CN118197650B - Intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery - Google Patents
Intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery Download PDFInfo
- Publication number
- CN118197650B CN118197650B CN202410612981.9A CN202410612981A CN118197650B CN 118197650 B CN118197650 B CN 118197650B CN 202410612981 A CN202410612981 A CN 202410612981A CN 118197650 B CN118197650 B CN 118197650B
- Authority
- CN
- China
- Prior art keywords
- monitoring
- operation flow
- data
- sequence
- flow sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 249
- 238000002324 minimally invasive surgery Methods 0.000 title claims abstract description 65
- 230000008447 perception Effects 0.000 claims abstract description 54
- 238000004458 analytical method Methods 0.000 claims abstract description 29
- 238000012552 review Methods 0.000 claims abstract description 17
- 238000000605 extraction Methods 0.000 claims abstract description 12
- 238000004891 communication Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 64
- 230000008569 process Effects 0.000 claims description 53
- 238000012512 characterization method Methods 0.000 claims description 23
- 238000012549 training Methods 0.000 claims description 18
- 238000001356 surgical procedure Methods 0.000 claims description 13
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000013135 deep learning Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 8
- 230000007246 mechanism Effects 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000012797 qualification Methods 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 5
- 230000002776 aggregation Effects 0.000 claims description 4
- 238000004220 aggregation Methods 0.000 claims description 4
- 230000003449 preventive effect Effects 0.000 abstract description 2
- 206010002091 Anaesthesia Diseases 0.000 description 12
- 230000037005 anaesthesia Effects 0.000 description 12
- 208000024891 symptom Diseases 0.000 description 5
- 230000036772 blood pressure Effects 0.000 description 3
- 238000002360 preparation method Methods 0.000 description 3
- 230000003444 anaesthetic effect Effects 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 230000004202 respiratory function Effects 0.000 description 2
- 230000036387 respiratory rate Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 208000032843 Hemorrhage Diseases 0.000 description 1
- 206010020772 Hypertension Diseases 0.000 description 1
- 208000001953 Hypotension Diseases 0.000 description 1
- 206010021143 Hypoxia Diseases 0.000 description 1
- 206010033372 Pain and discomfort Diseases 0.000 description 1
- 206010051077 Post procedural haemorrhage Diseases 0.000 description 1
- 210000001015 abdomen Anatomy 0.000 description 1
- 210000000683 abdominal cavity Anatomy 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000036592 analgesia Effects 0.000 description 1
- 230000000202 analgesic effect Effects 0.000 description 1
- 230000004872 arterial blood pressure Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 208000034158 bleeding Diseases 0.000 description 1
- 230000000740 bleeding effect Effects 0.000 description 1
- 230000036760 body temperature Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000035487 diastolic blood pressure Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000023597 hemostasis Effects 0.000 description 1
- 230000002439 hemostatic effect Effects 0.000 description 1
- 230000036543 hypotension Effects 0.000 description 1
- 230000007954 hypoxia Effects 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 238000002357 laparoscopic surgery Methods 0.000 description 1
- 238000002483 medication Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 230000008816 organ damage Effects 0.000 description 1
- 238000006213 oxygenation reaction Methods 0.000 description 1
- 230000036407 pain Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 230000002980 postoperative effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000000241 respiratory effect Effects 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000035488 systolic blood pressure Effects 0.000 description 1
- 210000004291 uterus Anatomy 0.000 description 1
- 210000001215 vagina Anatomy 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
- 230000029663 wound healing Effects 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
- G06N3/0455—Auto-encoder networks; Encoder-decoder networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- 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
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/20—ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Bioethics (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
An intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery relates to the technical field of intelligent monitoring, and comprises a monitoring center, wherein the monitoring center is in communication connection with a feature extraction module, a data acquisition module, a real-time monitoring module, a multi-layer perception analysis module and an intelligent early warning module; the feature extraction module is used for extracting the associated features and the non-associated features of each operation flow sequence; the data acquisition module is used for acquiring monitoring data; the real-time monitoring module is used for generating a safety alarm signal of the gynecological minimally invasive surgery; the multi-layer perception analysis module acquires a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period based on the multi-layer perception early warning model; the intelligent early warning module is used for generating a surgical operation flow sequence review signal, so that the early sign of the complication before the condition of the complication is mature is predicted, and preventive measures are taken before the condition of the complication is mature.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery.
