CN111768818A - Artificial intelligence-based pelvis fracture first-aid decision-making system and method - Google Patents
Artificial intelligence-based pelvis fracture first-aid decision-making system and method Download PDFInfo
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Abstract
The invention relates to an artificial intelligence-based pelvis fracture emergency aid assistant decision-making system and a method, which comprises the following steps: the data layer comprises an intelligent assistant decision-making knowledge base for first aid of pelvic fracture, the intelligent assistant decision-making knowledge base comprises historical case data and pelvic fracture diagnosis and treatment literature data, the data layer imports basic information of a patient with pelvic fracture emergency treatment, creates data information of the patient with pelvic fracture emergency treatment, collects vital signs, tests and examination results and clinical treatment measures so as to track disease changes of the patient in real time; the processing layer intelligently learns related knowledge and cases of the pelvis fracture emergency treatment, extracts related literature data to expand an intelligent assistant decision-making knowledge base, performs image recognition on the imaging of a patient with the pelvis fracture emergency treatment, and performs semantic analysis and matching on the data analyzed by the patient with the pelvis fracture emergency treatment and the intelligent assistant decision-making knowledge base to extract decision-making suggestions; the application layer displays relevant illness state data of the pelvic fracture emergency treatment patient, emergency treatment intelligent aid decision suggestions and actual treatment measures, and the summarized data are summarized in the data layer.
Description
Technical Field
The invention relates to the technical field of wound emergency aid assistance, in particular to an artificial intelligence-based pelvis fracture emergency aid decision-making system and method.
Background
The incidence of pelvic fractures accounts for 0.3% to 8% of all fractures, but pelvic Fracture mortality approaches 16%, with 27% for closed pelvic Fracture patients who are hemodynamically unstable, and more so over 55% for open pelvic Fracture patients, entitled terrorist by The Kiling Fracture. Although the current trauma first aid procedures and measures are increasingly sophisticated, the mortality rate of pelvic fractures remains the highest of all skeletal trauma, and bleeding is the major reversible factor leading to death in about 42% of patients with pelvic fractures. The bleeding sources mainly come from four aspects, namely bleeding at the fractured ends of the pelvis, bleeding caused by organ injury of the thoracic cavity and the abdominal cavity, pelvic venous bleeding and arterial injury blood loss. The most important aspect in the emergency treatment of pelvic fractures is the early diagnosis of bleeding. The diagnosis of pelvic fracture is not clear in type, delayed in diagnosis of combined injury and even missed diagnosis due to the reasons of unclear chief complaints, difficult physical examination, carrying risk during imaging examination, insufficient understanding of injury mechanism by doctors and the like of some patients. Different fracture types are prone to different vascular injuries, for example dislocation behind the pelvic ring (open book-like fracture) often causes damage to the internal iliac artery and its branches; butterfly fractures are prone to compromise the inferior pudendal vessels, and anterior pressure may damage the external iliac or femoral arteries. For bleeding caused by vein injury at fracture part, effective intervention can be performed by reducing pelvis volume and stabilizing fracture through external fixation and pelvis stuffing. But the tamponade is not sufficient to block arteriogenic bleeding. Arterial injury is often the most fatal, usually a small blood vessel at a deeper site rather than the well-known arterial injury. Angiographic embolization is an effective means of controlling arterial bleeding, but the treatment relies on a specialized vascular team and requires time-consuming preparation. The world emergency surgery society recommends pelvic tamponade as a first-line rescue tool, but many guidelines suggest objections to this, propose different first aid strategies for hemodynamically unstable pelvic fractures, and are new every year. Although the strategy is not uniform, the timing is considered critical for successful intervention and increased survival. However, emergency physicians often rely on their own experience to determine their condition, how to fully evaluate the condition of pelvic fracture patients, grasp the timing to determine the bleeding source as soon as possible, and scientifically treat the pelvic fracture patients, and adopt correct treatment strategies at appropriate timing to improve the survival rate of the patients according to evidence-based medicine, which is a great challenge to be solved urgently by trauma emergency physicians.
Therefore, there is a need to invent an artificial intelligence-based pelvis fracture emergency aid assistant decision platform to assist physicians in making scientific prognosis, accurate treatment and prognosis improvement quickly, and to solve the above technical problems in emergency treatment of pelvis fracture patients.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides an artificial intelligence-based pelvis fracture emergency aid assistant decision-making system and method.
