CN116189873A - Automatic medical information matching method - Google Patents

Automatic medical information matching method Download PDF

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CN116189873A
CN116189873A CN202310106886.7A CN202310106886A CN116189873A CN 116189873 A CN116189873 A CN 116189873A CN 202310106886 A CN202310106886 A CN 202310106886A CN 116189873 A CN116189873 A CN 116189873A
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wireless medical
information
matching
suspected
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刘晓华
匡明
杨文林
李刚
贾彦
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Hangzhou Kangsheng Health Consulting Co Ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a medical information automatic matching method, which comprises the following steps: acquiring health monitoring information sent by all wireless medical devices in a monitoring range, and determining objects of all wireless medical devices only according to signal intensity and arrival angles of all wireless medical devices; or classifying all the wireless medical equipment according to whether the speed is greater than a threshold value, and then determining the object according to the classification; analyzing health monitoring information of the same object to generate suspected etiology; after the suspected etiology and the medical data of all the objects are matched, determining a mode of matching the target remote diagnosis client for each object and determining the target remote diagnosis client; and sending the health monitoring information of the same object to the target remote diagnosis client. The invention enables a matching analysis device to realize the management of the whole monitoring ward object and realize the multi-dimensional matching.

Description

Automatic medical information matching method
Technical Field
The invention belongs to the technical field of computers, and particularly relates to an automatic medical information matching method.
Background
The wearable medical instrument is portable medical or health electronic equipment which can be directly worn on the body, and medical-grade wearable equipment on the market mainly improves the traditional medical instrument to develop towards the wearable, portable and intelligent directions, so that intelligent functions such as physical sign sensing, data recording, data transmission, data analysis and health intervention regulation and control are further increased on the basis of realizing the wearable. For example, wearable medical monitoring devices that can monitor physiological parameters such as electrocardiograph, body temperature, blood glucose, blood pressure, etc. in real time. At present, the clinical common wearable medical devices comprise a continuous blood glucose monitor, an electrocardiogram monitor, a pulse oximeter and a blood pressure monitor, and the wearable medical devices have wide application in the aspects of health monitoring, disease treatment, remote rehabilitation and the like, are convenient for guardianship, can also make up for the deficiency of medical resources, and relieve uneven distribution of the medical resources.
In the prior art, the wearable medical device is generally bound with a corresponding terminal to send information, but in some application scenarios such as a care unit, a plurality of objects need to be monitored, and meanwhile, a plurality of types of wearable devices need to be matched for use to monitor the objects, so that the problem of inconvenient use is easily faced in actual operation, and the method comprises the following steps: the paired wearable devices which are matched with each other are actually worn on different monitored objects to cause errors of results, the pairing management device is used for each set of wearable device to cause great workload of medical staff to be incapable of timely processing various information and the like, and the problems can finally cause the monitored objects to be incapable of being matched with most suitable remote medical resources to cause the monitored objects to be incapable of most timely obtaining the most suitable medical schemes.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an automatic medical information matching method which is applied to a matching analysis device, and the method comprises the following steps:
step S1, health monitoring information sent by all wireless medical equipment in a monitoring range is obtained, and meanwhile, signal intensity and arrival angle corresponding to the health monitoring information sent by the wireless medical equipment are detected and recorded;
step S2, judging whether the objects of all the wireless medical devices can be determined only according to the signal intensity and the arrival angle of all the wireless medical devices, if so, executing step S5, otherwise, executing step S3;
step S3, continuously monitoring health monitoring information sent by all wireless medical equipment, continuously positioning the wireless medical equipment according to signal intensity and arrival angle corresponding to the health monitoring information sent by the wireless medical equipment, and generating speed and acceleration of the wireless medical equipment;
step S4, classifying the wireless medical devices with the speed larger than the threshold value and the wireless medical devices with the speed not larger than the threshold value in all the wireless medical devices, and respectively determining objects of the classified wireless medical devices;
s5, analyzing the health monitoring information of the same object to generate suspected etiology;
step S6, after the suspected etiology and the medical data of all the objects are matched, determining a mode of matching the target remote diagnosis client for each object, and determining the target remote diagnosis client according to the mode of matching the target remote diagnosis client;
and step S7, the health monitoring information of the same object is sent to the target remote diagnosis client.
