Auxiliary system for medical data analysis based on artificial intelligence

Abstract

The invention discloses an auxiliary system for medical data analysis based on artificial intelligence, and relates to the technical field of medical data. Comprising a data input module, a data processing module, an artificial intelligence analysis module, a model training and optimizing module and an output module. And the data processing module is responsible for carrying out data preprocessing and eliminating error, inconsistent and invalid data in input data. Various medical records and examination data of a patient are comprehensively collected, data preprocessing and feature extraction are carried out in a scientific and reasonable mode, an algorithm model is trained and optimized on the basis of a large number of historical cases, the system can effectively recognize modes and trends of various diseases, the system can accurately analyze information of the current patient, and the accuracy of the patient is improved. The method can predict the potential development of diseases, help clinicians to take preventive measures in advance, support better disease diagnosis and treatment decisions, improve the overall level of medical services of patients, and improve the efficiency and accuracy of medical data analysis.

Description

Technical Field

[0001] The present invention relates to the field of medical data technology, and specifically to an auxiliary system for medical data analysis based on artificial intelligence. Background Art

[0002] In the traditional medical environment, doctors need to rely on professional knowledge and experience to analyze patients' symptoms and signs, which requires a lot of time and energy. In addition, the collection and recording of medical data have become more and more perfect in recent years, and the amount and type of data for each patient have become very large. When diagnosing a disease, manually screening and analyzing this data is a huge challenge. Therefore, there is an urgent need for a tool that can help doctors analyze medical data efficiently. The existing medical management systems or analysis tools mostly rely on manual processing of patients' electronic health records, test results, and large amounts of medical data. The processing speed is slow, and it is difficult to quickly and accurately identify key medical events or potential health risks.

[0003] How to effectively use this data has become an important challenge facing doctors and medical institutions. Traditional medical data processing methods are inefficient, and the accuracy of the data is limited by errors or deviations in the data collection and recording process, which cannot meet the needs of modern medical care. Therefore, a system that can improve the efficiency and accuracy of medical data analysis is needed to help doctors make more accurate diagnosis and treatment decisions. To this end, we proposed an auxiliary system for medical data analysis based on artificial intelligence to solve the above problems. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an auxiliary system for medical data analysis based on artificial intelligence to solve the problems raised in the above-mentioned background technology.

[0005] In order to achieve the above-mentioned purpose of automatically processing a large amount of medical data and generating high-quality diagnosis results, reducing the time required for doctors to manually analyze data and improving work efficiency, the present invention provides the following technical solutions: an auxiliary system for medical data analysis based on artificial intelligence, including a data input module, a data processing module, an artificial intelligence analysis module, a model training and optimization module and an output module;

[0006] The data processing module is responsible for data preprocessing to eliminate errors, inconsistencies and invalid data in the input data;

[0007] The artificial intelligence analysis module inputs the processed data into the intelligent algorithm to realize feature extraction and pattern recognition of the data, extract useful features, and reflect the development process of the disease and the clinical manifestations of the patient;

[0008] The model training and optimization module uses a machine learning algorithm to continuously optimize the analysis model, uses a machine learning algorithm to build a model, and trains the model through existing cases so that it can learn and understand the relationship between the symptoms and treatment methods of different diseases, so that it can understand the characteristic information in the data.

[0009] To further optimize the technical solution, the data input module is responsible for collecting various medical data of the patient, such as electronic medical records, radiological images, and collecting comprehensive medical information of the patient from different sources of medical records, examination results, and physiological indicators.

[0010] To further optimize the technical solution, the data processing module eliminates errors and inconsistencies in the input data, as well as invalid data, and uses data mining technology to identify and extract data that can reflect disease characteristics.

[0011] To further optimize the technical solution, the output module will present the analysis results to the doctor, provide suggestions for auxiliary decision-making, summarize the analysis results, and generate a customized report.