The invention solves the technical problems through the following technical scheme:
the invention provides an artificial intelligence-based pelvis fracture first-aid assistant decision-making system which is characterized by comprising a data layer, a processing layer and an application layer;
the data layer comprises a pelvis fracture emergency treatment intelligent aid decision-making knowledge base, the intelligent aid decision-making knowledge base comprises historical case data and intelligently retrieved pelvis fracture diagnosis and treatment literature data, and the data layer is used for importing basic information of a pelvis fracture emergency treatment patient, creating data information of the pelvis fracture emergency treatment patient, collecting vital signs, testing and checking results and carrying out clinical treatment measures to track disease state changes of the patient in real time;
the processing layer is used for intelligently learning related knowledge and cases of the pelvis fracture emergency treatment, extracting related literature data to expand an intelligent aid decision-making knowledge base, carrying out image recognition on the imaging of a patient with the pelvis fracture emergency treatment, carrying out semantic analysis and matching on the data analyzed by the patient with the pelvis fracture emergency treatment and the intelligent aid decision-making knowledge base, and extracting decision-making suggestions to be displayed on the application layer;
the application layer is used for displaying relevant illness state data, emergency treatment intelligent aid decision suggestions and actual treatment measures of the pelvic fracture emergency treatment patient, and the summarized data are summarized to the data layer.
Preferably, the data layer is used for introducing basic information of a pelvic fracture emergency patient of the post-traumatic hip pain, and collecting chief complaints, current medical history, past medical history, personal history, dynamically-changed vital signs, inspection and examination results and clinical treatment measures of the pelvic fracture emergency patient.
Preferably, the processing layer is used for performing accurate Tile typing, Young-Burgess typing and AO typing on the pelvic fracture through performing image recognition on the imaging of the pelvic fracture emergency patient based on the acquired information of the pelvic fracture emergency patient, and performing trauma scoring, trauma severity scoring, concise damage scoring, improved Glasgow coma scoring and shock index scoring through analyzing the current disease data of the pelvic fracture emergency patient;
based on the basic information, the fracture type, the trauma score, the trauma severity score, the concise damage score, the improved Glasgow coma score and the shock index score of the pelvis fracture emergency patient, semantic analysis and matching are carried out on the pelvis fracture emergency patient and the diagnosis and treatment intelligent auxiliary decision knowledge base, so that the first-aid intelligent auxiliary decision suggestion for the pelvis fracture emergency patient is obtained.
Preferably, the processing layer is configured to perform the wound severity scoring using a wound severity scoring algorithm, perform the concise damage scoring using a concise damage scoring algorithm, perform the modified Glasgow coma scoring using a modified Glasgow coma scoring algorithm, and perform the shock index scoring using a shock index algorithm.
The invention also provides an artificial intelligence-based pelvis fracture first-aid assistant decision-making method, which is characterized by comprising the following steps of:
s1, importing basic information of the pelvis fracture emergency treatment patient, and collecting chief complaints, current medical history, past medical history, personal history, dynamically-changed vital signs, inspection results and clinical treatment measures of the pelvis fracture emergency treatment patient;
s2, based on the collected information of the pelvis fracture emergency patients, carrying out image recognition on the iconography of the pelvis fracture emergency patients, carrying out accurate Tile typing, Young-Burgess typing and AO typing on the pelvis fracture, and carrying out trauma scoring, trauma severity scoring, concise damage scoring, improved Glasgow coma scoring and shock index scoring by analyzing the current disease data of the pelvis fracture emergency patients;
s3, performing semantic analysis and matching with a diagnosis and treatment intelligent auxiliary decision knowledge base based on the basic information, the fracture type, the trauma score, the trauma severity score, the brief damage score, the improved Glasgow coma score and the shock index score of the pelvis fracture emergency patient to obtain an emergency intelligent auxiliary decision suggestion for the pelvis fracture emergency patient;
and S4, displaying the emergency intelligent aid decision-making suggestion and the collected patient information for the patient with the pelvis fracture emergency treatment.
Preferably, the wound severity scoring algorithm is used for scoring the wound severity, the concise damage scoring algorithm is used for scoring the concise damage, the improved Glasgow coma scoring algorithm is used for scoring the improved Glasgow coma, and the shock index algorithm is used for scoring the shock index.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
1. the patient information is quickly collected on the same platform, so that the first-aid time of the pelvic fracture patient is greatly shortened, and the success rate of first-aid is improved;
2. the clinician is guided to carry out the first aid of the patient with pelvic fracture, thereby avoiding missed diagnosis to the utmost extent, accurately judging the bleeding source, grasping the time for treatment and selecting proper first-aid measures;
3. the artificial intelligence continuously learns, provides advanced evidence-based medical evidence-sufficient emergency measures, and obviously improves the survival probability of patients;
4. provides abundant case materials for developing clinical research, optimizes the first-aid process of pelvic fracture and obviously improves the experience accumulation of clinicians.
