditions of the user.
Further, the feature diagnosis and treatment data extraction management module comprises a feature diagnosis and treatment data extraction unit and a diagnosis and treatment event attribute extraction unit;
the characteristic diagnosis and treatment data extraction unit is used for extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor;
the diagnosis and treatment event attribute extraction unit is used for receiving the data in the characteristic diagnosis and treatment data extraction unit and extracting corresponding event attributes for each diagnosis and treatment event respectively.
Further, the diagnosis and treatment experience dimension model building module comprises an event classifying and processing unit and a diagnosis and treatment experience dimension model building unit;
the event classification processing unit is used for carrying out event classification processing on all diagnosis and treatment events based on event attributes in the historical diagnosis and treatment database of each doctor;
the diagnosis and treatment experience dimension model construction unit is used for receiving the data in the event classification processing unit, calculating experience values for each diagnosis and treatment experience dimension, and constructing a diagnosis and treatment experience dimension model corresponding to each doctor.
Compared with the prior art, the invention has the following beneficial effects: the invention can analyze data based on all the historical doctor records of each doctor, judge the doctor-seeing disease types in all the historical doctor records of each doctor based on the difference among the historical doctor records, reflect the channel value of each doctor for each disease type by constructing a diagnosis and treatment experience dimension model for each doctor, and display the diagnosis and treatment experience dimension model of each doctor to a user for data, so that the user can select the doctor most suitable for the user based on the situation, the phenomenon of hanging diagnosis by selecting expert numbers in a psychological blindness due to the pursuit of the patient is reduced, and the waste of medical resources can be reduced to a certain extent.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an intelligent hospital-based data automation management method according to the present invention;
fig. 2 is a schematic structural diagram of an intelligent hospital-based data automation management system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an intelligent hospital-based data automation management method comprises the following steps:
step S100: the method comprises the steps of retrieving historical diagnosis and treatment data of each doctor in a large database of an intelligent hospital, and respectively constructing a historical diagnosis and treatment database corresponding to each doctor, wherein the historical diagnosis and treatment data are data formed by gathering diagnosis and treatment data generated in each historical diagnosis and treatment record; the diagnosis and treatment data comprise self symptom description data of a patient, inquiry data of a doctor and diagnosis data prescribed by the doctor; the data types in the historical diagnosis and treatment database comprise audio and video data and text data;
step S200: based on the record information distribution characteristics of all the historical diagnosis and treatment records in each historical diagnosis and treatment database, splitting all the historical diagnosis and treatment records in each historical diagnosis and treatment database respectively to obtain a plurality of independent diagnosis and treatment record subsets and a plurality of independent historical diagnosis and treatment records;
wherein, step S200 includes:
step S201: the method comprises the steps of respectively extracting the generation time of records and the identity information of a patient to be diagnosed from all historical diagnosis and treatment records in all historical diagnosis and treatment databases; capturing the longest re-diagnosis period of the doctor diagnosing the same disease type for the patient to be diagnosed from all the historical diagnosis and treatment databases, and setting the longest re-diagnosis period as the re-diagnosis period corresponding to each disease type;
step S202: acquiring a re-diagnosis period T corresponding to each historical diagnosis and treatment database according to the disease type which is responsible for diagnosis by a doctor corresponding to each historical diagnosis and treatment database; in each historical diagnosis and treatment database, the historical diagnosis and treatment records with the same identity information of the patient to be diagnosed are collected according to the sequence generated by the records, and each historical diagnosis and treatment database is split into a plurality of diagnosis and treatment record sets and a plurality of independent historical diagnosis and treatment records; wherein, each diagnosis and treatment record set corresponds to identity information of a patient to be treated;
step S203: capturing interval record generation time between every two adjacent historical diagnosis records in each diagnosis record set in sequence; sequentially comparing the generation time of each interval record with the re-diagnosis period T corresponding to each historical diagnosis and treatment database;