Background
The prior art CN115565697A is a data analysis-based perioperative process monitoring and controlling system, which solves the technical problem that preoperative, intraoperative and postoperative processes cannot be analyzed in the prior art, monitors corresponding indexes of a historical anesthesia operation, performs risk assessment on the anesthesia operation through index monitoring, simultaneously analyzes according to historical influence indexes, improves monitoring pertinence of the current anesthesia operation, and improves safety and success of the current operation; the operation indexes of the current anesthesia operation are monitored in operation, the real-time influence of high risk index parameters in the anesthesia operation process is judged, the accuracy of the operation monitoring is improved, meanwhile, the safety of the anesthesia operation is improved, the operation success of the anesthesia operation is prevented from being reduced due to the abnormality of the anesthesia operation, and the follow-up treatment of a patient is affected;
The prior art CN117058854A 'a fault monitoring and early warning system based on the comprehensive operation power system' is used for solving the problems that the comprehensive operation power system is easy to break down and further the operation cannot be normally performed, and the operation is difficult or even wrong in the prior art because the comprehensive operation power system cannot be monitored in real time before and during the use process; the system can monitor the running state of the running comprehensive operation power system in real time, ensure the normal running state of the comprehensive operation power system, ensure the normal operation of the operation and improve the operation safety; the system can be used for screening the comprehensive operation power system for multiple times before the comprehensive operation power system is used, so that the comprehensive operation power system with excellent comprehensive conditions is obtained for operation, the probability of failure of the comprehensive operation power system is reduced, normal operation of the operation is further ensured, and the operation safety is improved;
In the existing intelligent monitoring technology for evaluating the safety of the gynecological minimally invasive surgery, a threshold monitoring technology is often adopted for each index in the surgical process, which is a static analysis method, the advantages of an online intelligent monitoring system cannot be reflected, in the existing intelligent monitoring technology for evaluating the safety of the gynecological minimally invasive surgery, closely related indexes exist for complications in the surgical process of a patient, the accuracy of the intelligent monitoring for evaluating the safety of the gynecological minimally invasive surgery is low, weak or difficult to identify symptoms exist in the operation process, if the operation process is not subjected to review or further detailed inspection, the occurrence of the complications in the operation process is likely to be caused, based on the existing threshold monitoring technology, early symptoms before the complications in the operation process are likely to be difficult to be detected, because the symptoms of the complications in the operation process are likely to be weak or difficult to identify, preventive measures cannot be taken before the conditions of the complications are mature, and how to solve the technical difficulties are problems are needed to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent monitoring system for evaluating the safety of gynecological minimally invasive surgery, which comprises a monitoring center, wherein the monitoring center is in communication connection with a feature extraction module, a data acquisition module, a real-time monitoring module, a multi-layer perception analysis module and an intelligent early warning module;
The feature extraction module is used for obtaining a standardized operation flow of the gynecological minimally invasive surgery and extracting associated features and non-associated features of each operation flow sequence in the standardized operation flow;
The data acquisition module is used for acquiring monitoring data of each operation flow sequence, marking acquisition time and setting an acquisition period;
the real-time monitoring module is used for judging whether each operation flow sequence is located in a corresponding qualified threshold interval or not, and generating a gynecological minimally invasive surgery safety alarm signal according to a judgment result;
The multi-layer perception analysis module builds a multi-layer perception early warning model based on deep learning, and obtains a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period based on the multi-layer perception early warning model;
And the intelligent early warning module analyzes the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period, and generates an operation flow sequence review signal according to the analysis result.
Further, the feature extraction module obtains a standardized operation flow of the gynecological minimally invasive surgery, and the process of extracting the associated features and the non-associated features of each operation flow sequence in the standardized operation flow comprises the following steps:
extracting an operation flow sequence according to a standardized operation flow of the gynecological minimally invasive surgery, and extracting associated features of the operation flow sequence;
Searching all monitoring indexes corresponding to the gynecological minimally invasive surgery and a plurality of historical medical records by utilizing a big data technology, marking all monitoring indexes corresponding to the gynecological minimally invasive surgery as judging and grinding indexes, collecting the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has no complication record in operation according to the plurality of historical medical records, collecting the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has produced the complication record in operation, and comparing the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has no complication record in operation with the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has produced the complication record in operation;
If the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has no complication record in the operation is consistent with the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has the complication record in the operation, acquiring a monitoring index corresponding to the judging and researching index with consistent numerical range of each operation flow sequence, and marking the monitoring index as an unassociated monitoring index;
if the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who finishes the gynecological minimally invasive surgery and has no complication record in the operation is inconsistent with the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who finishes the gynecological minimally invasive surgery and has the complication record in the operation, acquiring the monitoring index corresponding to the judging and researching index of which the numerical range of each operation flow sequence is inconsistent, and marking the monitoring index as the associated monitoring index.
Further, the data acquisition module acquires the monitoring data of each operation flow sequence and marks the acquisition time, and the process of setting the acquisition period comprises the following steps:
Setting a data monitoring point, wherein the data monitoring point acquires the monitoring data mark acquisition time corresponding to the unassociated monitoring index of each operation flow sequence according to the unassociated monitoring index of each operation flow sequence, and sets an acquisition period;
and the data monitoring point location acquires the monitoring data mark acquisition time corresponding to the associated monitoring index of each operation flow sequence according to the associated monitoring index of each operation flow sequence, and sets an acquisition period.
Further, the process of determining whether each operation flow sequence is located in the corresponding qualification threshold interval by the real-time monitoring module and generating the gynecological minimally invasive surgery safety alarm signal according to the determination result includes:
Presetting a corresponding qualification threshold interval of unassociated monitoring indexes of each operation flow sequence of the gynecological minimally invasive surgery and a corresponding qualification threshold interval of associated monitoring indexes of each operation flow sequence;
Acquiring monitoring data corresponding to unassociated monitoring indexes of a plurality of operation flow sequences and monitoring data corresponding to associated monitoring indexes of a plurality of operation flow sequences, which are acquired by data monitoring points in an acquisition period, and judging whether the monitoring data corresponding to unassociated monitoring indexes and the monitoring data corresponding to associated monitoring indexes are located in corresponding qualified threshold intervals or not;
If the monitoring data are located in the corresponding qualified threshold interval, trend characterization analysis is carried out on the monitoring data corresponding to the associated monitoring indexes of the operation flow sequences;
If the monitoring data which is not located in the corresponding qualified threshold interval exists, generating a gynecological minimally invasive surgery safety alarm signal and sending the safety alarm signal to a monitoring center.
