CN117393133B - Emergency rescue informatization management system for inpatients based on reasoning - Google Patents
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Classifications
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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
- G16H40/20—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 management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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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
- G16H40/40—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 management of medical equipment or devices, e.g. scheduling maintenance or upgrades
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- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention provides an inference-based hospitalized patient emergency rescue informatization management system, which comprises: the system comprises a rescue information acquisition unit, a control unit and a control unit, wherein the rescue information acquisition unit is used for acquiring the rescue information of a patient to be rescued, and the rescue information comprises patient identity information and illness state information; the vital sign recording unit is used for collecting and recording actual measurement sign data sequences of the patient in the rescuing process; the inference unit is used for acquiring a current treatment measure and a current actually measured sign data sequence, inferring an expected treatment effect of the treatment measure by adopting an inference machine and an expert knowledge database based on the current treatment measure and rescue information, obtaining an expected sign data sequence, comparing the expected sign data sequence with the actually measured sign data sequence, and evaluating the effect of the current treatment measure in real time. The invention realizes standardization and intellectualization of the rescue process and reduces the probability of medical error in the rescue process.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an inference-based emergency rescue informatization management system for inpatients.
Background
The emergency rescue is often carried out on critical patients, under the conditions of short time, urgent task and large workload, the emergency rescue work must be orderly carried out according to the characteristics and the working specification of the emergency rescue work, so that the patients can be ensured to be diagnosed and treated timely and correctly, and the lives of the patients can be saved to the greatest extent. At present, in the rescuing process, partial medical staff still have the handwriting recording working mode, recording errors are easy to occur, standard tables are not unified, and a great deal of time is easy to waste.
In addition, the existing monitoring equipment and the information system cannot be in butt joint, and automatic on-machine acquisition and recording of patient sign data and treatment data cannot be achieved. Rescue data are numerous, but the data are poor in systematicness and low in integration level, so that the data are difficult to monitor and use, and medical errors are easy to generate.
Disclosure of Invention
The invention provides an inference-based hospitalized patient emergency rescue informatization management system, which aims to improve at least one of the technical problems.
The embodiment of the invention provides an inference-based hospitalized patient emergency rescue informatization management system, which comprises:
the system comprises a rescue information acquisition unit, a control unit and a control unit, wherein the rescue information acquisition unit is used for acquiring the rescue information of a patient to be rescued, and the rescue information comprises patient identity information and illness state information;
the timing unit is used for calculating the rescue start-stop time and the time of each treatment measure for the patient in rescue;
the vital sign recording unit is used for collecting and recording actual measurement sign data sequences of the patient in the rescuing process;
the inference unit is used for acquiring a current treatment measure and a current actually measured sign data sequence, inferring an expected treatment effect of the treatment measure by adopting an inference machine and an expert knowledge database based on the current treatment measure and rescue information to acquire an expected sign data sequence, comparing the expected sign data sequence with the actually measured sign data sequence, evaluating the effect of the current treatment measure in real time, and recommending the next treatment measure according to an evaluation result; the reasoning unit is specifically configured to: inputting the rescue information, the current measured sign data sequence and the treatment measure into an expert knowledge database to obtain an expected sign data sequence corresponding to the treatment measure; matching the expected sign data sequence with the actual measured sign data sequence, and evaluating the effect of the current treatment measure in real time according to the matching result; wherein if the result of the matching indicates thatIf the measured physical sign data sequence is abnormal, the current treatment measures are not good in effect; otherwise, the current treatment measures are indicated to be good in effect; when the current treatment measures are judged to be poor in effect, recommending the next treatment measure based on the measured sign data and the expert knowledge database; wherein, upon matching the expected and measured vital sign data sequences: acquiring the generation frequency of the expected sign data sequence and the sampling frequency of the measured sign data sequence; calculating the sampling frequency and generating a ratio k of the frequency; averaging each k of the measured sign data sequences to obtain an average sign data sequence; matching the expected sign data sequence and the average sign data sequence; upon matching the expected sign data sequence and the average sign data sequence: acquiring an expected fitting equation corresponding to the expected sign data sequence; acquiring an average fitting equation corresponding to the average sign data sequence and a correlation coefficient R; for the current time t, calculating a first distance sum s between the expected sign data sequence and the average sign data sequence at the time t-n to the time t 1 The method comprises the steps of carrying out a first treatment on the surface of the For the current time t, calculating a second distance sum s of the expected fit equation and the average fit equation from t to the future time t+n 2 The method comprises the steps of carrying out a first treatment on the surface of the According to the first distance sum s 1 Second distance sum s 2 And a correlation coefficient R, obtaining a matching result; the result of the matching is the total distance sum s=s 1 +R×s 2 ；
The recording unit is used for generating rescue records according to various data generated in the rescue process.
Preferably, the method further comprises:
and the drug administration unit is used for acquiring the recommended drug quantity and the dosage volume of the rescue drug, and automatically converting the drug administration rate according to the recommended drug quantity, the dosage volume, the push injection pump speed and the weight of the patient.
Preferably, the rate of administrationThe method comprises the steps of carrying out a first treatment on the surface of the Wherein B is recommended dosage of the rescue medicine, the unit is mg, AThe unit of the dispensing volume of the rescue medicine is ml; c is the speed of the bolus pump in +.>The method comprises the steps of carrying out a first treatment on the surface of the D is the weight of the patient, and the unit is kg; drug administration rate->Is expressed in ug/kg.min.
Preferably, the recording unit is specifically configured to:
based on the treatment measures and the rescue drugs used in the rescue operation, a rescue list and a rescue consumable part list are automatically generated and pushed to the corresponding doctor end to open the doctor order;
based on the process of rescue operation and physical sign data, automatically generating a rescue record and pushing the rescue record to the doctor end, and simultaneously carrying the rescue record into a nursing record list in batches.
Preferably, the method further comprises:
and the reminding unit is used for setting the countdown of the write-back record according to a preset case write-back rule and reminding a doctor of completing the medical advice which is not confirmed to be issued.
In summary, the embodiment can automatically, accurately and rapidly collect patient data by means of an inference system to automatically evaluate the illness state and provide necessary auxiliary decision support aiming at the conditions of short emergency rescue time, urgent task and large workload; the system can provide data of a breathing machine, a bedside monitor and the like, and form a record list by combining related records; ensure the medication safety and avoid the medication error condition. Not only is the simple rescue process computerized, but also the management concept and the information technology are integrated, and all links are closely connected and are buckled by establishing a rescue management information system and a rescue equipment object classification frame. In the management method, the empirical management is changed to normalized management, thereby meeting the working requirements of medical staff, realizing the normalization of the rescue process, reducing medical errors, ensuring the reality, objectivity, completeness and accuracy of the rescue record, improving the working efficiency and medical quality of the medical staff and improving the working environment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of an inference-based inpatient emergency rescue informatization management system according to a first embodiment of the present invention.
Fig. 2 is a workflow diagram of an inpatient emergency rescue informatization management system.
Fig. 3 is a system architecture diagram of an in-patient emergency rescue information management system.
Fig. 4 is a schematic diagram of treatment recommendation and recording.
Fig. 5 is a schematic diagram of the operation of the dosing unit.
Fig. 6 is a schematic diagram of a generated rescue record.
FIG. 7 is a schematic illustration of a generated order.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. 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 to 3, a first embodiment of the present invention provides an inference-based hospitalized patient emergency rescue informatization management system, which includes:
a rescue information acquisition unit 10 for acquiring rescue information of a patient to be rescued, the rescue information including patient identity information and illness state information.
In this embodiment, the patient identification information may include, for example, the patient's sex, age group, height weight, and the like. The condition information may include the type of illness and symptoms of the patient, etc. Wherein the illness state information can be acquired through voice recognition, for example, in the emergency treatment process, the responsible doctor can state the illness state information of the patient through voice, and the illness state information is picked up by the emergency information acquisition unit 10.
For example, prior to surgery, a physician may state by spoken language: patient's abdominal bleeding, serious blood loss, coma and other illness information.
The timing unit 20 is used for calculating the rescue start-stop time and each treatment time of the patient in rescue.
In this embodiment, the system for managing the emergency and rescue information of the inpatients can be provided with a visual operation interface, and when the patient starts to rescue, the medical staff can time by starting the corresponding control on the operation interface, such as the rescue starting control. In addition, for each treatment in the rescue process, such as cardiopulmonary resuscitation, electric defibrillation, a respirator and the like, the timing can be started when the device is started, and the timing is stopped after the device is ended, so that the time for starting and stopping the rescue and the time for each treatment measure can be obtained.
A vital sign recording unit 30 for acquiring and recording the actual measured vital sign data sequence of the patient during the rescue.
In this embodiment, the emergency rescue informatization management system for inpatients may be connected to each detection device in the rescue room, such as an electrocardiograph detection device, a respiratory detection device, etc., so as to obtain, in real time, sign data detected by each detection device, such as blood pressure, respiration, blood oxygen, heart rate, etc.
The inference unit 40 is configured to obtain a current treatment measure and a current actually measured sign data sequence, infer an expected treatment effect of the treatment measure based on the current treatment measure and rescue information by using an inference engine and an expert knowledge database, obtain an expected sign data sequence, compare the expected sign data sequence with the actually measured sign data sequence, evaluate an effect of the current treatment measure in real time, and recommend a next treatment measure according to an evaluation result.
In this embodiment, the inference unit 40 is specifically configured to:
firstly, inputting the rescue information, the current measured sign data sequence and the treatment measure into an expert knowledge database to acquire an expected sign data sequence corresponding to the treatment measure.
In this embodiment, the inference engine performs the solution to the input information according to the input information of the user and the knowledge in the expert knowledge database under a certain control policy or inference rule, so as to obtain the corresponding answer.
In the above embodiment, the user input information is rescue information, the current actually measured sign data sequence and the treatment measures, and the inference engine can infer that the expected change of the sign data corresponding to the treatment measures is obtained under normal conditions based on the preset inference rule and the expert knowledge database, for example, when the user stops the heart, if cardiopulmonary resuscitation is performed, the heart should gradually resume beating within a predetermined time. When the user is in dyspnea and rapid, if the user accesses the breathing machine, the breathing should be gradually smoothed within a preset time. When the blood is lost too much, the blood pressure should gradually rise after blood transfusion is performed. It will be appreciated that the expected change in sign data will be different for different patients (e.g., patients of different ages, sexes, body weights) and for different condition information, which can be obtained by the inference engine and expert knowledge database.
Then, matching the expected sign data sequence with the actual measured sign data sequence, and evaluating the effect of the current treatment measure in real time according to a matching result; if the matching indicates that the measured physical sign data sequence is abnormal, the current treatment measure is not good in effect; otherwise, the current treatment measures are indicated to be good.
In this embodiment, since the sampling frequency of the measured sign data sequence is determined by the sampling frequency of the corresponding sensor, and the generating frequency of the expected sign data sequence is predefined, the two are generally different, and the sampling frequency of the measured sign data sequence is often greater than the generating frequency of the expected sign data sequence, so that the two sequences need to be aligned.
To this end, when matching the expected and measured vital sign data sequences: firstly, acquiring the generation frequency of the expected sign data sequence and the sampling frequency of the actually measured sign data sequence, then calculating the ratio k of the sampling frequency to the generation frequency, and then averaging every k sign data of the actually measured sign data sequence to obtain an average sign data sequence; at this time, the sampling frequencies of the two sequences are equal, and the expected sign data sequence and the average sign data sequence can be matched.
It will be appreciated that in this embodiment, averaging every k of measured vital sign data may also avoid the effect of some anomaly data on the final matching result due to sensor anomalies.
In this embodiment, when matching the expected sign data sequence and the average sign data sequence:
first, an expected fit equation corresponding to the expected sign data sequence is obtained.
Then, an average fitting equation corresponding to the average sign data sequence and a correlation coefficient R are obtained.
Then, for the current time t, calculating a first distance sum s between the expected sign data sequence and the average sign data sequence at times t-n to t 1 。
Assuming that n data are exactly present at the time t-n to t (i.e. the expected sign data sequence takes n data, the average sign data sequence takes the corresponding n data), the distances of the n data of the two sequences are calculated, and then summed to obtain a first distance sum s 1 . The distance between the two data may be the absolute value of the two data or the square difference, and the present invention is not particularly limited.
In the present embodiment, the first distance sum s 1 Mainly used for measuring two numbersIt is evident from the sequence that the greater the degree of deviation at the present level, the greater the gap between the present treatment and its intended effect.
Then, at the current time t, calculating a second distance sum s of the expected fit equation and the average fit equation from t to the future time t+n 2 。
Similarly, assuming that there are exactly n data at the time t to t+n (i.e., the expected sign data sequence takes n data, the average sign data sequence takes the corresponding n data), the distances of the n data of the two sequences are calculated, and then summed to obtain the second distance sum s 2 。
In the present embodiment, the second distance sum s 2 The method is mainly used for measuring the deviation degree of two data sequences at future time, and obviously, the larger the deviation degree is, the larger the difference between the current treatment measure and the expected effect is.
Here a second distance sum s is introduced 2 The method is mainly used for predicting the future effect of the current treatment measure, if the future effect deviates to a large extent, the current treatment measure is possibly problematic, and medical staff can adjust the treatment measure in advance according to the current treatment measure, so that the efficiency and the success rate of emergency treatment are improved.
And then, according to the first distance sum s 1 Second distance sum s 2 And a correlation coefficient R, obtaining a matching result.
In this embodiment, for example, the result of the matching may be set to be the first distance sum s 1 R×s 2 The result of the matching s=s may be set 1 +R×s 2 . Wherein the correlation coefficient R is used for measuring the correlation of the average fitting equation, and is smaller than or equal to 1, the smaller the correlation coefficient R is, the larger the distribution difference between the fitting equation and the actual data points is, so that the fitting equation and the second distance sum s are needed to be calculated 2 Multiplying to reduce errors.
In this embodiment, if the matching result is greater than the set threshold, such as the first distance and s 1 、R×s 2 Or(s) 1 +R×s 2 ) If the measured sign data sequence is larger than the set threshold value, the measured sign data sequence is indicated to have abnormality,the current treatment measures are indicated to be poor; otherwise, the current treatment measures are indicated to be good.
Wherein, in particular, the result of the matching is (s 1 +R×s 2 ) I.e. taking into account both the current degree of deviation and the future degree of deviation.
And finally, recommending a next treatment measure based on the measured sign data and the expert knowledge database when the current treatment measure is judged to be poor in effect.
In this embodiment, if the current treatment effect is not good, the system may recommend a next treatment measure based on the measured sign data and the expert knowledge database, where the next treatment measure may be a replacement treatment measure, or may be a parameter for adjusting the current treatment measure, such as increasing the supply of the ventilator, increasing the transfusion amount, and so on.
As shown in fig. 4, in this embodiment, the medical staff may perform corresponding treatment according to the treatment measures recommended by the inference system, or may perform corresponding treatment according to his own judgment, and the system will record the treatment of the medical staff in sequence.
In this embodiment, the inference rule of the inference system may be updated according to the actual operation, so as to improve the accuracy of inference. Specifically, the actual decision of medical staff and the final rescuing effect of the patient can be obtained, and the parameters and association rules of the inference engine can be adjusted.
The recording unit 50 is used for generating rescue records according to various data generated in the rescue process.
Specifically, as shown in fig. 6 and 7, the recording unit 50 automatically generates a rescue list and a rescue consumable part list based on treatment measures used in rescue operation and rescue medicines, and directly pushes the list and the consumable part list to a corresponding doctor end to issue medical orders, and automatically generates a rescue record based on the process of the rescue operation and physical sign data, pushes the rescue record to the doctor end, and simultaneously brings the rescue record list into a batch.
Preferably, the device further comprises a drug administration unit, wherein the drug administration unit is used for acquiring the recommended drug quantity and the dosage volume of the rescue drug, and automatically converting the drug administration rate according to the recommended drug quantity, the dosage volume, the push injection pump speed and the weight of the patient.
As shown in fig. 5, the medication order is formed by structurally splicing the selection input of the nurse. The multiple medicines can be configured by clicking the plus sign, the medicine delivery mode can be selected, the speed can be input or the full speed can be selected, the weight system can be automatically brought in, the medicine delivery rate of the pump can be automatically calculated after the calibration and the push pump speed are checked, and the rescue record remarks can be brought in by clicking the storage. When the rescue record of the drug administration is clicked and modified, the drug administration jumps to the inside of the in-out amount, and if the patient does not record the in-out amount doctor's advice, the patient does not carry the record.
For example, a certain rescue drug with 0.9% NS (5% GS) +Bmg is prepared into Aml, the speed of a push injection pump is C ml/h, the weight of a patient is D kg, the administration rate is X, and the formula is calculated as follows:
the unit is:
preferably, the medical advice prompt system further comprises a prompt unit, wherein the prompt unit is used for setting the countdown of the supplementary note according to the preset case supplementary note rule and prompting a doctor to finish the medical advice which is not confirmed.
Here, the preset case remedying rule may be, for example, "6-hour medical record remedying rule". In particular depending on different hospital or department settings.
In summary, the embodiment can automatically, accurately and rapidly collect patient data by means of an inference system to automatically evaluate the illness state and provide necessary auxiliary decision support aiming at the conditions of short emergency rescue time, urgent task and large workload; the system can provide data of a breathing machine, a bedside monitor and the like, and form a record list by combining related records; ensure the medication safety and avoid the medication error condition. Not only is the simple rescue process computerized, but also the management concept and the information technology are integrated, and all links are closely connected and are buckled by establishing a rescue management information system and a rescue equipment object classification frame. In the management method, the empirical management is changed to normalized management, thereby meeting the working requirements of medical staff, realizing the normalization of the rescue process, reducing medical errors, ensuring the reality, objectivity, completeness and accuracy of the rescue record, improving the working efficiency and medical quality of the medical staff and improving the working environment.
In the embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus and computer program products according to various embodiments of the present invention. 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.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes. It should be noted that, in this document, 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
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.
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.
Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". 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.
