Intelligent acquisition and analysis system and method for emergency patient data

Abstract

The invention provides an intelligent collection and analysis system and method for emergency patient data, and relates to the technical field of intelligent analysis of the emergency patient data. The data analysis module is used for receiving the emergency patient data set transmitted by the multi-dimensional acquisition module; the data integration module is used for being responsible for integrating all the medical archives stored in the database; the independent coding library generation unit is used for integrating the data set based on the data integration module; according to the system, a received data set is analyzed by applying a machine learning algorithm and a medical knowledge rule, and a standard information data set is generated, so that the situation that a large amount of manpower and time need to be consumed for data conversion, cleaning and matching is reduced, the data processing cost is increased, and data errors and omission are likely to occur; the probability of data consistency and manageability is influenced, and the probability that the timeliness of treatment decision is influenced due to the fact that a doctor is difficult to quickly acquire required patient information in an emergency is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent analysis of emergency patient data, and in particular to an intelligent collection and analysis system and method for emergency patient data. Background Art

[0002] Emergency medicine refers to the medical process of providing emergency treatment and rescue to patients with sudden illness, accidental injury or other acute conditions. Emergency medicine places high demands on the professional level and emergency response capabilities of medical staff. During the emergency process, timely and accurate monitoring and analysis of the patient's vital signs data is an important basis for judging the condition and taking treatment measures.

[0003] In terms of data integration, the existing emergency patient data management system faces huge challenges. Due to the different data formats and standards generated by different medical institutions, departments, and even different medical equipment, the data integration process becomes extremely complicated. For example, some hospitals use paper medical records to record patient information, while others use electronic medical record systems. The data formats between different electronic medical record systems also vary. This results in a large amount of manpower and time required for data conversion, cleaning, and matching during data integration. This not only increases the cost of data processing, but also makes data errors and omissions prone to occur, affecting data consistency and manageability. In addition, data consistency and manageability issues also bring difficulties to the analysis and utilization of emergency patient data. Due to the lack of unified standards and coding, data from different sources are difficult to effectively compare and analyze. At the same time, the decentralized storage and management of data also makes data query and retrieval cumbersome. Doctors find it difficult to quickly obtain the required patient information in emergency situations, which affects the timeliness of treatment decisions.

[0004] Therefore, it is necessary to provide a new intelligent collection and analysis system and method for emergency patient data to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an intelligent collection and analysis system and method for emergency patient data.

[0006] The intelligent collection and analysis system for emergency patient data provided by the present invention includes a multi-dimensional collection module for collecting multi-dimensional data of emergency patients;

[0007] Real-time monitoring and early warning module, used to continuously track patients' key vital signs and predict possible deterioration trends based on the comparison of historical data and real-time data;

[0008] A data analysis module is used to receive the emergency patient data set transmitted by the multi-dimensional acquisition module, analyze and process it, and pre-process the data. The data analysis module includes a standard information data set generation unit and an independent code library generation unit. The standard information data set generation unit is used to analyze the received data set based on the collected emergency patient data set using a machine learning algorithm and medical knowledge rules to generate a standard information data set. The independent code library generation unit is used to integrate all medical records stored in the database and generate an independent code library.

[0009] Data integration module, responsible for integrating medical records stored in all databases;

[0010] The data management module is used to match the standard information data group generated by the data analysis module with the independent code library generated by the independent code library generation unit, and assign corresponding codes to each piece of information in the standard information data group.

[0011] Preferably, the step of analyzing the received data set using a machine learning algorithm and medical knowledge rules to generate a standard information data set comprises the following steps:

[0012] S1. Data preprocessing: First, based on the reasonable range of medical data and common error patterns, duplicate records and erroneous data are removed, and missing values ​​are filled. Then, the raw data collected by the multidimensional acquisition module is converted into a structured data format;

[0013] S2. Preliminary screening based on medical knowledge rules: preliminary screening of data based on established medical diagnostic standards and clinical guidelines, while using medical knowledge rules to identify potential risk factors in the data;

[0014] S3, feature engineering, uses machine learning algorithms to extract valuable features from the data and selects the most meaningful features for subsequent analysis based on medical knowledge and machine learning algorithms;

[0015] S4. Machine learning model analysis: using annotated medical datasets to train machine learning models, and applying the trained models to the data to be analyzed. At the same time, machine learning models are used to predict and analyze patient data to assist in medical diagnosis;

[0016] S5. Generate a standard information data set, format the data that has undergone the above processing according to a unified medical data standard, and then integrate key patient information, the results of the analysis based on medical knowledge rules, and the conclusions of the machine learning model analysis to form a standard information data set.