Further, the process of constructing the multi-layer perception early warning model based on the deep learning by the multi-layer perception analysis module comprises the following steps:
Constructing a multi-layer perception early warning model based on deep learning, acquiring training data through a plurality of historical medical records, and training the multi-layer perception early warning model by utilizing the training data;
and inputting training data into the multi-layer perception early-warning model for training until the loss function training is stable, storing model parameters, testing the multi-layer perception early-warning model through a test set until the multi-layer perception early-warning model meets the preset requirement, and outputting the multi-layer perception early-warning model.
Further, the process of obtaining the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period by the multi-layer perception analysis module comprises the following steps:
Acquiring the monitoring data corresponding to the relevant monitoring indexes of the collected operation flow sequences in the collection period, respectively extracting the time characteristics and the space characteristics of the monitoring data corresponding to the relevant monitoring indexes of the operation flow sequences, and generating a time-space characteristic sequence of the monitoring data of the operation flow sequences;
And inputting the monitoring data of the operation flow sequences and the time-space characteristic sequences of the monitoring data of the operation flow sequences into a multi-layer perception early warning model, and generating a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period through the multi-layer perception early warning model.
Further, the process of extracting the temporal features and the spatial features of the monitoring data corresponding to the associated monitoring indexes of the operation flow sequences by the multi-layer perception analysis module respectively includes:
Acquiring an implementation sequence and an implementation relation among the operation flow sequences, taking the operation flow sequences as nodes of the topology directed graph, and taking the implementation sequence and the implementation relation among the operation flow sequences as a connection relation among the nodes to construct the topology directed graph;
Acquiring monitoring data corresponding to the associated monitoring indexes of a plurality of operation flow sequences acquired in an acquisition period, splicing the monitoring data corresponding to the associated monitoring indexes at each moment acquired in a monitoring time period of the plurality of operation flow sequences according to a time sequence relation, generating a two-dimensional feature matrix, constructing a time convolution neural network to learn the two-dimensional feature matrix of the plurality of operation flow sequences, and acquiring monitoring data change features according to the time convolution neural network after learning;
The method comprises the steps of constructing a graph attention network to learn a topological directed graph, inputting the change characteristics of monitoring data of a plurality of operation flow sequences into the graph attention network, acquiring the attention weight of adjacent nodes of all nodes in the topological directed graph to the self by an attention mechanism, and generating the time-space characteristics of the monitoring data of all nodes according to the attention weight and the time characteristics of the monitoring data of all nodes by utilizing a neighbor aggregation mechanism of the graph attention network.
Further, the process of analyzing the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period by the intelligent early warning module and generating the operation flow sequence review signal according to the analysis result comprises the following steps:
Acquiring a qualified threshold interval corresponding to an associated monitoring index of each operation flow sequence, acquiring a numerical fluctuation coefficient of a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence and a frequency of which the numerical value of the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence is not located in the corresponding qualified threshold interval, and acquiring an early warning characterization coefficient of the predicted data time sequence according to the numerical fluctuation coefficient and the frequency;
Presetting an early warning characterization coefficient threshold, and generating a surgery normal signal and sending the surgery normal signal to a monitoring center when the early warning characterization coefficient of a predicted data time sequence corresponding to an associated monitoring index of an operation flow sequence is smaller than the early warning characterization coefficient threshold;
When the early warning characterization coefficient of the predicted data time sequence corresponding to the associated monitoring index of the operation flow sequence is larger than or equal to the threshold value of the early warning characterization coefficient, generating a surgical operation flow sequence review signal and sending the surgical operation flow sequence review signal to a monitoring center.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of extracting relevant characteristics and non-relevant characteristics of each operation flow sequence in a standardized operation flow through a characteristic extraction module, judging whether each operation flow sequence is located in a corresponding qualified threshold interval through a real-time monitoring module, generating a gynecological minimally invasive surgery safety alarm signal according to a judging result, carrying out online threshold monitoring, then establishing a multi-layer perception early warning model through a multi-layer perception analysis module on the basis of the online threshold monitoring, acquiring a predicted data time sequence corresponding to relevant monitoring indexes of each operation flow sequence in a residual period of a collection period based on the multi-layer perception early warning model, analyzing the predicted data time sequence corresponding to the relevant monitoring indexes of each operation flow sequence in the residual period of the collection period, generating a surgery operation flow sequence review signal according to an analysis result, and realizing the prediction of early symptoms before the condition of the occurrence of complications, so that precautions are taken before the condition of the occurrence of the complications.
Drawings
Fig. 1 is a schematic diagram of an intelligent monitoring system for assessing safety of gynecological minimally invasive surgery according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, the intelligent monitoring system for evaluating the safety of the gynecological minimally invasive surgery comprises a monitoring center, wherein the monitoring center is in communication connection with a feature extraction module, a data acquisition module, a real-time monitoring module, a multi-layer perception analysis module and an intelligent early warning module;
The feature extraction module is used for obtaining a standardized operation flow of the gynecological minimally invasive surgery and extracting associated features and non-associated features of each operation flow sequence in the standardized operation flow;
The data acquisition module is used for acquiring monitoring data of each operation flow sequence, marking acquisition time and setting an acquisition period;
the real-time monitoring module is used for judging whether each operation flow sequence is located in a corresponding qualified threshold interval or not, and generating a gynecological minimally invasive surgery safety alarm signal according to a judgment result;
The multi-layer perception analysis module builds a multi-layer perception early warning model based on deep learning, and obtains a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period based on the multi-layer perception early warning model;
And the intelligent early warning module analyzes the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period, and generates an operation flow sequence review signal according to the analysis result.