step S204: when the ith historical diagnosis and treatment record L is captured in a certain diagnosis and treatment record set i And (i+1) th historical diagnosis and treatment record L i+1 The interval between records the generation timeSatisfy->If i=1, the 1 st historical diagnosis and treatment record L 1 Removing from a certain diagnosis and treatment record set to obtain a new diagnosis and treatment record set, and adding L to the new diagnosis and treatment record set 1 As a historical diagnostic record in each of the historical diagnosis and treatment databases independent of the corresponding one; if i is greater than or equal to 2, the wholeThe 1 st historical diagnosis and treatment record L 1 Obtaining diagnosis and treatment record subsets { L ] from the ith historical diagnosis and treatment record 1 ,…,L i Rejecting all historical diagnosis and treatment records in the diagnosis and treatment record subsets from a certain diagnosis and treatment record set to obtain a new diagnosis and treatment record set, and collecting the diagnosis and treatment record subsets { L } 1 ,…,L i As a set of historical diagnostic records in each of the historical diagnosis and treatment databases independent of the correspondence;
for example, include { L } in a set of diagnostic records within a historic diagnostic database U 1 ,L 2 ,L 3 ,L 4 ,L 5 And capture L 2 And L is equal to 3 The interval between records the generation timeSatisfy +.about.between day and T=7 days>Since i=2, L will be 1 ,L 2 Integration is carried out to obtain a diagnosis and treatment record subset { L } 1 ,L 2 And L is }, and 1 ,L 2 from { L 1 ,L 2 ,L 3 ,L 4 ,L 5 Removing from the { L }, and obtaining a new diagnosis and treatment record set of { L } 3 ,L 4 ,L 5 -a }; will { L ] 1 ,L 2 As a set of historical diagnostic records independent of U;
step S205: step S203-step S204 are circulated, and each historical diagnosis and treatment database is split into a plurality of independent diagnosis and treatment record subsets and a plurality of independent historical diagnosis and treatment records;
step S300: the diagnosis and treatment data generated in each independent diagnosis and treatment record subset or each independent historical diagnosis and treatment record in each historical diagnosis and treatment database are respectively used as diagnosis and treatment data generated by a doctor corresponding to each historical diagnosis and treatment database under one diagnosis and treatment event; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor to obtain event attributes of each diagnosis and treatment event;
wherein, step S300 includes:
step S301: if the independent diagnosis record subset is extracted from the historical diagnosis database of a doctor and comprises { W } 1 ,W 2 ,…,W n Extracting independent historic diagnosis records including { F } 1 ,F 2 ,…,F m -a }; wherein W is 1 ,…,W n Representing the 1 st, 2 nd, … th and n independent diagnosis and treatment record subsets in the historical diagnosis and treatment database; f (F) 1 ,F 2 ,…,F m Representing the 1 st, 2 nd, … th and m independent historical diagnosis and treatment records in the historical diagnosis and treatment database; judging the total number K=n+m of diagnosis and treatment events existing in a historical diagnosis and treatment database of a certain doctor;
step S302: extracting key information from self-symptom description data of a patient, inquiry data of a doctor and diagnosis data prescribed by the doctor, which are generated by each historical diagnosis record in each diagnosis record subset; setting key information extracted from self-symptom description data of a patient as first characteristic diagnosis and treatment data; setting key information extracted from doctor inquiry data as second characteristic diagnosis and treatment data; setting key information extracted from diagnostic data of a doctor as third characteristic diagnosis and treatment data;
wherein the key information extracted from the patient's self-symptom description data and the doctor's inquiry data includes key information about a body part, key information about symptom descriptions, key information about symptom degree descriptions; the key information extracted from the diagnosis data issued by the doctor comprises key information about diagnosis conclusion, medicine type information in the prescription, and medicine taking notice information in the prescription;
for example, key information pertaining to a body part includes, but is not limited to: head, stomach, abdomen, liver, knee, arm; key information pertaining to the description of symptoms includes, but is not limited to: pain, itching, tingling, soreness and coma; key information pertaining to the description of the extent of symptoms includes, but is not limited to: a matrix, jerky, very, slight, very;
step S303: summarizing all first characteristic diagnosis and treatment data, all second characteristic diagnosis and treatment data and all third characteristic diagnosis and treatment data extracted from all historical diagnosis and treatment records in each independent diagnosis and treatment record subset according to the generation sequence of the corresponding records respectively, and correspondingly obtaining a first characteristic diagnosis and treatment data sequence, a second characteristic diagnosis and treatment data sequence and a third characteristic diagnosis and treatment data sequence respectively; the first feature diagnosis and treatment data sequence, the second feature diagnosis and treatment data sequence and the third feature diagnosis and treatment data sequence are respectively used as a first event attribute, a second event attribute and a third event attribute of diagnosis and treatment events corresponding to each independent diagnosis and treatment record subset;
step S304: the first characteristic diagnosis and treatment data, the second characteristic diagnosis and treatment data and the third characteristic diagnosis and treatment data extracted from each independent history diagnosis and treatment record are respectively used as a first event attribute, a second event attribute and a third event attribute of diagnosis and treatment events corresponding to each independent history diagnosis and treatment record;