Drawings
Fig. 1 is a block diagram of an artificial intelligence-based pelvic fracture emergency aid decision-making system according to a preferred embodiment of the invention.
Fig. 2 is a flowchart of an artificial intelligence-based pelvis fracture emergency aid assistant decision-making method according to a preferred embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides an artificial intelligence-based pelvic fracture emergency aid assistant decision system, which includes a data layer 1, a processing layer 2, and an application layer 3 (see fig. 1) of an artificial intelligence-based pelvic fracture emergency aid assistant decision platform.
The data layer 1 comprises a patient data acquisition and pelvis fracture emergency treatment intelligent aid decision-making knowledge base, wherein the knowledge base comprises historical case data and intelligently retrieved pelvis fracture diagnosis and treatment literature data. Data collection is conducted into 120 hospital clinical data such as an emergency center, an HIS/LIS/RIS and the like through manual input or an API interface, basic information of a patient is imported and recorded, data information of the patient is created, vital signs are collected, inspection and examination results, clinical treatment measures and the like are collected, the state of illness of the patient is tracked in real time, input is conducted in a modularized and electronic mode, efficiency is improved, and missed diagnosis is avoided. The processed trauma patient information data is stored in an intelligent assistant decision knowledge base in the form of historical cases; relevant documents of pelvis fracture emergency treatment intelligently screened by the processing layer are stored in a knowledge base for reference of a clinician.
The processing layer 2 intelligent learning has machine learning and deep learning capabilities, relevant knowledge and cases of pelvic fracture emergency treatment are intelligently learned, relevant literature data are extracted to expand an intelligent aid decision-making knowledge base, the knowledge base is periodically updated, and advanced evidence-based medical evidence-full emergency strategy suggestions are provided; through carrying out image recognition on the patient imaging, carrying out accurate Tile typing, Young-Burgess typing, AO typing and the like on pelvic fracture, carrying out wound scoring, wound severity scoring, concise damage scoring, Glasgow coma scoring and shock index improvement through analyzing the current illness state data of the patient, carrying out semantic analysis and matching on the patient data and a diagnosis and treatment intelligent auxiliary decision knowledge base, providing decision suggestions for prognosis judgment, inspection, examination, treatment, consultation, transfer and the like, displaying the decision suggestions on an application layer 3, accurately judging bleeding sources, grasping the time for treatment and selecting appropriate emergency measures.
The processing layer is used for scoring the wound severity by adopting a wound severity scoring algorithm, scoring the wound concisely by adopting a concisely scoring algorithm, scoring the improved Glasgow coma by adopting an improved Glasgow coma scoring algorithm, and scoring the shock index by adopting a shock index algorithm.
The application layer 3 is used for displaying the real-time illness state of the patient, intelligent aid decision suggestion and actual processing measures, processing and summarizing data, and storing the summarized data in the data layer to form historical cases.
Example 2
As shown in fig. 2, the present embodiment provides an artificial intelligence-based pelvic fracture emergency aid assistant decision-making method, including the following steps:
102, based on the collected information of the pelvis fracture emergency patients, carrying out image recognition on the iconography of the pelvis fracture emergency patients, carrying out accurate Tile typing, Young-Burgess typing and AO typing on the pelvis fracture, and carrying out wound scoring, wound severity scoring, concise damage scoring, improved Glasgow coma scoring and shock index scoring by analyzing the current disease data of the pelvis fracture emergency patients.
And 103, performing semantic analysis and matching with a diagnosis and treatment intelligent auxiliary decision knowledge base based on basic information, fracture typing, trauma score, trauma severity score, concise damage score, improved Glasgow coma score and shock index score of the pelvis fracture emergency patient to obtain an emergency intelligent auxiliary decision suggestion for the pelvis fracture emergency patient.
Based on decision suggestion, a clinician timely makes clinical treatment, vital signs and examination and inspection results of a pelvis fracture patient are dynamically changed after treatment, and the pelvis fracture emergency aid assistant decision platform processing layer based on artificial intelligence continues to assist clinical decision until the state of an illness returns: improvement-admission in general ward; exacerbation-intensive care therapy, transfer, death; an operating room.