[0017] Preferably, the integration of medical records stored in all databases includes the following steps:

[0018] S11, data collection, by extracting data from hospital information systems, laboratory information systems, imaging archiving and communication systems, and electronic medical record systems;

[0019] S12. Data cleaning: First, remove duplicates, identify and eliminate duplicate records, check and correct errors in the data, and finally fill in missing information based on the context or using statistical methods;

[0020] S13, standardization, converting medical terms used in different sources into systematic medical nomenclature - clinical terminology and ensuring that all data are stored in a predetermined format;

[0021] S14, Coding, assign a unique identifier to each patient, each visit, and each diagnosis, and use;

[0022] S15. Integration and verification: Integrate the cleaned and standardized data into the new coding library to ensure logical consistency and conflict-free data. Verify the accuracy and completeness of the data through sampling inspection or other means.

[0023] S16, full and privacy protection, using advanced encryption standards to protect the security of sensitive information.

[0024] Preferably, the standard information data set generation unit is responsible for converting the original emergency data into a structured, standardized medical information set. The unit ensures that the generated information set has integrity, consistency and clinical decision support value through machine learning algorithms.

[0025] Preferably, the independent coding library generation unit is responsible for converting the integrated medical files into a unified coding system, and mapping heterogeneous medical data into standardized codes, establishing a mapping relationship of "original data-medical concept-unique code", and realizing structured storage and rapid retrieval of data.

[0026] Preferably, the multi-dimensional data of the emergency patient includes the patient's basic information, vital signs data, symptom description, past medical history and allergy history;

[0027] The patient's key vital signs include body temperature, heart rate, blood pressure, respiratory rate, blood oxygen saturation and state of consciousness.

[0028] The second aspect of the present invention provides an intelligent collection and analysis method for emergency patient data, comprising the following steps:

[0029] S10, data collection, using the multi-dimensional acquisition module to collect multi-dimensional data of emergency patients and transmit it to the data analysis module;

[0030] S20, data analysis, using a data analysis module to analyze and process the collected emergency patient data set to generate a standard information data set;

[0031] S30, data integration, using the data integration module to integrate all medical records stored in the database, eliminate the differences between the data, and form a unified data set;

[0032] S40, generating an independent coding library, using an independent coding library generating unit to analyze and encode the integrated data set to generate an independent coding library;

[0033] S50, data matching and query, using the data management module to match the standard information data set with the independent coding library, and perform search and query in the independent coding library based on the search data set.

[0034] Compared with related technologies, the intelligent collection and analysis system and method for emergency patient data provided by the present invention has the following beneficial effects:

[0035] The present invention uses machine learning algorithms and medical knowledge rules to analyze the received data sets and generate standard information data sets. First, it pre-processes the data according to the reasonable range of medical data and converts the raw data obtained by the multi-dimensional acquisition module into a structured data format. Then, it performs preliminary screening based on medical knowledge rules, filters the data according to established medical diagnostic standards and clinical guidelines, and identifies potential risk factors. Then, it enters the feature engineering stage, uses machine learning algorithms to extract valuable features from the data, and combines medical knowledge to select the key features that are most meaningful for subsequent analysis. After completing feature selection, the machine learning model is trained with the labeled medical data set and applied to the data to be analyzed. The prediction and analysis of analytical data assists doctors in making clinical judgments, and then formats all data according to unified medical data standards, and integrates key patient information, medical rule analysis results, and prediction conclusions of machine learning models to form a standardized, structured standard information data set, providing a high-quality data foundation for subsequent intelligent analysis and decision support. This device reduces the need for a large amount of manpower and time for data conversion, cleaning, and matching, increases the cost of data processing, and is prone to data errors and omissions, affecting the consistency and manageability of the data. It also reduces the probability that doctors will have difficulty quickly obtaining the required patient information in an emergency, thereby affecting the timeliness of treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a structural block diagram of the intelligent collection and analysis system for emergency patient data provided by the present invention;

[0037] Figure 2A flowchart of the present invention for analyzing a received data set using a machine learning algorithm and medical knowledge rules;

[0038] Figure 3 A flowchart of the process of integrating medical records stored in all databases provided by the present invention;

[0039] Figure 4 This is a flowchart of the intelligent collection and analysis method for emergency patient data provided by the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] Please refer to Figure 1 、 Figure 2 、 Figure 3 as well as Figure 4 ,in, Figure 1 This is a structural block diagram of the intelligent collection and analysis system for emergency patient data provided by the present invention; Figure 2 A flowchart of the present invention for analyzing a received data set using a machine learning algorithm and medical knowledge rules; Figure 3 A flowchart of the process of integrating medical records stored in all databases provided by the present invention; Figure 4 This is a flowchart of the intelligent collection and analysis method for emergency patient data provided by the present invention.

[0042] Example 1

[0043] In the specific implementation process, Figures 1 to 3 As shown, the intelligent collection and analysis system for emergency patient data provided by the present invention includes a multi-dimensional collection module for collecting multi-dimensional data of emergency patients;

[0044] It should be noted that the multi-dimensional data of emergency patients include basic information of the patients, vital signs data, symptom description, past medical history, and allergy history;

[0045] The functions and features of the multi-dimensional acquisition module include data source integration, real-time data acquisition and synchronization, data cleaning and pre-processing, and security and privacy protection;

[0046] Real-time monitoring and early warning module, used to continuously track patients' key vital signs and predict possible deterioration trends based on the comparison of historical data and real-time data;

[0047] It should be noted that the patient's key vital signs include body temperature, heart rate, blood pressure, respiratory rate, blood oxygen saturation, and state of consciousness;

[0048] Continuous tracking of key vital signs in the real-time monitoring and early warning module includes vital sign parameters, multi-device data fusion, and real-time data updates;

[0049] Among them, vital sign parameters usually monitor multiple key vital signs of patients in real time and continuously, including but not limited to heart rate, blood pressure (systolic pressure, diastolic pressure and mean arterial pressure), respiratory rate, body temperature, blood oxygen saturation basic vital signs as well as electrocardiogram (ECG) waveform, blood sugar level, blood gas analysis indicators (such as pH, oxygen partial pressure, carbon dioxide partial pressure, etc.);

[0050] Multi-device data fusion: By connecting to various medical devices, such as monitors, ventilators, blood glucose meters, and blood gas analyzers, the module can simultaneously collect data from different devices and perform fusion processing. This allows for a comprehensive understanding of the patient's physiological status from multiple dimensions, avoiding the limitations of single device data.

[0051] Real-time data updates, with a high frequency of real-time updates of vital signs data, ensuring that medical staff obtain the latest information on the patient's status;

[0052] A data analysis module is used to receive the emergency patient data set transmitted by the multi-dimensional acquisition module, analyze and process it, and pre-process the data. The data analysis module includes a standard information data set generation unit and an independent code library generation unit. The standard information data set generation unit is used to analyze the received data set based on the collected emergency patient data set using a machine learning algorithm and medical knowledge rules to generate a standard information data set. The independent code library generation unit is used to integrate all medical records stored in the database and generate an independent code library.

[0053] It should be noted that the standard information data set generation unit is responsible for converting raw emergency data into structured, standardized medical information sets. This unit uses machine learning algorithms to ensure that the generated information sets are complete, consistent, and have clinical decision support value.

[0054] The independent coding library generation unit is responsible for converting the integrated medical records into a unified coding system, mapping heterogeneous medical data into standardized codes, establishing a mapping relationship between "raw data - medical concepts - unique codes", and realizing structured storage and rapid retrieval of data;

[0055] Analyzing the received data set using machine learning algorithms and medical knowledge rules to generate a standard information data set includes the following steps:

[0056] S1. Data preprocessing: First, based on the reasonable range of medical data and common error patterns, duplicate records and erroneous data are removed, and missing values ​​are filled. Then, the raw data collected by the multidimensional acquisition module is converted into a structured data format;

[0057] S2. Preliminary screening based on medical knowledge rules: preliminary screening of data based on established medical diagnostic standards and clinical guidelines, while using medical knowledge rules to identify potential risk factors in the data;

[0058] S3, feature engineering, uses machine learning algorithms to extract valuable features from the data and selects the most meaningful features for subsequent analysis based on medical knowledge and machine learning algorithms;

[0059] S4. Machine learning model analysis: using annotated medical datasets to train machine learning models, and applying the trained models to the data to be analyzed. At the same time, machine learning models are used to predict and analyze patient data to assist in medical diagnosis;

[0060] S5. Generate a standard information data set, format the processed data according to a unified medical data standard, and then integrate key patient information, the results of the medical knowledge rule analysis, and the conclusions of the machine learning model analysis to form a standard information data set;

[0061] Integrating medical records stored in all databases involves the following steps:

[0062] S11, data collection, by extracting data from hospital information systems, laboratory information systems, imaging archiving and communication systems, and electronic medical record systems;

[0063] S12. Data cleaning: First, remove duplicates, identify and eliminate duplicate records, check and correct errors in the data, and finally fill in missing information based on the context or using statistical methods;

[0064] S13, standardization, converting medical terms used in different sources into systematic medical nomenclature - clinical terminology and ensuring that all data are stored in a predetermined format;

[0065] S14, Coding, assign a unique identifier to each patient, each visit, and each diagnosis, and use;

[0066] S15. Integration and verification: Integrate the cleaned and standardized data into the new coding library to ensure logical consistency and conflict-free data. Verify the accuracy and completeness of the data through sampling inspection or other means.

[0067] S16, full and privacy protection, using advanced encryption standards to protect the security of sensitive information;

[0068] Data integration module, responsible for integrating medical records stored in all databases;

[0069] It should be noted that the significance and role of the data integration module is to provide comprehensive patient information, support medical data analysis, and promote medical information sharing;

[0070] A data management module is used to match the standard information data group generated by the data analysis module with the independent code library generated by the independent code library generation unit, and assign a corresponding code to each piece of information in the standard information data group;

[0071] It should be noted that the detailed functions of the data management module include data storage and organization, data security and authority management, data quality management, and data lifecycle management. Among them, for data storage and organization, the data management module will create a dedicated data warehouse for centralized storage of various data of emergency patients, including real-time data collected from the multi-dimensional acquisition module and historical medical archive data. It will also standardize the format of the stored data to ensure that data of the same type has a consistent format, and create indexes for key data fields in the data warehouse.

[0072] Data security and rights management: Multiple security technologies are used to protect the security of emergency patient data, such as data encryption, access control, and firewalls. Data access rights are assigned to different users based on their roles and responsibilities. Finally, a comprehensive data backup strategy is developed to regularly back up emergency patient data, including local and off-site backups.

[0073] Data quality management involves real-time verification and review of data during data entry and collection to ensure its accuracy and completeness. Furthermore, consistency checks are regularly conducted on data in the data warehouse to ensure that there are no inconsistencies or conflicts between data from different sources and modules. Finally, a data quality assessment indicator system is established to regularly assess data accuracy, completeness, consistency, and timeliness.

[0074] Data lifecycle management is responsible for managing the creation and entry process of emergency patient data, providing medical staff with a convenient data entry interface and tools. At the same time, as the patient's treatment process progresses, the patient's data is updated in a timely manner, such as changes in the condition, updates to examination and test results, and adjustments to the treatment plan.

[0075] Example 2

[0076] refer to Figure 4 As shown, the second aspect of the present invention provides an intelligent collection and analysis method for emergency patient data, comprising the following steps:

[0077] S10, data collection, using the multi-dimensional acquisition module to collect multi-dimensional data of emergency patients and transmit it to the data analysis module;

[0078] S20, data analysis, using a data analysis module to analyze and process the collected emergency patient data set to generate a standard information data set;

[0079] S30, data integration, using the data integration module to integrate all medical records stored in the database, eliminate the differences between the data, and form a unified data set;

[0080] S40, generating an independent coding library, using an independent coding library generating unit to analyze and encode the integrated data set to generate an independent coding library;

[0081] S50, data matching and query, using the data management module to match the standard information data set with the independent coding library, and perform search and query in the independent coding library based on the search data set.

[0082] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. Intelligent collection and analysis system for emergency patient data, characterized by: Includes a multi-dimensional acquisition module for collecting multi-dimensional data of emergency patients; Real-time monitoring and early warning module, used to continuously track patients' key vital signs and predict possible deterioration of their condition based on the comparison of historical data and real-time data; A data analysis module is used to receive the emergency patient data set transmitted by the multi-dimensional acquisition module, analyze and process it, and pre-process the data. The data analysis module includes a standard information data set generation unit and an independent code library generation unit. The standard information data set generation unit is used to analyze the received data set based on the collected emergency patient data set using a machine learning algorithm and medical knowledge rules to generate a standard information data set. The independent code library generation unit is used to integrate all medical records stored in the database and generate an independent code library. Data integration module, responsible for integrating medical records stored in all databases; The data management module is used to match the standard information data group generated by the data analysis module with the independent code library generated by the independent code library generation unit, and assign corresponding codes to each piece of information in the standard information data group.

2. The intelligent collection and analysis system for emergency patient data according to claim 1 is characterized in that: The method of analyzing the received data set using a machine learning algorithm and medical knowledge rules to generate a standard information data set includes the following steps: S1. Data preprocessing: First, based on the reasonable range of medical data and common error patterns, duplicate records and erroneous data are removed, and missing values ​​are filled. Then, the raw data collected by the multidimensional acquisition module is converted into a structured data format; S2. Preliminary screening based on medical knowledge rules: preliminary screening of data based on established medical diagnostic standards and clinical guidelines, while using medical knowledge rules to identify potential risk factors in the data; S3, feature engineering, uses machine learning algorithms to extract valuable features from the data and selects the most meaningful features for subsequent analysis based on medical knowledge and machine learning algorithms; S4. Machine learning model analysis: using annotated medical datasets to train machine learning models, and applying the trained models to the data to be analyzed. At the same time, machine learning models are used to predict and analyze patient data to assist in medical diagnosis; S5. Generate a standard information data set, format the data that has undergone the above processing according to a unified medical data standard, and then integrate key patient information, the results of the analysis based on medical knowledge rules, and the conclusions of the machine learning model analysis to form a standard information data set.

3. The intelligent collection and analysis system for emergency patient data according to claim 2, characterized in that: The integration of medical records stored in all databases includes the following steps: S11, data collection, by extracting data from hospital information systems, laboratory information systems, imaging archiving and communication systems, and electronic medical record systems; S12. Data cleaning: First, remove duplicates, identify and eliminate duplicate records, check and correct errors in the data, and finally fill in missing information based on the context or using statistical methods; S13, standardization, converting medical terms used in different sources into systematic medical nomenclature - clinical terminology and ensuring that all data are stored in a predetermined format; S14, Coding, assign a unique identifier to each patient, each visit, and each diagnosis, and use; S15. Integration and verification: Integrate the cleaned and standardized data into the new coding library to ensure logical consistency and conflict-free data. Verify the accuracy and completeness of the data through sampling inspection or other means. S16, full and privacy protection, using advanced encryption standards to protect the security of sensitive information.

4. The intelligent collection and analysis system for emergency patient data according to claim 3 is characterized in that: The standard information data set generation unit is responsible for converting the original emergency data into a structured, standardized medical information set. The unit uses a machine learning algorithm to ensure that the generated information set has integrity, consistency and clinical decision support value.

5. The intelligent collection and analysis system for emergency patient data according to claim 4 is characterized in that: The independent coding library generation unit is responsible for converting the integrated medical records into a unified coding system, mapping heterogeneous medical data into standardized codes, establishing a mapping relationship of "raw data-medical concept-unique code", and realizing structured storage and rapid retrieval of data.

6. The intelligent collection and analysis system for emergency patient data according to claim 5, characterized in that: The multi-dimensional data of emergency patients include basic information of the patients, vital signs data, symptom description, past medical history and allergy history; The patient's key vital signs include body temperature, heart rate, blood pressure, respiratory rate, blood oxygen saturation and state of consciousness.

7. An intelligent collection and analysis method for emergency patient data, applicable to the intelligent collection and analysis system for emergency patient data according to any one of claims 1 to 6, characterized in that: The following steps are involved: S10, data collection, using the multi-dimensional acquisition module to collect multi-dimensional data of emergency patients and transmit it to the data analysis module; S20, data analysis, using a data analysis module to analyze and process the collected emergency patient data set to generate a standard information data set; S30, data integration, using the data integration module to integrate all medical records stored in the database, eliminate the differences between the data, and form a unified data set; S40, generating an independent coding library, using an independent coding library generating unit to analyze and encode the integrated data set to generate an independent coding library; S50, data matching and query, using the data management module to match the standard information data set with the independent coding library, and perform search and query in the independent coding library based on the search data set.

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