Trauma big data analysis method and system

Abstract

This patent application discloses a trauma big data analysis method and system, which involves big data technology in the medical field, and aims to improve the comprehensiveness of data collection in existing solutions. The system includes a data collection module, which includes the following data collection interfaces: a traffic information interface for collecting regional traffic data; a social media interface for collecting information posted by users on social media; a weather information interface for collecting weather information; and a natural disaster information interface for collecting natural disaster information. This solution can improve the comprehensiveness and real-time nature of data: by integrating multiple data collection interfaces, the system can obtain trauma-related information in real time from multiple angles, thereby improving the comprehensiveness and real-time nature of the data.

Description

Technical Field

[0001] The present invention relates to big data technology in the medical field, in particular to data collection technology. Background Art

[0002] Due to problems such as untimely treatment of trauma, millions of people die every year around the world. It most often occurs in younger age groups, with people under 45 years old ranking first. The mortality rate is the highest among all age groups, and the various losses caused by it also rank first. Among them, the most common traumatic event is car accidents. First aid is an important means of trauma treatment.

[0003] Trauma emergency treatment in my country can be guided by big data, and through the construction of a large platform, an integrated information exchange platform can be formed to achieve efficient trauma emergency treatment. At present, various types of social public data have become an irreplaceable and important content in human daily work activities. Pre-hospital emergency treatment, public health, and clinical medicine have extensive demand for data. Therefore, while efficiently completing emergency tasks or handling emergency health events in daily medical work, it is necessary to pay attention to data collection and accumulation. The demand for big data in trauma emergency treatment is large. It is necessary to apply the accumulation and analysis of data and its results to the cloud command system of the emergency and pre-hospital emergency platform through the network to ensure the grasp of the "platinum 10 minutes and golden 1 hour" of emergency treatment and achieve the accuracy of rescue.

[0004] A Chinese patent with patent publication number CN113014643A discloses an intelligent monitoring and early warning system for major emergencies using emergency big data, including a control module, the control module is electrically connected to a big data analysis module, a voltage regulation module, an auxiliary module and an information processing differentiation module; the big data analysis includes model construction, proportion prediction and data shaping, the information processing differentiation module is electrically connected to an information acquisition module, the information acquisition module is electrically connected to a digital-to-analog conversion module, the digital-to-analog conversion module is electrically connected to a communication module, and the communication module is communicatively connected to a hospital system. The present invention realizes real-time transmission of cases through the communication module, the information acquisition module and the information processing differentiation module process and differentiate the information, and analyzes the data information through the model. The proportion prediction can predict the incidence rate of the disease and effectively predict the incidence of the disease. The pathology of the disease is predicted through proportion analysis to facilitate treatment.

[0005] In fact, the above schemes still have the problem of incomplete data collection. No specific and valuable data collection schemes are proposed, which makes it difficult to guide technical personnel in the field to carry out comprehensive data collection planning. There is no solution on how to improve rapid response capabilities, and it is impossible to provide sufficient information support for emergency health events.

[0006] Any discussion of the background technology throughout the specification does not mean that the background technology is necessarily the prior art known to technicians in the relevant field; any discussion of the prior art throughout the specification does not mean that the prior art is necessarily widely known. Summary of the invention

[0007] The present invention aims to provide a trauma big data analysis method and system to improve the comprehensiveness of data collection in existing solutions.

[0008] The trauma big data analysis system includes a data acquisition module, which includes the following data acquisition interfaces:

[0009] Traffic information interface, used to collect regional traffic data;

[0010] Social media interface, used to collect information posted by users on social media;

[0011] Weather information interface, used to collect weather information;

[0012] Natural disaster information interface, used to collect natural disaster information.

[0013] The above data interface can be used to input a large amount of historical data at one time for subsequent analysis, or it can be connected to an existing data source (such as the API interface of a certain network social platform) to collect relevant data in real time. The analysis system may include the following modules: data preprocessing module: cleaning, converting and standardizing the collected data; data storage module: storing the processed data; data processing and analysis module: applying statistics, machine learning algorithms and other methods to conduct comprehensive analysis and processing on all collected data to improve the accuracy of trauma event prediction; data visualization module: presenting the analysis results in a form that is easy for people to understand.

[0014] The beneficial effects of this program include the following:

[0015] Improve the comprehensiveness and real-time nature of data: By integrating multiple data acquisition interfaces, the system can obtain trauma-related information from multiple angles in real time, thereby improving the comprehensiveness and real-time nature of data. This is crucial for timely detection of potential trauma risk factors.

[0016] Enhanced prediction accuracy and response speed: Traffic information interface can collect traffic status in a certain area, such as traffic volume, vehicle speed, emergency situations, etc. Social media interface can capture the emotional changes of users and the development trend of social events, which helps to warn of possible mass incidents or emergencies in advance. Sentiment analysis can analyze the text content on social media through natural language processing technology (NLP) to identify possible emergency signals. For example, a concentrated outbreak of negative emotions may indicate the approach of a certain crisis. Further semantic understanding of information can be carried out: not only staying at the surface text analysis, but also deeply understanding the real intentions expressed by social media users, so as to more accurately judge whether there is a real need for emergency treatment. Weather information interface can provide data on meteorological changes, which helps to analyze the relationship between weather conditions and trauma incidence. Natural disaster information interface can immediately reflect the situation of sudden disasters such as earthquakes and floods, so that the system can respond at the first time.

[0017] Multimodal data analysis improves prediction accuracy: Analyze traffic, social media, weather, and natural disaster information as a whole to explore the interactions between them. For example, analyze whether specific weather conditions will affect the driver's state and thus interfere with the probability of traffic accidents, and whether the driver's emotional fluctuations on social media are related to the probability of the traffic accident.

[0018] Promote efficient allocation of resources: By comprehensively analyzing traffic, social media, weather and natural disaster data, the system can help relevant departments grasp more on-site information, allocate medical resources more accurately, and improve emergency rescue efficiency. For example, when it is predicted that a certain area may have a high incidence of accidents due to bad weather, ambulances and medical personnel can be arranged in advance.

[0019] Assisting policy making and public education: Long-term data accumulation and analysis can reveal the occurrence patterns of certain traumatic events and provide a scientific basis for the government to formulate relevant policies. At the same time, based on the results of data analysis, targeted public health education activities can be carried out to improve people's safety awareness and self-help ability.

[0020] Improve the level of public safety management: Such a system helps to build a more intelligent urban management system. By integrating various data sources, it can achieve comprehensive monitoring of the city’s security situation, thereby improving the public safety management capabilities and level of the entire city.

[0021] Furthermore, the social media interface is connected to a historical information collection module, which is used to locate the accounts of the rescued person and the assisting person on the social media and collect historical information from the accounts.

[0022] The rescued person refers to the person who is injured in the accident and needs to be rescued, and the assistant refers to the person who can provide rescue assistance at the scene; or the person who knows the rescued person's information and can provide helpful information (such as the rescued person's medical history, physical condition, etc.).

[0023] After an accident is discovered, the user account history information is analyzed to provide a reference for the emergency plan. At the same time, the emergency command center can directly reach the on-site personnel to grasp the on-site situation through photos, audio, video, live broadcast, etc., analyze the companions of the injured, and establish communication.

[0024] Furthermore, it also includes a terminal positioning interface, which is used to obtain the positioning information of the terminal, such as the GPS positioning information of the mobile phone terminal, which is used to obtain the location information of the crowd.

[0025] Furthermore, it includes an activity information interface for obtaining activity information, such as concerts, competitions and other large-scale activity information collection, which may bring higher accident rates.

[0026] Furthermore, a map information interface is included to collect location information of various places, such as factories, bars, etc. The accident rates of different places are also different.

[0027] Furthermore, a health terminal interface is included for receiving data from health monitoring devices, such as health watches, portable electrophysiological meters, etc., which can be used to quickly identify critically ill patients at the scene of an accident.

[0028] Furthermore, it includes a satellite data receiving interface for receiving remote sensing data provided by a satellite.

[0029] The trauma big data analysis method includes the following steps:

[0030] f) Collect regional traffic data;

[0031] g) Collect information posted by users on social media;

[0032] h) Collect weather information;

[0033] i) Collecting natural disaster information;

[0034] j) Using the above collected information for subsequent trauma emergency analysis. DETAILED DESCRIPTION

[0035] Example 1: Trauma big data analysis system

[0036] 1. System Overview

[0037] This embodiment provides a trauma big data analysis system, which includes a data collection module, which is further subdivided into a social media interface, a weather information interface, and a natural disaster information interface, aiming to improve the prediction accuracy and response speed of trauma events.

[0038] 2. Data acquisition module

[0039] The data acquisition module is responsible for obtaining relevant information from different data sources and integrating it into the system for subsequent processing. The interface can exist in the form of a software programming interface (API).

[0040] 2.1 Social Media Interface

[0041] Function: Automatically crawl user information posted on social media platforms, including but not limited to user location, posted content, comments, and sentiment tags.

[0042] Implementation method: Interact with the API provided by the social media platform through HTTP requests, obtain user authorization using the OAuth authentication mechanism, and then crawl data regularly or in real time.

[0043] 2.2 Weather Information Interface

[0044] Function: Get real-time weather forecast and live information released by the meteorological department.

[0045] Implementation method: Cooperate with the National Meteorological Administration or third-party weather service providers to obtain data through API calls. Data types include temperature, humidity, wind speed, air pressure and other meteorological parameters.

[0046] 2.3 Natural disaster information interface

[0047] Function: Collect relevant information on natural disasters such as earthquakes, floods, and typhoons.

[0048] Implementation method: Data files can be downloaded through RESTful API or FTP / SFTP protocols, integrating data from official channels such as geological survey agencies and hydrological stations. At the same time, geographic information system (GIS) technology can also be used to combine satellite remote sensing data for monitoring.

[0049] 2.4 Traffic Information Interface

[0050] Function: Automatically collect information from various traffic data sources.

[0051] Implementation methods: including but not limited to traffic cameras, traffic management departments, GPS navigation systems, etc. The API interface can be developed using the RESTful architecture to ensure that it complies with HTTP standards and is easy to integrate.

[0052] 3. Data processing flow

[0053] Data collection: Get raw data through the above interface.

[0054] Data cleaning: remove invalid data, such as duplicate information, junk information, etc.

[0055] Data fusion: Integrate data from different sources to form a unified data set.

[0056] Data analysis: Use statistical and machine learning methods to analyze the correlation between data and predict the trend of traumatic events. Use multimodal data analysis in the process: Analyze traffic information, social media, weather and natural disaster information as a whole to explore the interaction between them. Select appropriate machine learning or deep learning models, such as random forests, support vector machines (SVMs), long short-term memory networks (LSTMs), etc., to explore the interaction between different data sources. Model training: Use training set data to train the model, adjust model parameters to improve prediction performance, and then use statistical analysis methods such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc. to explore the correlation between different data sources. Causal inference techniques, such as potential causal diagrams, causal effect estimation, etc., can also be used to try to understand whether a variable causes changes in another variable. Finally, based on the model prediction results, scenario simulation experiments are conducted to evaluate the probability of emergency events under different conditions.

[0057] Result output: Visualize the analysis results and provide them to decision makers for reference. Predict possible traumatic events and deploy medical resources in advance.

[0058] The system may also include the following acquisition modules:

[0059] 1. Historical information collection module: used to locate the accounts of rescued persons and assisting persons on social media, and collect historical information from the accounts. By analyzing the historical information of user accounts, it provides reference for emergency plans. In addition, it allows the emergency command center to communicate directly with on-site personnel through photos, audio, video, live broadcast, etc., to grasp the on-site situation, analyze the companions of the injured, and form effective communication.

[0060] 2. Terminal positioning interface

[0061] Acquisition of positioning information: This module obtains the location information of the crowd through the GPS positioning information of the mobile terminal to help determine the specific location of the accident scene and the location of related personnel.

[0062] 3. Activity information interface

[0063] Event information collection: used to obtain information about large-scale events (such as concerts, competitions, etc.), as these events may bring higher accident rates, thus helping to predict potential risk areas.

[0064] 4. Map information interface

[0065] Location information collection: used to collect location information of various places, such as factories, bars, etc., because different places have different accident rates, which helps to analyze the possibility of accidents.

[0066] 5.Healthy terminal interface

[0067] Health data reception: This module is used to receive data from health monitoring devices (such as health watches, portable electrophysiological meters, etc.) in order to quickly identify critically ill patients at the scene of the accident and provide targeted first aid measures.

[0068] 6. Satellite data receiving interface

[0069] Remote sensing data reception: This module is used to receive remote sensing data provided by satellites. These data can help analyze the terrain and provide geographic information support for rescue operations at the accident site.

[0070] Embodiment 2 Accident Rescue Embodiment

[0071] A traffic accident occurred in a city in Sichuan Province. The trauma big data analysis system detected abnormal high-risk areas for traffic accidents by real-time monitoring of traffic flow, vehicle speed changes, and traffic camera video streams, combined with GPS data to locate abnormally stagnant vehicles. Then, the traffic video camera information was used to confirm the on-site situation.

[0072] At the same time, the system monitors the real-time information flow on social media platforms, uses natural language processing technology to identify posts containing relevant keywords such as "car accident" and "accident", and combines geographic location tags to confirm the specific location of the incident, and then analyzes the pictures, videos and text descriptions in the posts to grasp the preliminary situation of the accident, such as the number of injured and the type of accident.

[0073] The system analyzes social media activities near the accident site and uses natural language processing technology to identify information posted by users related to the accident. It then confirms the identity of the rescued person through matching the geolocation tags and personal data in this information, and then obtains their contact information and other relevant information to assist in rescue work.

[0074] The system analyzes relevant content posted on social media platforms near the accident site, uses natural language processing and geographic location information matching technology to identify and lock user accounts related to the accident, and then confirms the social media account of the rescued person in order to further obtain relevant information for rescue. The system accesses the social media account of the rescued person and uses natural language processing technology to analyze the status, pictures and videos recently posted by the rescued person, and identifies any information that may be of guiding significance for first aid, such as the health status, allergy history, previous medical history or emergency contacts mentioned by the rescued person, so as to provide it to medical staff as a reference for first aid.

[0075] The system analyzes the friend lists, interaction records, and information about family members mentioned in the rescued person's social media account, and uses social graph technology and natural language processing technology to identify and confirm the social media accounts of their relatives and friends, so as to contact assistants who can provide help and promptly notify and communicate with them about the accident. At the same time, the system can also search for information that is instructive for first aid in the public information of the relatives and friends, and can also communicate with assistants on the platform and transmit accident scene information to the system through video, pictures, voice, and even live broadcast.

[0076] The above is only an embodiment of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the scheme of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A trauma big data analysis system, including a data acquisition module, characterized in that: The data acquisition module includes the following data acquisition interfaces: Traffic information interface, used to collect regional traffic data; Social media interface, used to collect information posted by users on social media; Weather information interface, used to collect weather information; Natural disaster information interface, used to collect natural disaster information.

2. The trauma big data analysis system according to claim 1, characterized in that: The social media interface is connected to a historical information collection module, which is used to locate the accounts of the rescued person and the assisting person on the social media and collect historical information from the accounts.

3. The trauma big data analysis system according to claim 2, characterized in that: It also includes a terminal positioning interface for obtaining the terminal's positioning information.

4. The trauma big data analysis system according to claim 3, characterized in that: Includes activity information interface, used to obtain activity information.

5. The trauma big data analysis system according to claim 4, characterized in that: Includes a map information interface for collecting location information of various places.

6. The trauma big data analysis system according to claim 5, characterized in that: Includes a health terminal interface for receiving data from health monitoring devices.

7. The trauma big data analysis system according to claim 6, characterized in that: It includes a satellite data receiving interface for receiving remote sensing data provided by the satellite.

8. Trauma big data analysis method, characterized by The following steps are involved: a) Collect regional traffic data; b) Collect information posted by users on social media; c) Collect weather information; d) Collecting natural disaster information; e) Using the above collected information for subsequent trauma emergency analysis.