And 104, displaying an emergency intelligent aid decision-making suggestion and collected patient information for the patient with the pelvis fracture emergency treatment.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (6)
1. An artificial intelligence-based pelvis fracture emergency aid assistant decision-making system is characterized by comprising a data layer, a processing layer and an application layer;
the data layer comprises a pelvis fracture emergency treatment intelligent aid decision-making knowledge base, the intelligent aid decision-making knowledge base comprises historical case data and intelligently retrieved pelvis fracture diagnosis and treatment literature data, and the data layer is used for importing basic information of a pelvis fracture emergency treatment patient, creating data information of the pelvis fracture emergency treatment patient, collecting vital signs, testing and checking results and carrying out clinical treatment measures to track disease state changes of the patient in real time;
the processing layer is used for intelligently learning related knowledge and cases of the pelvis fracture emergency treatment, extracting related literature data to expand an intelligent aid decision-making knowledge base, carrying out image recognition on the imaging of a patient with the pelvis fracture emergency treatment, carrying out semantic analysis and matching on the data analyzed by the patient with the pelvis fracture emergency treatment and the intelligent aid decision-making knowledge base, and extracting decision-making suggestions to be displayed on the application layer;
the application layer is used for displaying relevant illness state data, emergency treatment intelligent aid decision suggestions and actual treatment measures of the pelvic fracture emergency treatment patient, and the summarized data are summarized to the data layer.
2. The artificial intelligence based pelvic fracture aid decision making system according to claim 1, wherein the data layer is used for introducing post-traumatic hip pain and collecting chief complaints, current medical history, past medical history, personal history, dynamically changing vital signs, examination results and clinical treatment measures of the pelvic fracture aid patient in consideration of basic information of the pelvic fracture aid patient.
3. The artificial intelligence based pelvis fracture emergency aid decision making system according to claim 1, wherein the processing layer is used for carrying out accurate Tile typing, Young-Burgess typing and AO typing on pelvis fracture by carrying out image recognition on the imaging of the pelvis fracture emergency aid patient based on the collected information of the pelvis fracture emergency aid patient, carrying out trauma scoring, trauma severity scoring, concise damage scoring, improved Glasgow coma scoring and shock index scoring by analyzing the current disease data of the pelvis fracture emergency aid patient;
based on the basic information, the fracture type, the trauma score, the trauma severity score, the concise damage score, the improved Glasgow coma score and the shock index score of the pelvis fracture emergency patient, semantic analysis and matching are carried out on the pelvis fracture emergency patient and the diagnosis and treatment intelligent auxiliary decision knowledge base, so that the first-aid intelligent auxiliary decision suggestion for the pelvis fracture emergency patient is obtained.
4. The artificial intelligence based pelvic fracture first aid decision making system of claim 3, wherein the processing layer is configured to perform the trauma severity scoring using a trauma severity scoring algorithm, the concise damage scoring using a concise damage scoring algorithm, the modified Glasgow coma scoring using a modified Glasgow coma scoring algorithm, and the shock index scoring using a shock index algorithm.
5. An artificial intelligence-based pelvis fracture emergency aid assistant decision-making method is characterized by comprising the following steps:
s1, importing basic information of the pelvis fracture emergency treatment patient, and collecting chief complaints, current medical history, past medical history, personal history, dynamically-changed vital signs, inspection results and clinical treatment measures of the pelvis fracture emergency treatment patient;
s2, based on the collected information of the pelvis fracture emergency patients, carrying out image recognition on the iconography of the pelvis fracture emergency patients, carrying out accurate Tile typing, Young-Burgess typing and AO typing on the pelvis fracture, and carrying out trauma scoring, trauma severity scoring, concise damage scoring, improved Glasgow coma scoring and shock index scoring by analyzing the current disease data of the pelvis fracture emergency patients;
s3, performing semantic analysis and matching with a diagnosis and treatment intelligent auxiliary decision knowledge base based on the basic information, the fracture type, the trauma score, the trauma severity score, the brief damage score, the improved Glasgow coma score and the shock index score of the pelvis fracture emergency patient to obtain an emergency intelligent auxiliary decision suggestion for the pelvis fracture emergency patient;
and S4, displaying the emergency intelligent aid decision-making suggestion and the collected patient information for the patient with the pelvis fracture emergency treatment.
6. The artificial intelligence-based pelvis fracture emergency aid decision-making method according to claim 5, wherein a wound severity scoring algorithm is adopted for scoring the wound severity, a concise damage scoring algorithm is adopted for scoring the concise damage, an improved Glasgow coma scoring algorithm is adopted for scoring the improved Glasgow coma, and a shock index algorithm is adopted for scoring the shock index.
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