[0012] An auxiliary system for medical data analysis based on artificial intelligence, characterized by comprising the following steps:

[0013] S1. Establishment of user interface

[0014] Establish a user interface where users need to import various medical data including medical records, examination reports and imaging materials into the system. Doctors or other medical professionals can input relevant data of patients through the system, and the system receives and processes the medical data of patients for review;

[0015] S2. Data processing and feature extraction

[0016] The system will automatically process and extract features from the input data, using NLP technology to parse and understand the unstructured text of the patient’s description or the doctor’s record of the medical history and symptoms;

[0017] S3. Modeling and analysis

[0018] Find the appropriate algorithm model for historical data training. The system is trained through a deep neural network model to build a data model that can perform semantic analysis and classification based on medical expertise.

[0019] S4. Analysis Report

[0020] The prediction results are output through the convolutional neural network CNN and the recurrent neural network RNN ​​in step S3, and a corresponding analysis report is formed, and finally the analysis results are output to assist doctors in diagnosis and treatment, and finally the analysis results are generated for reference by medical staff;

[0021] S5. Result display module

[0022] The analysis and prediction results are presented to system users in an intuitive manner.

[0023] To further optimize the technical solution, in step S3, based on artificial intelligence algorithms, key information can be identified and extracted from complex data, and the system organically combines data processing algorithms, medical knowledge and artificial intelligence expert systems; in step S5, users can access the medical data analysis auxiliary system through the Internet or a dedicated terminal to input the medical data to be analyzed.

[0024] To further optimize the technical solution, in step S2, NLP technology is used to analyze the text, identify keywords, entities and semantic information, and identify important data such as medical history, chief complaint, diagnosis, examination results and treatment; the NLP engine and algorithm library need to be used together;

[0025] NLP engine: responsible for text preprocessing and semantic analysis;

[0026] Algorithm library: includes various NLP algorithms for text processing at different levels.

[0027] To further optimize the technical solution, the convolutional neural network and the recurrent neural network in step S5 can better handle the complexity of image and text data, improve the accuracy of diagnosis, and cooperate with the Gaussian mixture model for analysis and processing. Suppose the random variable X, the calculation formula is:

[0028]

[0029] Where: k represents that the distribution consists of K Gaussian components;

[0030] ω k That is, each component N(x1μ k ,∑ k )’s weight;

[0031] μ k is the mean vector of the kth Gaussian component; ∑ k is the covariance matrix of the Kth Gaussian component;

[0032] N(x1μ k ,∑ k ) is the multivariate normal density function of the kth component.

[0033] To further optimize this technical solution, the steps of using the NLP engine to preprocess and parse the text are as follows:

[0034] 1) Text reception: First, the patient's medical record text is entered into the system;

[0035] 2) Text preprocessing: perform word segmentation and stop word removal on the input text;

[0036] 3) Semantic analysis: Use pre-trained or customized semantic analysis models to parse text content and identify and annotate key information in medical records;

[0037] 4) Result output: extract and present the key information after analysis, including but not limited to patient history, symptom description, and diagnostic recommendations;

[0038] 5) Scoring of relevance results: Achieve efficient and accurate document similarity calculation to improve the relevance of search results.

[0039] To further optimize the technical solution, the relevance score multiplies the term frequency TF by the inverse document frequency IDF to obtain the TF-IDF value of the current term, and combines the TF-IDF method to evaluate the relevance of the query to the information in the database. The calculation formula is: TF-IDF(t, d)TF(t, d)IDF(t);

[0040] Where TF(t, d) is the frequency of word t in document d, IDF(t) is the inverse document frequency, calculated as N is the total number of documents, and nt is the number of documents containing word t.

[0041] Beneficial Effects

[0042] Compared with the prior art, the present invention provides an auxiliary system for medical data analysis based on artificial intelligence, which has the following beneficial effects:

[0043] 1. This AI-based auxiliary system for medical data analysis uses trained models to automatically parse, extract key information and perform computational analysis, and then generates easy-to-understand reports to help doctors diagnose conditions, adjust treatment plans or make other medical decisions faster and more accurately. It uses advanced data analysis technology to process and analyze various types of medical data, thereby providing accurate and timely medical diagnosis and treatment recommendations, improving the efficiency and quality of medical services, and can quickly and accurately extract useful information from a variety of complex data, supporting doctors to diagnose and treat more effectively, and improving data accuracy through deep learning and artificial intelligence technology.