It should be further noted that, in the specific implementation process, the feature extraction module obtains a standardized operation flow of the gynecological minimally invasive surgery, and the process of extracting the associated features and the non-associated features of each operation flow sequence in the standardized operation flow includes:
extracting an operation flow sequence according to a standardized operation flow of the gynecological minimally invasive surgery, and extracting associated features of the operation flow sequence;
Searching all monitoring indexes corresponding to the gynecological minimally invasive surgery and a plurality of historical medical records by utilizing a big data technology, marking all monitoring indexes corresponding to the gynecological minimally invasive surgery as judging and grinding indexes, collecting the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has no complication record in operation according to the plurality of historical medical records, collecting the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has produced the complication record in operation, and comparing the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has no complication record in operation with the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has produced the complication record in operation;
If the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has no complication record in the operation is consistent with the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has the complication record in the operation, acquiring a monitoring index corresponding to the judging and researching index with consistent numerical range of each operation flow sequence, and marking the monitoring index as an unassociated monitoring index;
if the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who finishes the gynecological minimally invasive surgery and has no complication record in the operation is inconsistent with the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who finishes the gynecological minimally invasive surgery and has the complication record in the operation, acquiring the monitoring index corresponding to the judging and researching index of which the numerical range of each operation flow sequence is inconsistent, and marking the monitoring index as the associated monitoring index.
The operation flow sequence of the gynecological minimally invasive surgery comprises the following steps:
The preparation working steps are as follows: doctors and surgical teams will be ready for necessary instruments, equipment and medications, and the patient is brought into the operating room and properly positioned on the operating table;
Anesthesia and analgesia steps: the anesthesiologist can anesthetize the patient, ensure that the patient has no pain in the operation process, and analgesic measures can be carried out before the operation so as to reduce the pain and discomfort after the operation;
Surgical site preparation step: a doctor can disinfect and spread towel on the operation site, so that the operation site is clean and sterile, and special operation site preparation, such as vaginal irrigation, can be needed for special situations;
small incision or endoscope access procedure: for laparoscopic surgery, a doctor performs a small incision in the abdomen of a patient and inserts a laparoscope for observing and manipulating organs of the inner abdominal cavity, and for hysteroscopic surgery, the doctor inserts a hysteroscope into the uterus through the vagina for performing endoscopic examination and manipulation steps;
surgical procedure: a doctor performs operation through a laparoscope or a hysteroscope, and performs related tissue excision steps, repair steps or reconstruction steps according to the illness state of a patient and operation requirements, and various minimally invasive surgical instruments such as forceps, scissors, a suction device and the like can be used by the doctor in the operation process;
Hemostatic and suturing steps: in the operation process, a doctor can perform necessary hemostasis treatment to reduce the risk of postoperative bleeding, and after the operation is finished, the doctor can suture or attach the operation part to ensure good wound healing;
Post-operation treatment steps: after the operation is finished, the patient is sent to a recovery room for observation and monitoring until the anesthesia effect completely subsides and the vital sign data is stable; the doctor can properly bind and care the operation part;
The difficulty and quality of completion of the next step will be directly affected by the operation process of each step in the operation flow sequence, and if there are weak or indistinguishable symptoms in the operation process, complications in the operation may occur if the operation process is not reviewed or further examined in detail.
Monitoring metrics include, but are not limited to:
vital sign data: the method comprises the following steps of including basic physiological indexes such as heart rate, respiratory rate and body temperature of a patient, wherein the indexes reflect the overall physiological state of the patient and have important significance for evaluating the safety of surgery;
blood pressure data: the blood pressure parameters such as systolic pressure, diastolic pressure, mean arterial pressure and the like are included, and abnormal conditions in the aspect of a circulatory system such as hypotension, hypertension and the like can be timely found by monitoring the blood pressure;
breathing parameter data: including respiratory related indicators of patient respiratory rate, tidal volume, depth of breath, etc., which can be used to assess patient respiratory function and ventilation;
anesthesia monitoring data: including anesthetic use, anesthetic depth monitoring indicators, etc., which are critical to assessing patient anesthesia effectiveness and safety during surgery;
Blood oxygen saturation data: by monitoring the blood oxygen saturation of the patient, the problems of poor oxygenation conditions or hypoxia can be found in time, and the normal respiratory function of the patient is ensured;
surgical operation data: including surgical instrument usage, procedure steps and time during surgery, etc., which can be used to assess the normative and safety of the surgical procedure.
Complications monitoring data: the complications possibly occurring in the operation process, such as bleeding, instrument damage, organ damage and the like, are monitored, and are discovered and treated in time, so that the method is important for ensuring the operation safety.
It should be further noted that, in the specific implementation process, the data acquisition module acquires the monitoring data of each operation flow sequence and marks the acquisition time, and the process of setting the acquisition period includes:
Setting a data monitoring point, wherein the data monitoring point acquires the monitoring data mark acquisition time corresponding to the unassociated monitoring index of each operation flow sequence according to the unassociated monitoring index of each operation flow sequence, and sets an acquisition period;
and the data monitoring point location acquires the monitoring data mark acquisition time corresponding to the associated monitoring index of each operation flow sequence according to the associated monitoring index of each operation flow sequence, and sets an acquisition period.