References to "first\second" in the embodiments are merely to distinguish similar objects and do not represent a particular ordering for the objects, it being understood that "first\second" may interchange a particular order or precedence where allowed. It is to be understood that the "first\second" distinguishing aspects may be interchanged where appropriate, such that the embodiments described herein may be implemented in sequences other than those illustrated or described herein.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. 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 (5)
1. An inference-based emergency rescue informatization management system for inpatients, comprising:
the system comprises a rescue information acquisition unit, a control unit and a control unit, wherein the rescue information acquisition unit is used for acquiring the rescue information of a patient to be rescued, and the rescue information comprises patient identity information and illness state information;
the timing unit is used for calculating the rescue start-stop time and the time of each treatment measure for the patient in rescue;
the vital sign recording unit is used for collecting and recording actual measurement sign data sequences of the patient in the rescuing process;
an inference unit for obtaining current treatment measures and current measured sign data sequences, inferring the treatment measures based on the current treatment measures and rescue information by using an inference engine and an expert knowledge databaseThe method comprises the steps of obtaining expected sign data sequences according to the treatment effect of a period, comparing the expected sign data sequences with the actually measured sign data sequences, evaluating the effect of current treatment measures in real time, and recommending the next treatment measure according to the evaluation result; the reasoning unit is specifically configured to: inputting the rescue information, the current measured sign data sequence and the treatment measure into an expert knowledge database to obtain an expected sign data sequence corresponding to the treatment measure; matching the expected sign data sequence with the actual measured sign data sequence, and evaluating the effect of the current treatment measure in real time according to the matching result; if the matching result shows that the actually measured physical sign data sequence is abnormal, the current treatment measure is poor in effect; otherwise, the current treatment measures are indicated to be good in effect; when the current treatment measures are judged to be poor in effect, recommending the next treatment measure based on the measured sign data and the expert knowledge database; wherein, upon matching the expected and measured vital sign data sequences: acquiring the generation frequency of the expected sign data sequence and the sampling frequency of the measured sign data sequence; calculating the sampling frequency and generating a ratio k of the frequency; averaging each k of the measured sign data sequences to obtain an average sign data sequence; matching the expected sign data sequence and the average sign data sequence; upon matching the expected sign data sequence and the average sign data sequence: acquiring an expected fitting equation corresponding to the expected sign data sequence; acquiring an average fitting equation corresponding to the average sign data sequence and a correlation coefficient R; for the current time t, calculating a first distance sum s between the expected sign data sequence and the average sign data sequence at the time t-n to the time t 1 The method comprises the steps of carrying out a first treatment on the surface of the For the current time t, calculating a second distance sum s of the expected fit equation and the average fit equation from t to the future time t+n 2 The method comprises the steps of carrying out a first treatment on the surface of the According to the first distance sum s 1 Second distance sum s 2 And a correlation coefficient R, obtaining a matching result; the result of the matching is the total distance sum s=s 1 +R×s 2 ；
The recording unit is used for generating rescue records according to various data generated in the rescue process.
2. The inference-based hospitalized patient emergency rescue informatization management system of claim 1, further comprising:
and the drug administration unit is used for acquiring the recommended drug quantity and the dosage volume of the rescue drug, and automatically converting the drug administration rate according to the recommended drug quantity, the dosage volume, the push injection pump speed and the weight of the patient.
3. The inference-based hospitalized patient emergency rescue informatization management system of claim 2, wherein the rate of administration isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein B is the recommended dosage of the rescue medicine, the unit is mg, A is the dosage volume of the rescue medicine, and the unit is ml; c is the speed of the bolus pump in +.>The method comprises the steps of carrying out a first treatment on the surface of the D is the weight of the patient, and the unit is kg; drug administration rate->Is expressed in ug/kg.min.
4. The inference-based hospitalized patient emergency rescue informatization management system according to claim 1, wherein said recording unit is specifically adapted to:
based on the treatment measures and the rescue drugs used in the rescue operation, a rescue list and a rescue consumable part list are automatically generated and pushed to the corresponding doctor end to open the doctor order;
based on the process of rescue operation and physical sign data, automatically generating a rescue record and pushing the rescue record to the doctor end, and simultaneously carrying the rescue record into a nursing record list in batches.
5. The inference-based hospitalized patient emergency rescue informatization management system of claim 1, further comprising:
and the reminding unit is used for setting the countdown of the write-back record according to a preset case write-back rule and reminding a doctor of completing the medical advice which is not confirmed to be issued.
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| KR20220131480A (en) * | 2021-03-21 | 2022-09-28 | 강민호 | Emergency patient diagnosis system |
| CN116389647A (en) * | 2023-06-02 | 2023-07-04 | 深圳市尚哲医健科技有限责任公司 | Emergency first-aid integrated platform |
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