step S400: in a historical diagnosis and treatment database of each doctor, carrying out event classification processing on all diagnosis and treatment events based on event attributes; constructing a diagnosis and treatment experience dimension model for each doctor;
step S500: the user registers and logs in an intelligent hospital platform, department information of pre-diagnosis is input in the intelligent hospital platform, the intelligent hospital platform pushes matched doctors to the user based on the department information, and meanwhile, the diagnosis and treatment experience dimension model of each matched doctor is subjected to data display to assist the user in selecting proper diagnosis and treatment doctors based on the disease condition of the user;
wherein, step S400 includes:
step S401: randomly selecting a diagnosis and treatment event as a similar center in a history diagnosis and treatment database of a doctor respectively; calculating the similarity of the first event attribute, the second event attribute and the third event attribute of other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as a similarity center respectively to obtain accumulated similarity values between the other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as the similarity center respectively: r=a×r 1 +b*r 2 +c*r 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is 1 、r 2 、r 3 Respectively representing similarity between other diagnosis and treatment events in the historical diagnosis and treatment database and diagnosis and treatment events serving as similar centers on the first event attribute, the second event attribute and the third event attribute; wherein a is r 1 B is r 2 Weight value of c is r 3 Weight value of (2); wherein a is>b>c；
Step S402: classifying other diagnosis and treatment events with the accumulated similarity value larger than the similarity threshold value between the diagnosis and treatment events serving as the similarity centers as the same type of diagnosis and treatment events with the diagnosis and treatment events serving as the similarity centers; classifying all diagnosis and treatment events contained in a history diagnosis and treatment database of a doctor respectively;
step S403: taking each type of diagnosis and treatment event appearing in a history diagnosis and treatment database of a doctor as a diagnosis and treatment experience dimension of the doctor, capturing the total number Q of the diagnosis and treatment events contained in each diagnosis and treatment experience dimension, and calculating the experience value corresponding to each diagnosis and treatment experience dimensionK represents the total number K of diagnosis and treatment events existing in a historical diagnosis and treatment database of a doctor, and diagnosis and treatment experience dimension models corresponding to each doctor are respectively constructed.
In order to better implement the method, a data automation management system is also provided, and the system comprises: the system comprises a historical diagnosis and treatment database construction module, a historical diagnosis and treatment database management module, a characteristic diagnosis and treatment data extraction management module, a diagnosis and treatment experience dimension model construction module and an intelligent hospital platform pushing management module;
the history diagnosis and treatment database building module is used for retrieving the history diagnosis and treatment data of each doctor in a large database of the intelligent hospital and respectively building a history diagnosis and treatment database corresponding to each doctor;
the history diagnosis and treatment database management module is used for receiving the data in the history diagnosis and treatment database construction module, and splitting all the history diagnosis and treatment records in each history diagnosis and treatment database based on the record information distribution characteristics of all the history diagnosis and treatment records in each history diagnosis and treatment database to obtain a plurality of independent diagnosis and treatment record subsets and a plurality of independent history diagnosis and treatment records;
the characteristic diagnosis and treatment data extraction management module is used for receiving the data in the history diagnosis and treatment database management module, and respectively taking diagnosis and treatment data generated in each independent diagnosis and treatment record subset or each independent history diagnosis and treatment record in each history diagnosis and treatment database as diagnosis and treatment data generated by a doctor corresponding to each history diagnosis and treatment database under one diagnosis and treatment event; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor to obtain event attributes of each diagnosis and treatment event;
the diagnosis and treatment experience dimension model construction module is used for carrying out event classification processing on all diagnosis and treatment events based on event attributes in a historical diagnosis and treatment database of each doctor; constructing a diagnosis and treatment experience dimension model for each doctor;
the intelligent hospital platform pushing management module pushes the matched doctors to the user according to department information of the pre-hanging diagnosis input by the user in the intelligent hospital platform, and meanwhile, performs data display on the diagnosis and treatment experience dimension model of each matched doctor to assist the user in selecting proper diagnosis and treatment doctors based on the disease conditions of the user.