It should be further noted that, in the specific implementation process, the process of determining, by the real-time monitoring module, whether each operation flow sequence is located in the corresponding qualified threshold interval and generating the gynecological minimally invasive surgery safety alarm signal according to the determination result includes:
Presetting a corresponding qualification threshold interval of unassociated monitoring indexes of each operation flow sequence of the gynecological minimally invasive surgery and a corresponding qualification threshold interval of associated monitoring indexes of each operation flow sequence;
Acquiring monitoring data corresponding to unassociated monitoring indexes of a plurality of operation flow sequences and monitoring data corresponding to associated monitoring indexes of a plurality of operation flow sequences, which are acquired by data monitoring points in an acquisition period, and judging whether the monitoring data corresponding to unassociated monitoring indexes and the monitoring data corresponding to associated monitoring indexes are located in corresponding qualified threshold intervals or not;
If the monitoring data are located in the corresponding qualified threshold interval, trend characterization analysis is carried out on the monitoring data corresponding to the associated monitoring indexes of the operation flow sequences;
If the monitoring data which is not located in the corresponding qualified threshold interval exists, generating a gynecological minimally invasive surgery safety alarm signal and sending the safety alarm signal to a monitoring center.
It should be further noted that, in the implementation process, the process of constructing the multi-layer perception early-warning model by the multi-layer perception analysis module based on deep learning includes:
Constructing a multi-layer perception early warning model based on deep learning, acquiring training data through a plurality of historical medical records, and training the multi-layer perception early warning model by utilizing the training data;
and inputting training data into the multi-layer perception early-warning model for training until the loss function training is stable, storing model parameters, testing the multi-layer perception early-warning model through a test set until the multi-layer perception early-warning model meets the preset requirement, and outputting the multi-layer perception early-warning model.
It should be further noted that, in the implementation process, the process of obtaining the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the remaining time period of the acquisition period by the multi-layer perception analysis module includes:
Acquiring the monitoring data corresponding to the relevant monitoring indexes of the collected operation flow sequences in the collection period, respectively extracting the time characteristics and the space characteristics of the monitoring data corresponding to the relevant monitoring indexes of the operation flow sequences, and generating a time-space characteristic sequence of the monitoring data of the operation flow sequences;
And inputting the monitoring data of the operation flow sequences and the time-space characteristic sequences of the monitoring data of the operation flow sequences into a multi-layer perception early warning model, and generating a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period through the multi-layer perception early warning model.
It should be further noted that, in the specific implementation process, the process of extracting the temporal features and the spatial features of the monitoring data corresponding to the associated monitoring indexes of the plurality of operation flow sequences by the multi-layer perception analysis module includes:
Acquiring an implementation sequence and an implementation relation among the operation flow sequences, taking the operation flow sequences as nodes of the topology directed graph, and taking the implementation sequence and the implementation relation among the operation flow sequences as a connection relation among the nodes to construct the topology directed graph;
Acquiring monitoring data corresponding to the associated monitoring indexes of a plurality of operation flow sequences acquired in an acquisition period, splicing the monitoring data corresponding to the associated monitoring indexes at each moment acquired in a monitoring time period of the plurality of operation flow sequences according to a time sequence relation, generating a two-dimensional feature matrix, constructing a time convolution neural network to learn the two-dimensional feature matrix of the plurality of operation flow sequences, and acquiring monitoring data change features according to the time convolution neural network after learning;
The method comprises the steps of constructing a graph attention network to learn a topological directed graph, inputting the change characteristics of monitoring data of a plurality of operation flow sequences into the graph attention network, acquiring the attention weight of adjacent nodes of all nodes in the topological directed graph to the self by an attention mechanism, and generating the time-space characteristics of the monitoring data of all nodes according to the attention weight and the time characteristics of the monitoring data of all nodes by utilizing a neighbor aggregation mechanism of the graph attention network.
It should be further described that, in the specific implementation process, the formula for generating the two-dimensional feature matrix is that, according to the time sequence relationship, the monitoring data corresponding to the associated monitoring indexes at each moment collected in the monitoring time periods of the operation flow sequences are spliced, where:
＝concact(Q1j,Q2j,...Qij,...,QNj)，i＝1,2,...,N；
j＝1,2,...,K；
Wherein Qiaj represents a characteristic data matrix corresponding to the monitoring data corresponding to the associated monitoring index of the jth operation flow sequence at the ith moment, N represents the total number of monitoring moments of the jth operation flow sequence, R represents the number of associated monitoring index types, K represents the number of total operation flow sequences, concact represents the splicing of the characteristic data matrix at each moment according to a time sequence relationship, A two-dimensional data matrix for the constructed j-th operation flow sequence;
the method comprises the steps of constructing a time convolution neural network to learn two-dimensional feature matrixes of a plurality of operation flow sequences, and acquiring a formula for monitoring data change features according to the time convolution neural network after learning is completed, wherein the formula comprises the following steps:
＝TCN（）；
Wherein, Representing the monitored data change characteristics of the j-th operational flow sequence, TCN () represents a function of the time convolutional neural network.
It should be further described that, in the implementation process, the attention weight of the neighboring node pair of each node in the topological directed graph is obtained through the attention mechanism, and the specific process of generating the time-space feature sequence of the monitoring data of each node according to the attention weight and the time feature of the monitoring data of each node by using the neighbor aggregation mechanism of the graph attention network is as follows:
；
；
；
...