[0044] 2. This artificial intelligence-based auxiliary system for medical data analysis can effectively identify the patterns and trends of various diseases by comprehensively collecting various medical records and examination data of patients, adopting scientific and reasonable methods for data preprocessing and feature extraction, and training and optimizing algorithm models based on a large number of historical cases. The system can not only accurately analyze the current patient's information, but also predict the potential development of their disease, help clinicians take preventive measures in advance, and support better disease diagnosis and treatment decisions, thereby improving the overall level of patient medical services and improving the efficiency and accuracy of medical data analysis.

[0045] 3. This auxiliary system for medical data analysis based on artificial intelligence, convolutional neural network (CNN) is often used for image data analysis, such as X-rays, CT scans, and fundus images; recurrent neural network (RNN): often used for sequence data analysis, such as patient electronic medical records and medical reports; by building and training appropriate NLP models, unstructured medical texts are effectively parsed, and medical record data can be quickly and accurately processed, giving full play to the value of hidden information in text data to medical services. In addition, the analysis results are presented to users in a structured or visual form, providing doctors with intuitive decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of an auxiliary system for medical data analysis based on artificial intelligence proposed by the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1: Please refer to Figure 1 As shown, an auxiliary system for medical data analysis based on artificial intelligence includes a data input module, a data processing module, an artificial intelligence analysis module, a model training and optimization module and an output module;

[0049] The data processing module is responsible for data preprocessing to eliminate errors, inconsistencies and invalid data in the input data;

[0050] The artificial intelligence analysis module inputs the processed data into the intelligent algorithm to realize feature extraction and pattern recognition of the data, extract useful features, and reflect the development process of the disease and the clinical manifestations of the patient;

[0051] The model training and optimization module uses machine learning algorithms to continuously optimize the analysis model, uses machine learning algorithms to build models, and trains the model through existing cases so that it can learn and understand the relationship between the symptoms and treatment methods of different diseases, so that it can understand the characteristic information in the data;

[0052] The data input module is responsible for collecting various medical data of patients, such as electronic medical records, radiological images, and collecting comprehensive medical information of patients from different sources of medical records, examination results, and physiological indicators;

[0053] The data processing module eliminates errors and inconsistencies in the input data, as well as invalid data, and uses data mining technology to identify and extract data that can reflect disease characteristics;

[0054] The output module will present the analysis results to the doctor, provide suggestions for auxiliary decision-making, summarize the analysis results, and generate a customized report.

[0055] An auxiliary system for medical data analysis based on artificial intelligence, comprising the following steps:

[0056] S1. Establishment of user interface

[0057] Establish a user interface where users need to import various medical data including medical records, examination reports and imaging materials into the system. Doctors or other medical professionals can input relevant data of patients through the system, and the system receives and processes the medical data of patients for review;

[0058] S2. Data processing and feature extraction

[0059] The system will automatically process and extract features from the input data, and use NLP technology to parse and understand the unstructured text of the patient's description or the doctor's record of the medical history and symptoms. In step S2, the text is analyzed using NLP technology to identify keywords, entities and semantic information, and to identify important data on the medical history, chief complaint, diagnosis, examination results and treatment; the NLP engine and algorithm library need to be used together;

[0060] NLP engine: responsible for text preprocessing and semantic analysis;

[0061] Algorithm library: includes various NLP algorithms for text processing at different levels;

[0062] The steps of using the NLP engine to preprocess and parse text are as follows:

[0063] 1) Text reception: First, the patient's medical record text is entered into the system;

[0064] 2) Text preprocessing: perform word segmentation and stop word removal on the input text;

[0065] 3) Semantic analysis: Use pre-trained or custom semantic analysis models to parse text content and identify and annotate key information in medical records;

[0066] 4) Result output: extract and present the key information after analysis, including but not limited to patient history, symptom description, and diagnostic recommendations;

[0067] 5) Scoring of relevance results: Achieve efficient and accurate document similarity calculation to improve the relevance of search results.

[0068] The relevance score multiplies the term frequency TF by the inverse document frequency IDF to obtain the TF-IDF value of the current term, and combines the TF-IDF method to evaluate the relevance of the query and the information in the database. The calculation formula is: TF-IDF(t, d)TF(t, d)IDF(t);

[0069] Where TF(t, d) is the frequency of word t in document d, IDF(t) is the inverse document frequency, calculated as N is the total number of documents, and nt is the number of documents containing word t.