Wherein the step S2 of determining whether the objects of all the wireless medical devices can be determined only according to the signal intensities and the arrival angles of all the wireless medical devices includes:
s2.1, forming a first vector by the signal intensity and the arrival angle of the wireless medical equipment, and clustering the first vectors of all the wireless medical equipment;
step S2.2, calculating a first average vector of the first vectors of the clustered wireless medical devices;
step S2.3, judging whether the variance of the first vectors of the wireless medical devices clustered into one group and the distance between the first vectors and the first average vector exceed a first threshold value, if so, judging that the objects of all the wireless medical devices cannot be determined only according to the signal intensity and the arrival angle of all the wireless medical devices, otherwise, judging that the objects of all the wireless medical devices can be determined only according to the signal intensity and the arrival angle of all the wireless medical devices;
the magnitude of the first threshold is positively correlated with the vector length of the average vector of the first vector of the wireless medical device, and the first vector is a two-dimensional vector.
Wherein the signal strength corresponding to the health monitoring information is sent according to the wireless medical equipment
Figure SMS_1
And angle of arrival->
Figure SMS_2
Obtaining position coordinates of said wireless medical device +.>
Figure SMS_3
：
Figure SMS_4
；
wherein ,
Figure SMS_5
for the position coordinates of the matching evaluation device, < >>
Figure SMS_6
To match the distance of the analysis means from the wireless medical device, the signal strength is +.>
Figure SMS_7
Distance->
Figure SMS_8
The relationship of (2) is as follows:
Figure SMS_9
wherein the parameters are
Figure SMS_10
Is defined as the received signal strength value at 1m from the transmitting node; />
Figure SMS_11
Is a path loss index whose value depends on the environment and the type of building.
In the step S4, the performing object determination on the classified wireless medical devices respectively includes:
generating a second vector according to the position coordinates, the speed and the acceleration of the wireless medical equipment for the category of the wireless medical equipment with the speed larger than the threshold value, and clustering the second vector to obtain wireless medical equipment corresponding to the mobile individual;
and determining the position coordinates as a third vector for the class of the wireless medical equipment with the residual speed not greater than the threshold value, and clustering the third vector to obtain the wireless medical equipment corresponding to the non-mobile individual.
Wherein the position coordinates of the wireless medical equipment are calculated
Figure SMS_16
Speed->
Figure SMS_17
And acceleration->
Figure SMS_18
Composition of the second vector->
Figure SMS_19
, wherein ,/>
Figure SMS_20
Position coordinates +.>
Figure SMS_21
Speed and velocity of/>
Figure SMS_22
And acceleration->
Figure SMS_12
Corresponding weight, and->
Figure SMS_13
The following relationship is satisfied: />
Figure SMS_14
And->
Figure SMS_15
。
The matching analysis device further comprises a display module, an input module and an interaction module;
the display module is used for displaying the position of the wireless medical equipment in the monitoring range, and is also used for displaying the matching relation between the object and the target remote diagnosis client;
the input module is used for acquiring medical data of an object, and the information in the medical data comprises medical record information, historical diagnosis information and management doctor information;
and the interaction module is used for carrying out interaction according to the prompt of the display module.
The method for generating suspected etiology after analyzing the health monitoring information of the same object comprises the following steps: after all wireless medical equipment of the same object is determined, the health monitoring data of all wireless medical equipment are tidied and input into a pre-selected trained CNN etiology generation model, and suspected etiology is generated.
In the step S6, determining a target remote diagnosis client according to the suspected disease is the object includes:
determining the matching relation between the suspected etiology of all the objects and the medical data, and if matching ambiguity does not exist, determining the target remote diagnosis client comprises:
for any object, determining whether medical data of the object contains information of a nursing doctor, if so, determining a remote diagnosis client corresponding to the nursing doctor as a target remote diagnosis client, otherwise, determining the target remote diagnosis client according to a default mode;
wherein the default manner includes:
after preliminary screening is carried out according to the similarity of departments, features and suspected etiology corresponding to the remote diagnosis client, determining a target remote diagnosis client from preliminary screening results according to queuing waiting time and data transmission time;
wherein the absence of a match ambiguity is:
and generating the number of suspected etiologies according to the CNN etiology generation model, wherein the number of the suspected etiologies is equal to the number of the acquired medical data, and all the medical data information comprises historical diagnosis information and the suspected etiologies are matched with the historical diagnosis information one by one.
If the number of suspected etiologies generated according to the CNN etiology generation model is unequal to the number of the acquired medical data, specifically, the number of the acquired medical data is smaller than the number of the suspected etiologies, matching is carried out according to the acquired medical data, a display module of the matching analysis device carries out input prompt, the input prompt comprises suspected etiologies which are not successfully matched, and matching is carried out again after waiting for input;
if the medical information does not contain the historical diagnosis information, marking and prompting are carried out on a display module of the matching analysis device, the position of the object which does not contain the historical diagnosis information is marked through an interaction module, and the matching analysis device carries out matching again after suspected etiology generated according to the health monitoring information of the wireless medical equipment corresponding to the marked position is removed;
if the suspected etiology and the historical diagnosis information cannot be matched one by one, the method comprises the following steps:
if the same suspected etiology exists and the partial object matching ambiguity caused by the historical diagnosis information exists, prompting the corresponding position of the same historical diagnosis information to be confirmed in the interaction module through the display module;
if the suspected etiology and the historical diagnosis information cannot be matched in a one-to-one correspondence manner, the fact that a new disorder appears in at least some objects is indicated, the object positions corresponding to all medical materials are confirmed in the interaction module in the display module, the object with the new disorder appears is confirmed, the information of a doctor in charge of the object with the new disorder is obtained, if the doctor in charge of the object does not exist, the target remote diagnosis client is confirmed according to the default mode according to the new disorder, if the doctor in charge of the object does not exist, whether the specialty and the department of the doctor in charge of the object cover the new disorder is confirmed, if the doctor in charge of the object does not exist, the doctor in charge of the object is confirmed to be the target remote diagnosis client, and if the doctor in charge of the object does not exist, the target remote diagnosis client is confirmed according to the default mode.
And uploading the health monitoring information of the same object to a background server, and sending the health monitoring information to a target remote diagnosis client by the background server.
Compared with the prior art, the method of the invention can realize the management of the whole monitoring ward object by only one matching analysis device in the monitoring ward, and the matching analysis device can realize multi-dimensional matching, including realizing the automatic matching of a plurality of wireless medical devices and objects through signal monitoring, determining the corresponding conditions of the objects and medical data through the automatic matching of suspected etiology and historical diagnosis information, and matching remote diagnosis data for the objects through the matching conditions of the suspected etiology and the historical diagnosis information.
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The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar or corresponding parts and in which:
fig. 1 is a flowchart illustrating a method of automatic matching of medical information according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two.
It should be understood that although the terms first, second, third, etc. may be used to describe … … in embodiments of the present invention, these … … should not be limited to these terms. These terms are only used to distinguish … …. For example, the first … … may also be referred to as the second … …, and similarly the second … … may also be referred to as the first … …, without departing from the scope of embodiments of the present invention.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product 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 product or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or device comprising such element.
Alternative embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Embodiment 1,
As shown in fig. 1, the invention discloses an automatic medical information matching method, which is applied to a matching analysis device, and comprises the following steps:
step S1, health monitoring information sent by all wireless medical equipment in a monitoring range is obtained, and meanwhile, signal intensity and arrival angle corresponding to the health monitoring information sent by the wireless medical equipment are detected and recorded;
step S2, judging whether the objects of all the wireless medical devices can be determined only according to the signal intensity and the arrival angle of all the wireless medical devices, if so, executing step S5, otherwise, executing step S3;
step S3, continuously monitoring health monitoring information sent by all wireless medical equipment, continuously positioning the wireless medical equipment according to signal intensity and arrival angle corresponding to the health monitoring information sent by the wireless medical equipment, and generating speed and acceleration of the wireless medical equipment;
step S4, classifying the wireless medical devices with the speed larger than the threshold value and the wireless medical devices with the speed not larger than the threshold value in all the wireless medical devices, and respectively determining objects of the classified wireless medical devices;
s5, analyzing the health monitoring information of the same object to generate suspected etiology;
step S6, after the suspected etiology and the medical data of all the objects are matched, determining a mode of matching the target remote diagnosis client for each object, and determining the target remote diagnosis client according to the mode of matching the target remote diagnosis client;
and step S7, the health monitoring information of the same object is sent to the target remote diagnosis client.
The monitoring range of the matching analysis device is application scenes such as a monitoring ward and a monitoring center, a plurality of monitored objects are arranged in the range, each object is provided with a plurality of wireless medical equipment, and the wireless medical equipment is medical wearable equipment and can comprise a continuous blood glucose monitor, an electrocardiogram monitor, a pulse oximeter and a blood pressure monitor. The matching analysis device can receive information sent by all wireless medical equipment of all objects in the monitoring range, and is connected with the wireless medical equipment in a wireless mode, wherein the wireless technology comprises Bluetooth, wifi and the like.
According to the invention, the matching analysis device can automatically match the wireless medical equipment with the object according to the received signal intensity and the arrival angle information, the object in the monitoring ward can freely select the wireless medical equipment with the corresponding function, whether the selected wireless medical equipment is the same set or not is not required to be confirmed before use, and whether the matching relation is established or not is not required, so that the device management in the monitoring ward is greatly facilitated.
The invention can also realize the automatic matching of the medical data and the object, because the object in the monitoring range can move, if the medical data is input in advance according to the situation of position and the like, the subsequent error can be caused, therefore, the object corresponding to the medical data can be automatically judged according to the past diagnosis information in the medical data and the matching of suspected etiology judged by the monitoring data, the manager does not need to check and confirm the corresponding relation between the medical data and the object in real time, the burden of the manager is reduced, the situation of medical resource allocation error caused by the manual input error of the manager can be avoided, and meanwhile, the invention can match proper medical resources for the object according to the comprehensive factors of the medical data, new symptoms, medical queuing situation and the like of the object.
In an embodiment, the determining in the step S2 whether the objects of all the wireless medical devices can be determined only according to the signal intensities and the arrival angles of all the wireless medical devices includes:
s2.1, forming a first vector by the signal intensity and the arrival angle of the wireless medical equipment, and clustering the first vectors of all the wireless medical equipment;
step S2.2, calculating a first average vector of the first vectors of the clustered wireless medical devices;
step S2.3, judging whether the variance of the first vectors of the wireless medical devices clustered into one group and the distance between the first vectors and the first average vector exceed a first threshold value, if so, judging that the objects of all the wireless medical devices cannot be determined only according to the signal intensity and the arrival angle of all the wireless medical devices, otherwise, judging that the objects of all the wireless medical devices can be determined only according to the signal intensity and the arrival angle of all the wireless medical devices;
the magnitude of the first threshold is positively correlated with the vector length of the average vector of the first vector of the wireless medical device, and the first vector is a two-dimensional vector.
Since the medical wearable apparatus cannot exceed a certain range if it is on a subject, whether the distance variance or the distance from the average position, if it exceeds a certain range, it indicates that such a cluster may not coincide with the actual situation.
In one embodiment, the signal strength corresponding to the health monitoring information is sent according to the wireless medical device
Figure SMS_23
And angle of arrival->
Figure SMS_24
Obtaining position coordinates of said wireless medical device +.>
Figure SMS_25
：
Figure SMS_26
；
wherein ,
Figure SMS_27
for the position coordinates of the matching evaluation device, < >>
Figure SMS_28
To match the distance of the analysis means from the wireless medical device, the signal strength is +.>
Figure SMS_29
Distance->
Figure SMS_30
The relationship of (2) is as follows:
Figure SMS_31
wherein the parameters are
Figure SMS_32
Is defined as the received signal strength value at 1m from the transmitting node; />
Figure SMS_33
Is a path loss index whose value depends on the environment and the type of building.
In an embodiment, in the step S4, the performing object determination on the classified wireless medical devices includes:
generating a second vector according to the position coordinates, the speed and the acceleration of the wireless medical equipment for the category of the wireless medical equipment with the speed larger than the threshold value, and clustering the second vector to obtain wireless medical equipment corresponding to the mobile individual;
and determining the position coordinates as a third vector for the class of the wireless medical equipment with the residual speed not greater than the threshold value, and clustering the third vector to obtain the wireless medical equipment corresponding to the non-mobile individual.
The situation that only signal intensity and azimuth angle can be used for reasonable clustering is generally suitable for the situation that the positions of all objects are relatively static, if the objects move, partial wireless medical equipment can be relatively close to each other in positions at some moments, but the wireless medical equipment is not actually worn on the same object, and thus the clustering can generate errors in judging results. Therefore, the matching is performed after the moving object and the non-moving object are classified, so that error occurrence can be reduced, and matching certainty can be increased.
In one embodiment, the location coordinates of the wireless medical device are determined
Figure SMS_35
Speed->
Figure SMS_37
And acceleration
Figure SMS_39
Composition of the second vector->
Figure SMS_41
, wherein ,/>
Figure SMS_42
Position coordinates +.>
Figure SMS_43
Speed->
Figure SMS_44
And acceleration->
Figure SMS_34
Corresponding weight, and->
Figure SMS_36
The following relationship is satisfied:
Figure SMS_38
and->
Figure SMS_40
。
Different weights are configured for position coordinates, speed and acceleration, the weights used in the test are respectively 0.6, 0.3 and 0.1, the weights can be adjusted according to practical experience, the specific selection of the weights is related to the field size and department type of an application scene, and a tester can adjust the weights according to data of a test stage, so that the accuracy of a pairing result is highest.
In one embodiment, the matching analysis device further comprises a display module, an input module and an interaction module;
the display module is used for displaying the position of the wireless medical equipment in the monitoring range, and is also used for displaying the matching relation between the object and the target remote diagnosis client;
the input module is used for acquiring medical data of an object, and the information in the medical data comprises medical record information, historical diagnosis information and management doctor information;
and the interaction module is used for carrying out interaction according to the prompt of the display module.
The input module can be a camera, a scanner and the like, and after the image information of the medical data is acquired, the information in the corresponding medical data is determined through analysis after recognition by OCR, and the analysis method has a plurality of types, such as a regular expression mode, and also comprises an algorithm commonly used for medical information analysis, such as a RNN, LSTM, GRU, BERT, biLSTM model and the like, so that the medical data information can be extracted.
In one embodiment, the analyzing the health monitoring information of the same subject to generate the suspected etiology includes: after all wireless medical equipment of the same object is determined, the health monitoring data of all wireless medical equipment are tidied and input into a pre-selected trained CNN etiology generation model, and suspected etiology is generated.
The CNN is a common neural network algorithm, the health monitoring data can be processed and input according to a preset format to generate suspected etiology, and the data format in the training process is the same as the preset format.
In an embodiment, the determining the target remote viewing client according to the suspected disease in step S6 includes:
determining the matching relation between the suspected etiology of all the objects and the medical data, and if matching ambiguity does not exist, determining the target remote diagnosis client comprises:
for any object, determining whether medical data of the object contains information of a nursing doctor, if so, determining a remote diagnosis client corresponding to the nursing doctor as a target remote diagnosis client, otherwise, determining the target remote diagnosis client according to a default mode;
wherein the default manner includes:
after preliminary screening is carried out according to the similarity of departments, features and suspected etiology corresponding to the remote diagnosis client, determining a target remote diagnosis client from preliminary screening results according to queuing waiting time and data transmission time;
wherein the absence of a match ambiguity is:
and generating the number of suspected etiologies according to the CNN etiology generation model, wherein the number of the suspected etiologies is equal to the number of the acquired medical data, and all the medical data information comprises historical diagnosis information and the suspected etiologies are matched with the historical diagnosis information one by one.
For example, if the monitoring center has five objects clustered, the suspected etiology analyzed by the health monitoring data of the five objects is ABCDE, and the historical diagnosis information in the corresponding medical information is ABCDE, the medical information and the objects can be quickly corresponding, and the remote medical resources can be quickly allocated according to the information of the medical doctors in the medical information, or the medical resources can be quickly allocated according to a default mode.
In an embodiment, if the number of suspected etiologies generated according to the CNN etiology generating model is not equal to the number of acquired medical data, specifically, the number of acquired medical data is less than the number of suspected etiologies, matching is performed according to the acquired medical data, a prompt is input to a display module of the matching analysis device, the input prompt includes suspected etiologies which are not successfully matched, and matching is performed again after waiting for input;
for example, the monitoring center has five objects clustered, the suspected etiology analyzed by the health monitoring data of the five objects is ABCDE, but the medical data is only four, and the history diagnosis information in the corresponding medical information is ABCD, so that the suspected disease is possible because the medical data of the object of E is not recorded, the display module is used for reminding that the medical data of the object is not recorded, and prompting that the suspected etiology E is likely to be beneficial to enabling the manager to quickly find the object which is not recorded.
If the medical information does not contain the historical diagnosis information, marking and prompting are carried out on a display module of the matching analysis device, the position of the object which does not contain the historical diagnosis information is marked through an interaction module, and the matching analysis device carries out matching again after suspected etiology generated according to the health monitoring information of the wireless medical equipment corresponding to the marked position is removed;
for example, the monitoring center has five objects clustered, the suspected etiology analyzed by the health monitoring data of the five objects is ABCDE, the medical materials are five, but only four history diagnosis information in the corresponding medical information indicate that one medical material has no history diagnosis information, at the moment, marking prompt is carried out on the display module, a manager can use the interaction module to mark the object without the history diagnosis information, the interaction module can be a touch screen, a mouse, an operation rod and the like, the manager can mark the area information of the corresponding object on the monitoring center map on the display module, the matching analysis device judges which object has no history diagnosis information according to the position coordinates of different wireless medical devices, and after the suspected etiology information of the object is eliminated, the rest of the objects are matched again.
If the suspected etiology and the historical diagnosis information cannot be matched one by one, the method comprises the following steps:
if the same suspected etiology exists and the partial object matching ambiguity caused by the historical diagnosis information exists, prompting the corresponding position of the same historical diagnosis information to be confirmed in the interaction module through the display module;
for example, the monitoring center has five objects clustered, the suspected etiology analyzed by the health monitoring data of the five objects is AACDE, the history diagnosis information of the medical data is also five AACDE, and the matching is correct at this time, but two corresponding objects of a cannot be determined, and at this time, the matching is performed by indicating the positions of the two objects with the history diagnosis information of a by a manager.
If the suspected etiology and the historical diagnosis information cannot be matched in a one-to-one correspondence manner, the fact that a new disorder appears in at least some objects is indicated, the object positions corresponding to all medical materials are confirmed in the interaction module in the display module, the object with the new disorder appears is confirmed, the information of a doctor in charge of the object with the new disorder is obtained, if the doctor in charge of the object does not exist, the target remote diagnosis client is confirmed according to the default mode according to the new disorder, if the doctor in charge of the object does not exist, whether the specialty and the department of the doctor in charge of the object cover the new disorder is confirmed, if the doctor in charge of the object does not exist, the doctor in charge of the object is confirmed to be the target remote diagnosis client, and if the doctor in charge of the object does not exist, the target remote diagnosis client is confirmed according to the default mode.
For example, the monitoring center has five objects clustered, the suspected etiology analyzed by the health monitoring data of the five objects is ABCDE, the history diagnosis information of the medical data is five ABCDE, the matching is incorrect at this time, the situation that new symptoms are likely to occur is indicated, at this time, the manager needs to confirm the corresponding relation between the positions of the objects in the current monitoring center and the medical data, the objects with new symptoms are arranged, and then the target remote diagnosis client is distributed according to the corresponding rule.
In one embodiment, the health monitoring information of the same object is uploaded to a background server, and the background server sends the health monitoring information to a target remote diagnosis client.
Compared with the prior art, the method of the invention can realize the management of the whole monitoring ward object by only one matching analysis device in the monitoring ward, and the matching analysis device can realize multi-dimensional matching, including realizing the automatic matching of a plurality of wireless medical devices and objects through signal monitoring, determining the corresponding conditions of the objects and medical data through the automatic matching of suspected etiology and historical diagnosis information, and matching remote diagnosis data for the objects through the matching conditions of the suspected etiology and the historical diagnosis information.
The disclosed embodiments provide a non-transitory computer storage medium storing computer executable instructions that perform the method steps described in the embodiments above.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local Area Network (AN) or a Wide Area Network (WAN), or can be connected to AN external computer (for example, through the Internet using AN Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The foregoing description of the preferred embodiments of the present invention has been presented for purposes of clarity and understanding, and is not intended to limit the invention to the particular embodiments disclosed, but is intended to cover all modifications, alternatives, and improvements within the spirit and scope of the invention as outlined by the appended claims.

Claims (10)

1. An automatic medical information matching method is applied to a matching analysis device, and comprises the following steps:
step S1, health monitoring information sent by all wireless medical equipment in a monitoring range is obtained, and meanwhile, signal intensity and arrival angle corresponding to the health monitoring information sent by the wireless medical equipment are detected and recorded;
step S2, judging whether the objects of all the wireless medical devices can be determined only according to the signal intensity and the arrival angle of all the wireless medical devices, if so, executing step S5, otherwise, executing step S3;
step S3, continuously monitoring health monitoring information sent by all wireless medical equipment, continuously positioning the wireless medical equipment according to signal intensity and arrival angle corresponding to the health monitoring information sent by the wireless medical equipment, and generating speed and acceleration of the wireless medical equipment;
step S4, classifying the wireless medical devices with the speed larger than the threshold value and the wireless medical devices with the speed not larger than the threshold value in all the wireless medical devices, and respectively determining objects of the classified wireless medical devices;
s5, analyzing the health monitoring information of the same object to generate suspected etiology;
step S6, after the suspected etiology and the medical data of all the objects are matched, determining a mode of matching the target remote diagnosis client for each object, and determining the target remote diagnosis client according to the mode of matching the target remote diagnosis client;
and step S7, the health monitoring information of the same object is sent to the target remote diagnosis client.
2. The method according to claim 1, wherein the step S2 of determining whether the objects of all wireless medical devices can be determined based on only the signal strengths and angles of arrival of all wireless medical devices comprises:
s2.1, forming a first vector by the signal intensity and the arrival angle of the wireless medical equipment, and clustering the first vectors of all the wireless medical equipment;
step S2.2, calculating a first average vector of the first vectors of the clustered wireless medical devices;
step S2.3, judging whether the variance of the first vectors of the wireless medical devices clustered into one group and the distance between the first vectors and the first average vector exceed a first threshold value, if so, judging that the objects of all the wireless medical devices cannot be determined only according to the signal intensity and the arrival angle of all the wireless medical devices, otherwise, judging that the objects of all the wireless medical devices can be determined only according to the signal intensity and the arrival angle of all the wireless medical devices;
the magnitude of the first threshold is positively correlated with the vector length of the average vector of the first vector of the wireless medical device, and the first vector is a two-dimensional vector.
3. The method of claim 1, wherein the signal strength corresponding to the health monitoring information is transmitted in accordance with the wireless medical device
Figure QLYQS_1
And angle of arrival->
Figure QLYQS_2
Obtaining position coordinates of said wireless medical device +.>
Figure QLYQS_3
：
Figure QLYQS_4
；
wherein ,
Figure QLYQS_5
for the position coordinates of the matching evaluation device, < >>
Figure QLYQS_6
To match the distance of the analysis means from the wireless medical device, the signal strength is +.>
Figure QLYQS_7
Distance->
Figure QLYQS_8
The relationship of (2) is as follows:
Figure QLYQS_9
wherein the parameters are
Figure QLYQS_10
Is defined as the received signal strength value at 1m from the transmitting node; />
Figure QLYQS_11
Is a path loss index whose value depends on the environment and the type of building.
4. The method according to claim 1, wherein the determining the object of the classified wireless medical devices in step S4 includes:
generating a second vector according to the position coordinates, the speed and the acceleration of the wireless medical equipment for the category of the wireless medical equipment with the speed larger than the threshold value, and clustering the second vector to obtain wireless medical equipment corresponding to the mobile individual;
and determining the position coordinates as a third vector for the class of the wireless medical equipment with the residual speed not greater than the threshold value, and clustering the third vector to obtain the wireless medical equipment corresponding to the non-mobile individual.
5. The method of claim 4, wherein,
coordinates of the position of the wireless medical device
Figure QLYQS_13
Speed->
Figure QLYQS_17
And acceleration->
Figure QLYQS_18
Composition of the second vector
Figure QLYQS_19
, wherein ,/>
Figure QLYQS_20
Position coordinates +.>
Figure QLYQS_21
Speed->
Figure QLYQS_22
And acceleration->
Figure QLYQS_12
Corresponding weight, and->
Figure QLYQS_14
The following relationship is satisfied: />
Figure QLYQS_15
And (2) and
Figure QLYQS_16
。
6. the method of claim 1, wherein the match analysis device further comprises a display module, an entry module, and an interaction module;
the display module is used for displaying the position of the wireless medical equipment in the monitoring range, and is also used for displaying the matching relation between the object and the target remote diagnosis client;
the input module is used for acquiring medical data of an object, and the information in the medical data comprises medical record information, historical diagnosis information and management doctor information;
and the interaction module is used for carrying out interaction according to the prompt of the display module.
7. The method of claim 1, wherein analyzing the health monitoring information of the same subject to generate a suspected etiology comprises: after all wireless medical equipment of the same object is determined, the health monitoring data of all wireless medical equipment are tidied and input into a pre-selected trained CNN etiology generation model, and suspected etiology is generated.
8. The method according to any one of claims 1, 6 and 7, wherein determining a target remote viewing client from the subject based on the suspected pathogen in step S6 comprises:
determining the matching relation between the suspected etiology of all the objects and the medical data, and if matching ambiguity does not exist, determining the target remote diagnosis client comprises:
for any object, determining whether medical data of the object contains information of a nursing doctor, if so, determining a remote diagnosis client corresponding to the nursing doctor as a target remote diagnosis client, otherwise, determining the target remote diagnosis client according to a default mode;
wherein the default manner includes:
after preliminary screening is carried out according to the similarity of departments, features and suspected etiology corresponding to the remote diagnosis client, determining a target remote diagnosis client from preliminary screening results according to queuing waiting time and data transmission time;
wherein the absence of a match ambiguity is:
and generating the number of suspected etiologies according to the CNN etiology generation model, wherein the number of the suspected etiologies is equal to the number of the acquired medical data, and all the medical data information comprises historical diagnosis information and the suspected etiologies are matched with the historical diagnosis information one by one.
9. The method of claim 8, wherein,
if the number of suspected etiologies generated according to the CNN etiology generation model is unequal to the number of the acquired medical data, specifically that the number of the acquired medical data is smaller than the number of the suspected etiologies, matching is carried out according to the acquired medical data, a recording prompt is carried out on a display module of the matching analysis device, the recording prompt comprises suspected etiologies which are not successfully matched, and the matching is carried out again after waiting for recording;
if the medical information does not contain the historical diagnosis information, marking and prompting are carried out on a display module of the matching analysis device, the position of the object which does not contain the historical diagnosis information is marked through an interaction module, and the matching analysis device carries out matching again after suspected etiology generated according to the health monitoring information of the wireless medical equipment corresponding to the marked position is removed;
if the suspected etiology and the historical diagnosis information cannot be matched one by one, the method comprises the following steps:
if the same suspected etiology exists and the partial object matching ambiguity caused by the historical diagnosis information exists, prompting the corresponding position of the same historical diagnosis information to be confirmed in the interaction module through the display module;
if the suspected etiology and the historical diagnosis information cannot be matched in a one-to-one correspondence manner, the fact that a new disorder appears in at least some objects is indicated, the object positions corresponding to all medical materials are confirmed in the interaction module in the display module, the object with the new disorder appears is confirmed, the information of a doctor in charge of the object with the new disorder is obtained, if the doctor in charge of the object does not exist, the target remote diagnosis client is confirmed according to the default mode according to the new disorder, if the doctor in charge of the object does not exist, whether the specialty and the department of the doctor in charge of the object cover the new disorder is confirmed, if the doctor in charge of the object does not exist, the doctor in charge of the object is confirmed to be the target remote diagnosis client, and if the doctor in charge of the object does not exist, the target remote diagnosis client is confirmed according to the default mode.
10. The method of claim 1, wherein,
and uploading the health monitoring information of the same object to a background server, and sending the health monitoring information to a target remote diagnosis client by the background server.
CN202310106886.7A 2023-02-14 2023-02-14 Automatic medical information matching method Pending CN116189873A (en)

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