；
Wherein, Representation ofThe corresponding adjacency matrix, ELU, tanh represent the activation function, FULLY represent the fully connected layers of the graph attention network,AndRespectively representing the corresponding weight matrix and bias after the (x+1) th iteration,Representing the spatiotemporal feature sequence of the jth operational flow sequence after the x+1th iteration.
It should be further noted that, in the specific implementation process, the process of analyzing the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the collection period by the intelligent early warning module and generating the operation flow sequence review signal according to the analysis result includes:
Acquiring a qualified threshold interval corresponding to an associated monitoring index of each operation flow sequence, acquiring a numerical fluctuation coefficient of a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence and a frequency of which the numerical value of the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence is not located in the corresponding qualified threshold interval, and acquiring an early warning characterization coefficient of the predicted data time sequence according to the numerical fluctuation coefficient and the frequency;
Presetting an early warning characterization coefficient threshold, and generating a surgery normal signal and sending the surgery normal signal to a monitoring center when the early warning characterization coefficient of a predicted data time sequence corresponding to an associated monitoring index of an operation flow sequence is smaller than the early warning characterization coefficient threshold;
When the early warning characterization coefficient of the predicted data time sequence corresponding to the associated monitoring index of the operation flow sequence is larger than or equal to the threshold value of the early warning characterization coefficient, generating a surgical operation flow sequence review signal and sending the surgical operation flow sequence review signal to a monitoring center.
It should be further described that, in the specific implementation process, the operation flow sequence review signal indicates that each item of monitoring data of the current operation flow sequence is qualified, but a doctor who needs to perform an operation or other doctors with abundant experience on site need to review the current process of completing the operation flow sequence of the patient, through repeated inspection, the doctor can timely find potential problems or errors and timely correct the problems or errors, so as to ensure the safety of the operation process, which is crucial for avoiding occurrence of unexpected events or complications in the operation, is helpful for ensuring that the patient receives high-quality medical treatment, reduces operation risks to the greatest extent, and protects the life and health of the patient.
It should be further noted that, in the implementation process, a calculation formula of the numerical fluctuation coefficient of the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence is:
；
Wherein, Numerical fluctuation coefficients of the predicted data time sequence corresponding to the associated monitoring index of the jth operation flow sequence are represented; A value corresponding to the predicted data at the t-th time corresponding to the associated monitoring index of the j-th operation flow sequence; Representing an average value corresponding to the predicted data corresponding to the associated monitoring index of the jth operational flow sequence; n represents the total number of monitoring moments of the j-th operation flow sequence;
the calculation formula for obtaining the early warning characterization coefficient of the predicted data time sequence according to the numerical fluctuation coefficient and the frequency is as follows:
；
Wherein, The early warning characterization coefficient of the predicted data corresponding to the associated monitoring index of the jth operational flow sequence is represented,The frequency at which the value of the predicted data timing sequence corresponding to the associated monitor indicator representing the jth operational flow sequence is not located in the corresponding pass threshold interval,Representing the conversion factor.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (1)
1. The intelligent monitoring system for evaluating the safety of the gynecological minimally invasive surgery comprises a monitoring center, and is characterized in that the monitoring center is in communication connection with a feature extraction module, a data acquisition module, a real-time monitoring module, a multi-layer perception analysis module and an intelligent early warning module;
The feature extraction module is used for obtaining a standardized operation flow of the gynecological minimally invasive surgery and extracting associated features and non-associated features of each operation flow sequence in the standardized operation flow;
the feature extraction module acquires a standardized operation flow of the gynecological minimally invasive surgery, and the process for extracting the associated features and the non-associated features of each operation flow sequence in the standardized operation flow comprises the following steps:
extracting an operation flow sequence according to a standardized operation flow of the gynecological minimally invasive surgery, and extracting associated features of the operation flow sequence;
Searching all monitoring indexes corresponding to the gynecological minimally invasive surgery and a plurality of historical medical records by utilizing a big data technology, marking all monitoring indexes corresponding to the gynecological minimally invasive surgery as judging and grinding indexes, collecting the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has no complication record in operation according to the plurality of historical medical records, collecting the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has produced the complication record in operation, and comparing the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has no complication record in operation with the numerical ranges of judging and grinding indexes of each operation flow sequence corresponding to a patient who has historically completed the gynecological minimally invasive surgery and has produced the complication record in operation;
If the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has no complication record in the operation is consistent with the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has the complication record in the operation, acquiring a monitoring index corresponding to the judging and researching index with consistent numerical range of each operation flow sequence, and marking the monitoring index as an unassociated monitoring index;
If the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has no complication record in the operation is inconsistent with the numerical range of the judging and researching index of each operation flow sequence corresponding to the patient who completes the gynecological minimally invasive surgery and has the complication record in the operation, acquiring a monitoring index corresponding to the judging and researching index inconsistent with the numerical range of each operation flow sequence, and marking the monitoring index as an associated monitoring index;