[0070] S3. Modeling and analysis

[0071] Find the appropriate algorithm model for historical data training. The system is trained through a deep neural network model to establish a data model, which can perform semantic analysis and classification based on medical expertise. In step S3, based on artificial intelligence algorithms, key information can be identified and extracted from complex data. The system organically combines data processing algorithms, medical knowledge and artificial intelligence expert systems.

[0072] S4. Analysis Report

[0073] The prediction results are output through the convolutional neural network CNN and the recurrent neural network RNN ​​in step S3, and a corresponding analysis report is formed, and finally the analysis results are output to assist doctors in diagnosis and treatment, and finally the analysis results are generated for reference by medical staff;

[0074] S5. Result display module

[0075] The results of analysis and prediction are presented to the system user in an intuitive manner. In step S5, the user can access the medical data analysis auxiliary system through the Internet or a dedicated terminal and input the medical data to be analyzed. The convolutional neural network and recurrent neural network in step S5 can better handle the complexity of image and text data and improve the accuracy of diagnosis. The analysis and processing are carried out in conjunction with the Gaussian mixture model. Suppose the random variable X is calculated as follows:

[0076]

[0077] Where: k represents that the distribution consists of K Gaussian components;

[0078] ω k That is, each component N(x1μ k ,Σ k )’s weight;

[0079] μ k is the mean vector of the kth Gaussian component; Σ k is the covariance matrix of the Kth Gaussian component;

[0080] N(x1μ k ,Σ k ) is the multivariate normal density function of the kth component.

[0081] The beneficial effects of the present invention are as follows: the auxiliary system for medical data analysis based on artificial intelligence uses a trained model to automatically parse, extract key information and perform computational analysis, and then generates easy-to-understand reports to help doctors diagnose conditions faster and more accurately, adjust treatment plans or make other medical decisions. It uses advanced data analysis technology to process and analyze various types of medical data, thereby providing accurate and timely medical diagnosis and treatment recommendations, improving the efficiency and quality of medical services, and can quickly and accurately extract useful information from a variety of complex data, supporting doctors to diagnose and treat more effectively, and improving data accuracy through deep learning and artificial intelligence technology.

[0082] This artificial intelligence-based auxiliary system for medical data analysis can effectively identify the patterns and trends of various diseases by comprehensively collecting various medical records and examination data of patients, adopting scientific and reasonable methods for data preprocessing and feature extraction, and training and optimizing algorithm models based on a large number of historical cases. The system can not only accurately analyze the current patient's information, but also predict the potential development of their disease, help clinicians take preventive measures in advance, and support better disease diagnosis and treatment decisions, thereby improving the overall level of patient medical services and improving the efficiency and accuracy of medical data analysis.

[0083] This auxiliary system for medical data analysis based on artificial intelligence, convolutional neural networks (CNN) are often used for image data analysis, such as X-rays, CT scans, and fundus images; recurrent neural networks (RNN): often used for sequence data analysis, such as patient electronic medical records and medical reports; by building and training appropriate NLP models, unstructured medical texts are effectively parsed, medical record data is processed quickly and accurately, and the value of hidden information in text data to medical services is fully utilized. In addition, the parsing results are presented to users in a structured or visual form, providing doctors with intuitive decision support.

[0084] In the description of this specification, the description with reference to the term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An auxiliary system for medical data analysis based on artificial intelligence, characterized in that: It includes data input module, data processing module, artificial intelligence analysis module, model training and optimization module and output module; The data processing module is responsible for data preprocessing to eliminate errors, inconsistencies and invalid data in the input data; The artificial intelligence analysis module inputs the processed data into the intelligent algorithm to realize feature extraction and pattern recognition of the data, extract useful features, and reflect the development process of the disease and the clinical manifestations of the patient; The model training and optimization module uses a machine learning algorithm to continuously optimize the analysis model, uses a machine learning algorithm to build a model, and trains the model through existing cases so that it can learn and understand the relationship between the symptoms and treatment methods of different diseases, so that it can understand the characteristic information in the data.

2. The artificial intelligence-based medical data analysis auxiliary system according to claim 1, characterized in that: The data input module is responsible for collecting various medical data of patients, such as electronic medical records, radiological images, and collecting comprehensive medical information of patients from different sources of medical records, examination results, and physiological indicators.