The feature diagnosis and treatment data extraction management module comprises a feature diagnosis and treatment data extraction unit and a diagnosis and treatment event attribute extraction unit;
the characteristic diagnosis and treatment data extraction unit is used for extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor;
the diagnosis and treatment event attribute extraction unit is used for receiving the data in the characteristic diagnosis and treatment data extraction unit and extracting corresponding event attributes for each diagnosis and treatment event respectively.
The diagnosis and treatment experience dimension model building module comprises an event classification processing unit and a diagnosis and treatment experience dimension model building unit;
the event classification processing unit is used for carrying out event classification processing on all diagnosis and treatment events based on event attributes in the historical diagnosis and treatment database of each doctor;
the diagnosis and treatment experience dimension model construction unit is used for receiving the data in the event classification processing unit, calculating experience values for each diagnosis and treatment experience dimension, and constructing a diagnosis and treatment experience dimension model corresponding to each doctor.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An intelligent hospital-based data automation management method, which is characterized by comprising the following steps:
step S100: the method comprises the steps of retrieving historical diagnosis and treatment data of each doctor in a large database of an intelligent hospital, and respectively constructing a historical diagnosis and treatment database corresponding to each doctor, wherein the historical diagnosis and treatment data are data formed by gathering diagnosis and treatment data generated in each historical diagnosis and treatment record; the diagnosis and treatment data comprise self symptom description data of a patient, inquiry data of a doctor and diagnosis data prescribed by the doctor; the data types in the historical diagnosis and treatment database comprise audio and video data and text data;
step S200: based on the record information distribution characteristics of all the historical diagnosis and treatment records in each historical diagnosis and treatment database, splitting all the historical diagnosis and treatment records in each historical diagnosis and treatment database respectively to obtain a plurality of independent diagnosis and treatment record subsets and a plurality of independent historical diagnosis and treatment records;
step S300: the diagnosis and treatment data generated in each independent diagnosis and treatment record subset or each independent history diagnosis and treatment record in each history diagnosis and treatment database are respectively used as diagnosis and treatment data generated by a doctor corresponding to each history diagnosis and treatment database under one diagnosis and treatment event; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of the doctor to obtain event attributes of each diagnosis and treatment event;
step S400: in a historical diagnosis and treatment database of each doctor, carrying out event classification processing on all diagnosis and treatment events based on event attributes; constructing a diagnosis and treatment experience dimension model for each doctor;
step S500: the intelligent hospital platform is used for inputting department information of pre-hanging diagnosis, pushing matched doctors to the user based on the department information, and simultaneously carrying out data display on diagnosis and treatment experience dimension models of the matched doctors to assist the user in selecting proper diagnosis and treatment doctors based on own disease conditions.
2. The method for automated data management in intelligent hospitals according to claim 1, wherein the step S200 comprises:
step S201: the method comprises the steps of respectively extracting the generation time of records and the identity information of a patient to be diagnosed from all historical diagnosis and treatment records in all historical diagnosis and treatment databases; capturing the longest re-diagnosis period of a doctor diagnosing the same disease type for a patient to be diagnosed from all the historical diagnosis and treatment databases, and setting the longest re-diagnosis period as the re-diagnosis period corresponding to each disease type;
step S202: acquiring a review period T corresponding to each historical diagnosis and treatment database according to the disease type which is responsible for diagnosis by a doctor corresponding to each historical diagnosis and treatment database; in each historical diagnosis and treatment database, collecting the historical diagnosis and treatment records with the same identity information of the patient to be diagnosed according to the sequence generated by the records, and splitting each historical diagnosis and treatment database into a plurality of diagnosis and treatment record sets and a plurality of independent historical diagnosis and treatment records; wherein, each diagnosis and treatment record set corresponds to identity information of a patient to be treated;
step S203: capturing interval record generation time between every two adjacent historical diagnosis records in each diagnosis record set in sequence; sequentially comparing the generation time of each interval record with the re-diagnosis period T corresponding to each historical diagnosis and treatment database;
step S204: when the ith historical diagnosis and treatment record L is captured in a certain diagnosis and treatment record set i And (i+1) th historical diagnosis and treatment record L i+1 The interval between records the generation timeSatisfy->If i=1, the 1 st historical diagnosis and treatment record L 1 Removing from the certain diagnosis and treatment record set to obtain a new diagnosis and treatment record set, and adding L to the new diagnosis and treatment record set 1 As a historical diagnostic record in each of said historical diagnosis and treatment databases independent of the corresponding one; if i is more than or equal to 2, integrating the 1 st historical diagnosis and treatment record L 1 Obtaining diagnosis and treatment record subsets { L ] from the ith historical diagnosis and treatment record 1 ,…,L i Rejecting all historical diagnosis and treatment records in the diagnosis and treatment record subsets from the certain diagnosis and treatment record set to obtain a new diagnosis and treatment record set, and collecting the diagnosis and treatment record subsets { L } 1 ,…,L i -as a set of historical diagnostic records in each of said historical diagnosis and treatment databases independent of the corresponding;
step S205: step S203-step S204 are repeated, and each historical diagnosis and treatment database is split into a plurality of independent diagnosis and treatment record subsets and a plurality of independent historical diagnosis and treatment records.
3. The method for automated data management in a smart hospital according to claim 1, wherein the step S300 comprises:
step S301: if the independent diagnosis record subset is extracted from the historical diagnosis database of a doctor and comprises { W } 1 ,W 2 ,…,W n Extracting independent historic diagnosis records including { F } 1 ,F 2 ,…,F m -a }; wherein W is 1 ,…,W n Representing a subset of 1 st, 2 nd, … th, n independent diagnosis and treatment records in the historical diagnosis and treatment database; f (F) 1 ,F 2 ,…,F m Representing the 1 st, 2 nd, … th and m independent historical diagnosis and treatment records in the historical diagnosis and treatment database; judging the total number K=n+m of diagnosis and treatment events existing in a historical diagnosis and treatment database of a certain doctor;
step S302: extracting key information from self-symptom description data of a patient, inquiry data of a doctor and diagnosis data prescribed by the doctor, which are generated by each historical diagnosis record in each diagnosis record subset; setting key information extracted from self-symptom description data of a patient as first characteristic diagnosis and treatment data; setting key information extracted from doctor inquiry data as second characteristic diagnosis and treatment data; setting key information extracted from diagnostic data of a doctor as third characteristic diagnosis and treatment data;
step S303: summarizing all first characteristic diagnosis and treatment data, all second characteristic diagnosis and treatment data and all third characteristic diagnosis and treatment data extracted from all historical diagnosis and treatment records in each independent diagnosis and treatment record subset according to the generation sequence of the corresponding records respectively, and correspondingly obtaining a first characteristic diagnosis and treatment data sequence, a second characteristic diagnosis and treatment data sequence and a third characteristic diagnosis and treatment data sequence respectively; the first characteristic diagnosis and treatment data sequence, the second characteristic diagnosis and treatment data sequence and the third characteristic diagnosis and treatment data sequence are respectively used as a first event attribute, a second event attribute and a third event attribute of diagnosis and treatment events corresponding to each independent diagnosis and treatment record subset;
step S304: and respectively corresponding the first characteristic diagnosis and treatment data, the second characteristic diagnosis and treatment data and the third characteristic diagnosis and treatment data extracted from each independent history diagnosis and treatment record to be used as a first event attribute, a second event attribute and a third event attribute of the diagnosis and treatment event corresponding to each independent history diagnosis and treatment record.
4. A data automation management method based on intelligent hospitals according to claim 3, wherein the key information extracted from the self-symptom description data of the patient and the doctor's inquiry data includes key information about body parts, key information about symptom descriptions, key information about symptom degree descriptions; the key information extracted from the diagnosis data prescribed by the doctor includes key information on diagnosis conclusion, medicine kind information in the prescription, medicine taking notice information in the prescription.
5. The automated intelligent hospital-based data management method according to claim 4, wherein step S400 comprises:
step S401: randomly selecting a diagnosis and treatment event as a similar center in a history diagnosis and treatment database of a doctor respectively; and respectively carrying out similarity calculation on the first event attribute, the second event attribute and the third event attribute of other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as a similarity center to respectively obtain accumulated similarity values between the other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as the similarity center: r=a×r 1 +b*r 2 +c*r 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is 1 、r 2 、r 3 Respectively representing similarity between other diagnosis and treatment events in the historical diagnosis and treatment database and the diagnosis and treatment event serving as a similarity center on a first event attribute, a second event attribute and a third event attribute; wherein a is r 1 B is r 2 Weight value of c is r 3 Weight value of (2); wherein a is>b>c；
Step S402: classifying other diagnosis and treatment events with the accumulated similarity value larger than a similarity threshold value between the diagnosis and treatment events serving as the similarity centers as the same type of diagnosis and treatment events of the diagnosis and treatment events serving as the similarity centers; classifying all diagnosis and treatment events contained in the history diagnosis and treatment database of a doctor respectively;
step S403: taking each type of diagnosis and treatment event appearing in a history diagnosis and treatment database of a doctor as a diagnosis and treatment experience dimension of the doctor, capturing the total number Q of the diagnosis and treatment events contained in each diagnosis and treatment experience dimension, and calculating an experience value corresponding to each diagnosis and treatment experience dimensionK represents the total number K of diagnosis and treatment events existing in a historical diagnosis and treatment database of a doctor, and diagnosis and treatment experience dimension models corresponding to each doctor are respectively constructed.
6. A data automation management system applying the intelligent hospital-based data automation management method of any one of claims 1 to 5, the system comprising: the system comprises a historical diagnosis and treatment database construction module, a historical diagnosis and treatment database management module, a characteristic diagnosis and treatment data extraction management module, a diagnosis and treatment experience dimension model construction module and an intelligent hospital platform pushing management module;
the history diagnosis and treatment database building module is used for retrieving the history diagnosis and treatment data of each doctor in a large database of the intelligent hospital and respectively building a history diagnosis and treatment database corresponding to each doctor;
the history diagnosis and treatment database management module is used for receiving the data in the history diagnosis and treatment database construction module, and splitting all the history diagnosis and treatment records in each history diagnosis and treatment database based on the record information distribution characteristics of all the history diagnosis and treatment records in each history diagnosis and treatment database to obtain a plurality of independent diagnosis and treatment record subsets and a plurality of independent history diagnosis and treatment records;
the characteristic diagnosis and treatment data extraction management module is used for receiving the data in the history diagnosis and treatment database management module, and respectively taking diagnosis and treatment data generated in each independent diagnosis and treatment record subset or each independent history diagnosis and treatment record in each history diagnosis and treatment database as diagnosis and treatment data generated by a doctor corresponding to each history diagnosis and treatment database under one diagnosis and treatment event; extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of the doctor to obtain event attributes of each diagnosis and treatment event;
the diagnosis and treatment experience dimension model construction module is used for carrying out event classification processing on all diagnosis and treatment events based on event attributes in a historical diagnosis and treatment database of each doctor; constructing a diagnosis and treatment experience dimension model for each doctor;
the intelligent hospital platform pushing management module pushes the matched doctors to the user according to department information of pre-hanging diagnosis input by the user in the intelligent hospital platform, and meanwhile, performs data display on the diagnosis and treatment experience dimension model of each matched doctor to assist the user in selecting proper diagnosis and treatment doctors based on the disease conditions of the user.
7. The automated data management system of claim 6, wherein the characteristic diagnosis and treat data extraction management module comprises a characteristic diagnosis and treat data extraction unit and a diagnosis and treat event attribute extraction unit;
the characteristic diagnosis and treatment data extraction unit is used for extracting characteristic diagnosis and treatment data of each diagnosis and treatment event of a doctor;
the diagnosis and treatment event attribute extraction unit is used for receiving the data in the characteristic diagnosis and treatment data extraction unit and extracting corresponding event attributes for each diagnosis and treatment event respectively.
8. The data automation management system according to claim 6, wherein the diagnosis and treatment experience dimension model construction module comprises an event classification processing unit and a diagnosis and treatment experience dimension model construction unit;
the event classifying and processing unit is used for performing event classifying and processing on all diagnosis and treatment events based on event attributes in the historical diagnosis and treatment database of each doctor;
the diagnosis and treatment experience dimension model construction unit is used for receiving the data in the event classification processing unit, calculating experience values for each diagnosis and treatment experience dimension, and constructing a diagnosis and treatment experience dimension model corresponding to each doctor.
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