The data acquisition module is used for acquiring monitoring data of each operation flow sequence, marking acquisition time and setting an acquisition period;
The data acquisition module acquires monitoring data of each operation flow sequence and marks acquisition time, and the process of setting the acquisition period comprises the following steps:
Setting a data monitoring point, wherein the data monitoring point acquires the monitoring data mark acquisition time corresponding to the unassociated monitoring index of each operation flow sequence according to the unassociated monitoring index of each operation flow sequence, and sets an acquisition period;
the data monitoring point location acquires the monitoring data mark acquisition time corresponding to the associated monitoring index of each operation flow sequence according to the associated monitoring index of each operation flow sequence, and sets an acquisition period;
the real-time monitoring module is used for judging whether each operation flow sequence is located in a corresponding qualified threshold interval or not, and generating a gynecological minimally invasive surgery safety alarm signal according to a judgment result;
the process of judging whether each operation flow sequence is located in the corresponding qualified threshold interval by the real-time monitoring module and generating the gynecological minimally invasive surgery safety alarm signal according to the judgment result comprises the following steps:
Presetting a corresponding qualification threshold interval of unassociated monitoring indexes of each operation flow sequence of the gynecological minimally invasive surgery and a corresponding qualification threshold interval of associated monitoring indexes of each operation flow sequence;
Acquiring monitoring data corresponding to unassociated monitoring indexes of a plurality of operation flow sequences and monitoring data corresponding to associated monitoring indexes of a plurality of operation flow sequences, which are acquired by data monitoring points in an acquisition period, and judging whether the monitoring data corresponding to unassociated monitoring indexes and the monitoring data corresponding to associated monitoring indexes are located in corresponding qualified threshold intervals or not;
If the monitoring data are located in the corresponding qualified threshold interval, trend characterization analysis is carried out on the monitoring data corresponding to the associated monitoring indexes of the operation flow sequences;
if monitoring data which is not located in the corresponding qualified threshold interval exists, generating a gynecological minimally invasive surgery safety alarm signal and sending the safety alarm signal to a monitoring center;
The multi-layer perception analysis module builds a multi-layer perception early warning model based on deep learning, and obtains a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period based on the multi-layer perception early warning model;
The process for constructing the multi-layer perception early warning model based on the deep learning by the multi-layer perception analysis module comprises the following steps:
Constructing a multi-layer perception early warning model based on deep learning, acquiring training data through a plurality of historical medical records, and training the multi-layer perception early warning model by utilizing the training data;
Inputting training data into the multi-layer perception early-warning model for training until the loss function training is stable, storing model parameters, testing the multi-layer perception early-warning model through a test set until the multi-layer perception early-warning model meets preset requirements, and outputting the multi-layer perception early-warning model;
The process of obtaining the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period by the multi-layer perception analysis module comprises the following steps:
Acquiring the monitoring data corresponding to the relevant monitoring indexes of the collected operation flow sequences in the collection period, respectively extracting the time characteristics and the space characteristics of the monitoring data corresponding to the relevant monitoring indexes of the operation flow sequences, and generating a time-space characteristic sequence of the monitoring data of the operation flow sequences;
inputting the monitoring data of a plurality of operation flow sequences and the time-space characteristic sequences of the monitoring data of the operation flow sequences into a multi-layer perception early warning model, and generating a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period through the multi-layer perception early warning model;
The process of extracting the time features and the space features of the monitoring data corresponding to the associated monitoring indexes of the operation flow sequences by the multi-layer perception analysis module comprises the following steps:
Acquiring an implementation sequence and an implementation relation among the operation flow sequences, taking the operation flow sequences as nodes of the topology directed graph, and taking the implementation sequence and the implementation relation among the operation flow sequences as a connection relation among the nodes to construct the topology directed graph;
Acquiring monitoring data corresponding to the associated monitoring indexes of a plurality of operation flow sequences acquired in an acquisition period, splicing the monitoring data corresponding to the associated monitoring indexes at each moment acquired in a monitoring time period of the plurality of operation flow sequences according to a time sequence relation, generating a two-dimensional feature matrix, constructing a time convolution neural network to learn the two-dimensional feature matrix of the plurality of operation flow sequences, and acquiring monitoring data change features according to the time convolution neural network after learning;
Constructing a graph attention network to learn a topological directed graph, inputting the change characteristics of the monitoring data of a plurality of operation flow sequences into the graph attention network, acquiring the attention weight of the adjacent node of each node in the topological directed graph to the self through an attention mechanism, and generating the time-space characteristics of the monitoring data of each node according to the attention weight and the time characteristics of the monitoring data of each node by utilizing a neighbor aggregation mechanism of the graph attention network;
the intelligent early warning module analyzes the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period, and generates an operation flow sequence review signal according to the analysis result;