3. The auxiliary system for medical data analysis based on artificial intelligence according to claim 1 is characterized in that: The data processing module eliminates errors and inconsistencies in the input data, as well as invalid data, and uses data mining technology to identify and extract data that can reflect disease characteristics.

4. The artificial intelligence-based medical data analysis auxiliary system according to claim 1, characterized in that: The output module will present the analysis results to the doctor, provide suggestions for auxiliary decision-making, summarize the analysis results, and generate a customized report.

5. The artificial intelligence-based medical data analysis auxiliary system according to claim 1 is characterized in that: The following steps are involved: S1. Establishment of user interface Establish a user interface where users need to import various medical data including medical records, examination reports and imaging materials into the system. Doctors or other medical professionals can input relevant data of patients through the system, and the system receives and processes the medical data of patients for review; S2. Data processing and feature extraction The system will automatically process and extract features from the input data, using NLP technology to parse and understand the unstructured text of the patient’s description or the doctor’s record of the medical history and symptoms; S3. Modeling and analysis Find the appropriate algorithm model for historical data training. The system is trained through a deep neural network model to establish a data model that can perform semantic analysis and classification based on medical expertise. S4. Analysis Report The prediction results are output through the convolutional neural network CNN and the recurrent neural network RNN ​​in step S3, and a corresponding analysis report is formed, and finally the analysis results are output to assist doctors in diagnosis and treatment, and finally the analysis results are generated for reference by medical staff; S5. Result display module The analysis and prediction results are presented to system users in an intuitive manner.

6. The artificial intelligence-based medical data analysis auxiliary system according to claim 5, characterized in that: In step S3, based on artificial intelligence algorithms, key information can be identified and extracted from complex data. The system organically combines data processing algorithms, medical knowledge and artificial intelligence expert systems; In step S5, the user can access the medical data analysis auxiliary system through the Internet or a dedicated terminal and input the medical data to be analyzed.

7. The artificial intelligence-based medical data analysis auxiliary system according to claim 5, characterized in that: In step S2, NLP technology is used to analyze the text, identify keywords, entities and semantic information, and identify important data such as medical history, chief complaint, diagnosis, examination results and treatment; the NLP engine and algorithm library need to be used together; NLP engine: responsible for text preprocessing and semantic analysis; Algorithm library: includes various NLP algorithms for text processing at different levels.

8. The artificial intelligence-based medical data analysis auxiliary system according to claim 5, characterized in that: The convolutional neural network and recurrent neural network in step S5 can better handle the complexity of image and text data, improve the accuracy of diagnosis, and cooperate with the Gaussian mixture model for analysis and processing. Suppose the random variable X, the calculation formula is: Where: k represents that the distribution consists of K Gaussian components; ω k That is, each component N(x1μ k , ∑k) weights; μ k is the mean vector of the kth Gaussian component; ∑k is the covariance matrix of the Kth Gaussian component; N(x1μ k ,∑k) is the multivariate normal density function of the kth component.

9. The artificial intelligence-based medical data analysis auxiliary system according to claim 7, characterized in that: The steps of using the NLP engine to preprocess and parse text are as follows: 1) Text reception: First, the patient's medical record text is entered into the system; 2) Text preprocessing: perform word segmentation and stop word removal on the input text; 3) Semantic analysis: Use pre-trained or customized semantic analysis models to parse text content and identify and annotate key information in medical records; 4) Result output: extract and present the key information after analysis, including but not limited to patient history, symptom description, and diagnostic recommendations; 5) Scoring of relevance results: Achieve efficient and accurate document similarity calculation to improve the relevance of search results.

10. The artificial intelligence-based medical data analysis auxiliary system according to claim 9, characterized in that: The relevance score multiplies the term frequency TF by the inverse document frequency IDF to obtain the TF-IDF value of the current term, and combines the TF-IDF method to evaluate the relevance of the query and the information in the database. The calculation formula is: TF-IDF(t, d)TF(t, d)IDF(t); Where TF(t, d) is the frequency of word t in document d, IDF(t) is the inverse document frequency, calculated as N is the total number of documents, and nt is the number of documents containing word t.

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