The intelligent early warning module analyzes the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence in the residual time period of the acquisition period, and the process of generating the operation flow sequence review signal according to the analysis result comprises the following steps:
Acquiring a qualified threshold interval corresponding to an associated monitoring index of each operation flow sequence, acquiring a numerical fluctuation coefficient of a predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence and a frequency of which the numerical value of the predicted data time sequence corresponding to the associated monitoring index of each operation flow sequence is not located in the corresponding qualified threshold interval, and acquiring an early warning characterization coefficient of the predicted data time sequence according to the numerical fluctuation coefficient and the frequency;
Presetting an early warning characterization coefficient threshold, and generating a surgery normal signal and sending the surgery normal signal to a monitoring center when the early warning characterization coefficient of a predicted data time sequence corresponding to an associated monitoring index of an operation flow sequence is smaller than the early warning characterization coefficient threshold;
When the early warning characterization coefficient of the predicted data time sequence corresponding to the associated monitoring index of the operation flow sequence is larger than or equal to the threshold value of the early warning characterization coefficient, generating a surgical operation flow sequence review signal and sending the surgical operation flow sequence review signal to a monitoring center.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410612981.9A CN118197650B (en) | 2024-05-17 | 2024-05-17 | Intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410612981.9A CN118197650B (en) | 2024-05-17 | 2024-05-17 | Intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118197650A CN118197650A (en) | 2024-06-14 |
| CN118197650B true CN118197650B (en) | 2024-07-30 |
Family
ID=91401928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410612981.9A Active CN118197650B (en) | 2024-05-17 | 2024-05-17 | Intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118197650B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115565697A (en) * | 2022-10-25 | 2023-01-03 | 深圳雅尔典环境技术科技有限公司 | Perioperative period process monitoring and control system based on data analysis |
| CN116864060A (en) * | 2023-07-14 | 2023-10-10 | 四川大学 | Early cancer surgery perioperative data management system and method |
| CN117768235A (en) * | 2023-12-29 | 2024-03-26 | 广东云百智联科技有限公司 | Real-time flow monitoring alarm system based on Internet of things |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA3180513A1 (en) * | 2020-04-17 | 2021-10-21 | Exactech, Inc. | Method and system for modeling predictive outcomes of arthroplasty surgical procedures |
| US11710559B2 (en) * | 2021-08-21 | 2023-07-25 | Ix Innovation Llc | Adaptive patient condition surgical warning system |
| KR102382707B1 (en) * | 2021-11-02 | 2022-04-08 | 주식회사 바스젠바이오 | disease onset information generating apparatus based on time-dependent correlation using polygenic risk score and method therefor |
| CN116681187B (en) * | 2023-08-03 | 2023-10-20 | 北京滴普科技有限公司 | Enterprise carbon quota prediction method based on enterprise operation data |
| CN117133471A (en) * | 2023-08-18 | 2023-11-28 | 中国医学科学院阜外医院 | Blood transfusion prediction method, system, equipment and medium for acute aortic dissection diseases |
| CN116844733B (en) * | 2023-08-31 | 2023-11-07 | 吉林大学第一医院 | Medical data integrity analysis method based on artificial intelligence |
| CN117116497B (en) * | 2023-10-16 | 2024-01-12 | 长春中医药大学 | Clinical care management system for gynecological diseases |
| CN117133448B (en) * | 2023-10-23 | 2024-01-23 | 长春中医药大学 | Gynecological disease consultation system based on big data |
| CN117316457A (en) * | 2023-10-23 | 2023-12-29 | 上海交通大学宁波人工智能研究院 | Intelligent management method and system for physiological index prediction in dialysis |
| CN117560300B (en) * | 2023-12-28 | 2024-04-30 | 广东云百智联科技有限公司 | Intelligent internet of things flow prediction and optimization system |
-
2024
- 2024-05-17 CN CN202410612981.9A patent/CN118197650B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115565697A (en) * | 2022-10-25 | 2023-01-03 | 深圳雅尔典环境技术科技有限公司 | Perioperative period process monitoring and control system based on data analysis |
| CN116864060A (en) * | 2023-07-14 | 2023-10-10 | 四川大学 | Early cancer surgery perioperative data management system and method |
| CN117768235A (en) * | 2023-12-29 | 2024-03-26 | 广东云百智联科技有限公司 | Real-time flow monitoring alarm system based on Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118197650A (en) | 2024-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5724983A (en) | Continuous monitoring using a predictive instrument | |
| Aziz et al. | Pervasive body sensor network: an approach to monitoring the post-operative surgical patient | |
| US20230338099A1 (en) | Robotic surgery system with tool edge sensors for edge computing | |
| US11776697B2 (en) | System for administering a qualitative assessment using an automated verbal interface | |
| US11478314B1 (en) | System and method for implanting smart implants using robotic telesurgery | |
| Ploss et al. | Pilot use of a novel tool to assess neurosurgical capacity in Uganda | |
| CN118197650B (en) | Intelligent monitoring system for evaluating safety of gynecological minimally invasive surgery | |
| CN116965774A (en) | Intraoperative anesthesia state monitoring equipment | |
| Orphanidou et al. | Telemetry-based vital sign monitoring for ambulatory hospital patients | |
| US20230368904A1 (en) | Adaptive patient condition surgical warning system | |
| US11937879B2 (en) | Apparatus, system, and method for implanting surgical material | |
| CN116825340A (en) | Health management method and system suitable for postoperative patient | |
| Garfinkel et al. | Patient monitoring in the operating room: validation of instrument readings by artificial intelligence methods | |
| US20230215560A1 (en) | System for a triage virtual assistant | |
| Donohue et al. | An evaluation of voiding cystometry as a diagnostic test for bladder outlet obstruction | |
| Young et al. | One elevated bladder pressure measurement may not be enough to diagnose abdominal compartment syndrome | |
| Childers et al. | 279 Overdiagnosis of urinary tract infections in the emergency department | |
| RU2396899C2 (en) | Mokhov-chaschin's method of obtaining data about cranial tissue state and device for its realisation | |
| CN118248342B (en) | Method and related equipment for predicting forearm swelling after radial artery puncture | |
| CN116230189A (en) | Comfortable nursing management device and system in paediatrics art | |
| US11894126B1 (en) | Systems and methods for tracking movement of a wearable device for advanced image stabilization | |
| RU2820734C1 (en) | Method for assessing severity of early postpartum haemorrhage | |
| US20240290449A1 (en) | Report generation for radiology | |
| CN106955402A (en) | One kind anesthesia monitoring method | |
| Cetrulo et al. | Medicolegal dystocia |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |