Human body case multifunctional stem cell treatment analysis system based on AI algorithm

Abstract

The invention relates to the field of medical information analysis and artificial intelligence, and discloses a human body case multifunctional stem cell treatment analysis system based on an AI algorithm, and the system comprises a data collection and integration module which is used for collecting, cleaning, standardizing and storing data information related to human body multifunctional stem cell treatment from multi-source heterogeneous data; the AI model construction and optimization module is used for performing feature extraction, model training, optimization and evaluation on the data to generate a prediction model; and the treatment effect prediction and decision-making auxiliary module is used for analyzing the treatment effect of the patient based on the prediction model. Through multi-source data integration, deep learning modeling and personalized treatment scheme recommendation technologies, the problems that in the prior art, data processing is dispersed, the variable relation is complex, modeling is difficult, personalized treatment lacks basis, and data transmission efficiency and safety are insufficient are solved, and accurate analysis and safety management of stem cell treatment are achieved.

Description

Technical Field

[0001] The present invention relates to the fields of medical information analysis and artificial intelligence, and specifically to a human case pluripotent stem cell therapy analysis system based on AI algorithms. Background Art

[0002] As a cutting-edge method in modern medicine, human pluripotent stem cell therapy has shown significant potential in the diagnosis and treatment of difficult cases. However, due to the strong individual differences among patients and the complex variables involved in stem cell therapy, the treatment effect and potential risks are difficult to predict. The current technical system still has many deficiencies in dealing with this complexity.

[0003] First, existing clinical data collection and integration technologies are relatively fragmented. Patient information is usually stored in multiple independent medical systems, such as hospital information systems (HIS), laboratory information management systems (LIMS), and picture archiving and communication systems (PACS). There is a lack of unified data interfaces between these systems, and the formats, standards, and sources of data are often inconsistent. This fragmented data situation requires a lot of manual intervention to organize and clean data before analysis. This is not only time-consuming, but also prone to errors, especially when the amount of data is large, the efficiency problem is particularly obvious.

[0004] Secondly, traditional model analysis methods are mostly based on statistics, which makes it difficult to capture the complex nonlinear relationships between multiple variables. Stem cell therapy involves multi-dimensional information such as the patient's medical history, immune status, disease severity, as well as biological characteristics such as stem cell source, preparation process, and differentiation potential. The interactions between these variables are dynamic and complex, and existing technologies rely more on univariate analysis or simple linear regression models. The model results often cannot fully reflect the actual situation, resulting in a lack of scientific basis for the formulation of treatment plans.

[0005] Furthermore, current stem cell treatment plans are mostly experience-based and lack personalization. Doctors usually develop plans based on limited clinical data or previous treatment experience, which ignores the patient's specific signs and disease characteristics. Some patients may face risks such as immune rejection and abnormal differentiation during stem cell treatment, but existing technologies lack effective evaluation tools and cannot accurately predict these problems before treatment. This may lead to higher uncertainty in the treatment process and even increase the probability of adverse events.

[0006] In addition, the low efficiency of data transmission and sharing between system modules is also a major pain point of existing technologies. In traditional systems, each module often runs independently, and data interaction usually relies on manual import and export, or is based on simple point-to-point transmission. This method cannot cope with complex application scenarios of multi-module collaboration, and is prone to delays or losses when the amount of data is large. In addition, the lack of effective security encryption and permission management mechanisms during data transmission may lead to the risk of patient privacy leakage or data tampering. For applications involving medical data, the shortcomings of existing technologies in this area are particularly prominent. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a human case multifunctional stem cell therapy analysis system based on AI algorithm, which solves the problems in the prior art such as difficulty in integrating multi-source data, insufficient ability to model complex relationships between multidimensional variables in stem cell therapy, lack of scientific basis for personalized treatment plans, and low data interaction efficiency and security.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A human case multifunctional stem cell treatment analysis system based on AI algorithm, comprising:

[0009] Data collection and integration module, used to collect, clean, standardize and store data information related to human pluripotent stem cell therapy from multi-source heterogeneous data;

[0010] AI model building and optimization module, which is used to extract features, train models, optimize and evaluate data to generate prediction models;

[0011] Treatment effect prediction and decision-making support module, which is used to analyze the treatment effect of patients based on the prediction model, generate personalized treatment plans, and provide risk assessment and early warning functions during the treatment process;

[0012] The data interaction module is used to realize the transmission and logical association of data between the above modules.

[0013] Preferably, the data collection and integration module includes:

[0014] Data collection unit, used to collect basic patient information, medical history records and clinical examination data from the hospital information system, collect stem cell sources, culture conditions and cell quality information from the laboratory information management system, collect image data from the image storage and transmission system, and obtain stem cell-related gene and protein data from external databases;

[0015] Data cleaning unit, used to remove outliers, duplicate values ​​and incomplete data, and fill in missing values;

[0016] The data standardization unit is used to normalize the collected numerical data, encode the classified data, and perform structured conversion on unstructured text data and image data.

[0017] Preferably, the AI ​​model building and optimization module includes:

[0018] Feature extraction unit, used to extract disease severity, immune status and other key features from patient data, extract cell purity, viability, differentiation potential and other features from stem cell data, and construct interactive features between patient immune status and stem cell immune regulation function;

[0019] A model training unit, used to model the feature data using a deep learning method, wherein the deep learning method includes a multi-layer perceptron model for numerical feature modeling, a convolutional neural network for image feature modeling, and fusing features output by different models;

[0020] The model optimization unit is used to optimize the model hyperparameters, use regularization methods to avoid overfitting, and evaluate and adjust based on the validation data set.

[0021] Preferably, the treatment effect prediction and decision support module includes:

[0022] Treatment effect prediction unit, which is used to predict treatment effectiveness and patient survival time through AI models based on patient characteristics and stem cell characteristics;

[0023] A personalized treatment plan recommendation unit, which is used to optimize treatment parameters, including stem cell dosage, infusion method, and treatment duration, based on the predicted results, and generate a patient-specific treatment plan;

[0024] The risk assessment and early warning unit is used to evaluate the risk of immune rejection, infection and abnormal differentiation during the treatment process based on the prediction results, and trigger early warning information when the risk exceeds the preset threshold.

[0025] Preferably, the feature extraction unit comprises:

[0026] The calculation module of the disease severity score calculates the disease severity by weighted synthesis of the patient's symptom score, the deviation of the examination index and the duration of the disease;

[0027] The interactive feature calculation module of stem cell immunoregulatory function and patient immune status quantifies the interaction between the two by calculating the matching value between the immune factors secreted by stem cells and the proportion of T cell subpopulations of patients.

[0028] Preferably, the model training unit uses a multi-layer perceptron to model numerical features, and the multi-layer perceptron includes an input layer, multiple hidden layers and an output layer, and each hidden layer uses an activation function to perform nonlinear mapping on the input signal; for image data, a convolutional neural network is used to extract spatial features, including a convolutional layer and a pooling layer, the convolutional layer is used to extract feature patterns in the image, and the pooling layer is used to reduce dimensionality to reduce computational complexity.

[0029] Preferably, the model optimization unit adjusts the model parameters through an optimization algorithm, and the optimization algorithm includes an Adam optimizer for dynamically adjusting the learning rate according to the gradient of the loss function and constraining the model weights through regularization technology, thereby improving the generalization ability of the model.

[0030] Preferably, the personalized treatment plan recommendation unit determines the optimal treatment plan through an optimization function, and the optimization function takes maximizing the treatment effect and minimizing the treatment risk as the goal, generates a treatment parameter combination that meets the conditions through a multi-objective optimization method, and dynamically adjusts the recommended plan based on the individual characteristics of the patient.

[0031] Preferably, the risk assessment and early warning unit quantitatively assesses the treatment risk by constructing a Bayesian network, calculates the probability of immune rejection based on patient characteristics and stem cell properties, generates risk early warning information when the risk probability value exceeds a set threshold, and provides corresponding risk mitigation suggestions.

[0032] Preferably, the data interaction module includes:

[0033] Data stream transmission unit, used to realize dynamic data transmission between modules;

[0034] Data log management unit, used to record the data processing and transmission process to ensure the traceability and transparency of data processing;

[0035] The data security management unit is used to encrypt data to ensure the security of data transmission and storage.

[0036] The present invention provides a human case multifunctional stem cell therapy analysis system based on AI algorithm. It has the following beneficial effects:

[0037] 1. The present invention achieves the technical effect of efficient integration and cleaning of multi-source heterogeneous data through a technical solution combining standardized processing and distributed storage with a data collection and integration module. Compared with the data processing method that relies on single-source data collection or manual cleaning in the prior art, it solves the shortcomings of single, incomplete, and difficult to adapt data sources to model input, and provides high-quality data input for subsequent AI modeling.

[0038] 2. The present invention achieves the technical effect of high-precision processing of multidimensional data of patients and stem cells and comprehensive prediction of treatment effects by constructing a multimodal deep learning model architecture. Compared with the technical solution of traditional statistical methods that can only simply associate a single variable, it solves the problem that complex interaction features between multiple variables cannot be effectively modeled, thereby significantly improving the reliability and scientificity of the prediction.

[0039] 3. The present invention can improve the effectiveness of treatment while controlling the risk level through the optimized personalized treatment plan recommendation and risk assessment technical solutions, achieving a comprehensive technical effect that takes into account both safety and effect. Compared with the technical solutions in the prior art that mainly use fixed treatment plans and lack risk assessment, this solves the technical defects of insufficient personalization and uncontrollable treatment risks.

[0040] 4. The present invention achieves the technical effect of efficient data transmission, unified format, and safe and reliable between modules by implementing the technical solution of distributed data transmission and security management through the data interaction module. Compared with the technical solution in the prior art where each module operates independently, data transmission efficiency is low and encryption measures are lacking, the problem of poor data flow and insufficient privacy protection between modules is solved, thereby improving the overall operating efficiency and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0042] Figure 2 It is a structural schematic diagram of the data acquisition and integration module of the present invention;

[0043] Figure 3 It is a structural schematic diagram of the data interaction module of the present invention;

[0044] Figure 4 This is a schematic diagram of the AI ​​model construction and optimization module structure of the present invention;

[0045] Figure 5 It is a schematic diagram of the structure of the treatment effect prediction and decision-making assistance module of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the specification 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.

[0047] Please see attached Figure 1-Figure 5The embodiment of the present invention provides a human case pluripotent stem cell treatment analysis system based on an AI algorithm, comprising:

[0048] Data collection and integration module

[0049] Data Source

[0050] The system collects patient and stem cell related information from multiple data sources to construct a multimodal dataset. The main sources include:

[0051] Hospital Information System (HIS): used to collect patients’ basic information (gender, age, weight, height), medical history (family medical history, past treatment records) and examination results (blood, biochemical indicators).

[0052] Laboratory Information Management System (LIMS): Obtain information on the source of stem cells (such as bone marrow, adipose tissue), culture process records (temperature, culture time) and cell quality parameters (activity, purity).

[0053] Picture Archiving and Communication System (PACS): Collects imaging data, such as MRI and CT images, for analyzing the location and progression of lesions.

[0054] External research database: Extract stem cell therapy-related gene information from authoritative medical databases (such as GenBank) to expand model knowledge.

[0055] Data processing and cleaning

[0056] After data collection is completed, the system will automatically perform cleaning operations. First, duplicate data is removed to avoid redundancy caused by multiple collections. Then outliers are identified and eliminated. For example, if the patient's age is entered as a negative value, the system will automatically mark it as incorrect data. For missing value processing, the system ensures data integrity through methods such as mean filling and regression prediction. Finally, all data will be converted to a unified format. For example, the weight unit is converted from pounds to kilograms, and categorical information (such as gender) is uniformly encoded in digital form.

[0057] Data Standardization

[0058] In order to adapt to the AI ​​model, all numerical data will be normalized and the data values ​​will be mapped to the [0,1] interval. Text data is parsed by natural language processing (NLP) algorithms to convert descriptive data into quantifiable key features. For example, "mild abnormal liver function" will be converted into a fixed weight score.

[0059] This module ensures the integrity and consistency of multi-source data, laying a solid data foundation for subsequent modeling. Compared with manual processing, it greatly improves efficiency and avoids human errors.

[0060] In this embodiment, the data collection and integration module includes the following technical implementations:

[0061] In general, the data sources of this module include but are not limited to hospital information systems (HIS), laboratory information management systems (LIMS), picture archiving and communication systems (PACS) and external open databases. These data sources cover basic patient information, disease characteristics, stem cell data, imaging examination results and biomedical research information.

[0062] As a possible implementation method, this module obtains basic patient information from HIS, such as gender, age, weight, height and other basic parameters, as well as medical history information, including previous diseases, family medical history and previous treatment records. At the same time, clinical examination data extracted from HIS, such as routine blood examination indicators (white blood cells, red blood cells, platelets, etc.), liver function and renal function biochemical indicators, will also be included in the processing scope.

[0063] In another possible implementation, the module obtains the source information of stem cells through LIMS, including their tissue type (e.g., bone marrow, adipose tissue, or umbilical cord blood), extraction method, culture conditions (culture time, temperature, and CO 2 concentration) and quality data of stem cells, such as cell purity, proportion of live cells and differentiation potential. Feature extraction of stem cell differentiation potential can include multiple indicators, such as differentiation efficiency, differentiation direction and expression levels of related markers.

[0064] The imaging data obtained from PACS generally include X-ray images, CT scans and MRI images of patients. These imaging data are read by the system through standardized interfaces (such as DICOM protocol) and then converted into a format that can adapt to the input of the AI ​​model.

[0065] In some embodiments, gene expression data, protein sequence information, and literature data related to stem cell therapy can also be obtained from external open databases (such as GenBank or UniProt). Specifically, these external data are parsed by natural language processing (NLP) technology to extract structured key features, such as the expression level of gene markers and mutation information related to the target disease.

[0066] In this embodiment, the data cleaning technology is implemented as follows:

[0067] Generally, the collected raw data may be duplicated, abnormal or missing. In order to ensure the data quality, this module sets up a variety of cleaning operations.

[0068] As an option, this module optimizes data integrity by removing duplicate data and outliers. For example, if the patient age is entered as a negative value or the proportion of live stem cells exceeds 100%, the system will automatically mark it as an abnormal data and remove it.

[0069] For missing data, this module can handle it through mean imputation, correlation-based prediction imputation, or regression interpolation. Specifically, if the patient's serum albumin data is missing, its value can be predicted using a regression model based on other biochemical indicators (such as total protein and liver enzyme activity).

[0070] In this embodiment, data standardization is achieved by:

[0071] In general, in order to adapt to the input requirements of the AI ​​model, all data needs to be converted to a unified format and processed numerically. The standardization process of this module includes normalizing numerical data, encoding categorical data, and structuring unstructured data.

[0072] Specifically, the formula for normalizing numerical data is:

[0073]

[0074] Among them, x i is the data to be normalized, min(X) and max(X) are the minimum and maximum values ​​in the data set, respectively. Through this normalization process, numerical features such as blood sugar values ​​and blood lipid values ​​can be mapped to the [0,1] interval to reduce the differences between data dimensions.

[0075] For categorical data, such as gender and disease type, one-hot encoding is used. For example, "male" is encoded as [1,0], and "female" is encoded as [0,1]. For disease classification information, it is numbered according to the International Classification of Diseases (ICD).

[0076] In some embodiments, this module also introduces natural language processing technology (NLP) to extract keywords and label unstructured text data such as medical history descriptions and doctor's diagnosis opinions. For example, for the text "the patient has mild liver function abnormalities", the system extracts "liver function abnormalities" and assigns a fixed weight score.

[0077] For image data processing, this module converts CT and MRI images into standardized grayscale value matrices through image preprocessing algorithms. Image data can also be downsampled and normalized to reduce computational complexity.

[0078] In this embodiment, data storage and security are implemented as follows:

[0079] In general, data storage is managed using a distributed file system (such as HDFS). Specifically, the system divides the cleaned and standardized data into structured and unstructured categories, which are stored in a relational database and a distributed file system respectively.

[0080] As an option, this module uses AES encryption technology to encrypt patient sensitive information. The formula is:

[0081]

[0082] Among them, C is ciphertext, P is plaintext, and K is the encryption key. Through this encryption algorithm, the security of patient data during storage and transmission is ensured.

[0083] The extended technology in this embodiment:

[0084] In some embodiments, in order to further improve the efficiency of the system, this module introduces a distributed data collection and processing architecture. For example, by implementing data stream processing through Kafka, data can be collected from multiple hospital systems at the same time and distributed directly to the data cleaning module.

[0085] In another possible implementation, this module supports a federated learning framework, which enables different medical institutions to collaborate on data modeling without sharing the original data. This extension enables the system to adapt to cross-regional case analysis needs.

[0086] Model building and optimization module

[0087] Feature extraction

[0088] From the cleaned data, the system extracts key features relevant to treatment.

[0089] Patient characteristics: including disease severity, immune status (such as T cell ratio, immunoglobulin level) and physical function (such as exercise capacity score).

[0090] Stem cell characteristics: extracted cell source, differentiation potential, purity and viability.

[0091] Interaction features: Construct interaction features between the patient's immune status and stem cell immunomodulatory factors to evaluate the therapeutic suitability of stem cells.

[0092] Model Architecture

[0093] The system uses deep learning technology and combines multiple models to achieve efficient prediction:

[0094] Numerical feature modeling: Based on multi-layer perceptron (MLP). The input layer receives the numerical features of patients and stem cells, and outputs the prediction results after nonlinear mapping of multiple hidden layers. The hidden layer uses ReLU activation function to improve the model's expressiveness.

[0095] Image feature modeling: Image data is processed by a convolutional neural network (CNN). The convolution layer extracts image feature patterns, and the pooling layer reduces the data dimension. Multiple convolution kernels extract key information from different regions, such as tumor size, lesion density, etc.

[0096] Feature fusion: The system fuses numerical features with image features to form comprehensive feature input.

[0097] Model training and optimization

[0098] During model training, the cross entropy loss function is used to calculate the classification error, and the mean square error loss function is used to calculate the regression error. The Adam optimizer is used to dynamically adjust the learning rate to ensure rapid model convergence. To avoid overfitting, the system introduces regularization constraints and monitors the validation set error through the early stopping method. Once the error stops decreasing, training is stopped immediately.

[0099] This module achieves comprehensive modeling of complex data and significantly improves prediction accuracy. In particular, through feature interaction construction and multimodal fusion, it effectively solves the problem of high-dimensional data that is difficult to handle with traditional methods.

[0100] In this embodiment, the feature engineering is implemented as follows:

[0101] Generally speaking, feature engineering is one of the key aspects of AI model performance. In this embodiment, feature engineering includes extracting key features from patient and stem cell data, and generating advanced features through interactive analysis.

[0102] Specifically, patient feature extraction includes disease severity score, immune status, and quantitative description of physical function. The disease severity score can be calculated by the following formula:

[0103] S dis =w 1 ·S sym +w 2 ·D check +w 3 ·T dur

[0104] Among them, S dis S is the severity of the disease, sym Denotes symptom score, D check Indicates the deviation of the inspection index, T dur represents the duration of the disease. 1 、w2 、w 3 It is the weight of each feature, usually set through data analysis or expert experience.

[0105] Alternatively, stem cell characterization may include cell purity, viability, and differentiation potential. pur The calculation formula is:

[0106]

[0107] L live It is expressed by the following formula:

[0108]

[0109] In some embodiments, differentiation potential can also be characterized by the multi-lineage differentiation efficiency of stem cells (eg, bone, cartilage, and fat directions).

[0110] In one possible implementation, the interaction between the patient and the stem cells is characterized by the immune matching index I match The calculation formula is:

[0111]

[0112] in, represents the level of the i-th immune factor secreted by stem cells, Represents the proportion or activity of the patient's type i immune cells. Through the above formula, the system can effectively evaluate the degree of compatibility between the patient's immune status and stem cell function.

[0113] In this embodiment, the design of the model architecture is:

[0114] In general, the construction of AI models requires the design of an adaptive network structure based on the type of input data. In this embodiment, the system uses a multimodal deep learning framework, including a multi-layer perceptron (MLP) model for numerical features and a convolutional neural network (CNN) for image data, and achieves unified output of the model through feature fusion.

[0115] Specifically, the multi-layer perceptron (MLP) structure is used to model numerical features. The input layer receives patient features and stem cell features, and the hidden layer expresses complex data relationships through nonlinear activation functions. The calculation formula of the hidden layer is:

[0116] h (l) =σ(W (l) ·h (l-1) +b (l) )

[0117] Among them, h (l)represents the output of the lth layer, W (l) and b (l) are weight matrix and bias vector respectively, and σ is activation function. In one possible implementation, the activation function uses ReLU (rectified linear unit), which is defined as:

[0118] σ(x)=max(0,x)

[0119] As an option, convolutional neural network (CNN) is used to process image data. Specifically, the convolution layer extracts spatial features through the convolution kernel, and the calculation formula is:

[0120] f conv =σ(W*X+b)

[0121] in,

[0122] * indicates the convolution operation,

[0123] X is the input image tensor,

[0124] W is the convolution kernel weight,

[0125] b is the bias term.

[0126] In one possible implementation, the system merges the outputs of MLP and CNN through a feature fusion module to generate comprehensive feature input for processing by the final fully connected layer.

[0127] In this embodiment, the model training and optimization are implemented as follows:

[0128] Generally, model training is completed by minimizing the loss function. In this embodiment, the classification problem uses the cross entropy loss function, and the calculation formula is:

[0129]

[0130] Among them, y i,j represents the true label of the i-th sample in the j-th category, Represents the probability value predicted by the model, N is the total number of samples, and K is the number of classification categories.

[0131] As an option, the regression problem uses the mean squared error (MSE) loss function:

[0132]

[0133] Among them, y i is the true value, is the model's predicted value.

[0134] In one possible implementation, the model optimization uses the Adam optimization algorithm, which combines the advantages of momentum and adaptive learning rate adjustment. Its weight update formula is:

[0135]

[0136] Among them, θ t is the current parameter, η is the learning rate, m t and v t are the first-order and second-order momentum estimates respectively, and ∈ is a smoothing term.

[0137] In order to prevent overfitting, regularization technology is introduced in this embodiment. In a possible implementation, regularization uses L2 norm constraint, and its loss term is:

[0138]

[0139] Among them, λ is the regularization strength, w j is the jth weight of the model.

[0140] Through the above technical implementation, the AI ​​model building and optimization module can efficiently process multimodal data and provide solid technical support for subsequent treatment effect prediction and decision-making assistance. Those skilled in the art can fully reproduce the technical solution of this module according to the above description and flexibly adjust the model structure to meet specific needs.

[0141] Treatment effect prediction and decision support module

[0142] Prediction of treatment effect

[0143] Based on the patient input, the model predicts the following outcomes:

[0144] Treatment effectiveness: Predict whether the current treatment plan is effective, and the output result is a probability value, such as a 70% effectiveness probability.

[0145] Survival time: Use a regression model to predict the patient's survival time after treatment, for example, 3.5 years.

[0146] The prediction results are explained by feature importance analysis tools (such as SHAP), which intuitively display the key features that affect the prediction results.

[0147] Personalized treatment plan recommendation

[0148] The system optimizes treatment parameters based on the predicted results:

[0149] If the stem cell dose is insufficient, the system will recommend increasing the dose.

[0150] If the patient is at risk for immune rejection, the system will recommend an immunosuppressive regimen.

[0151] The optimization process is based on a multi-objective optimization function, which ensures that the treatment effect is maximized while minimizing the risk.

[0152] Risk assessment and early warning

[0153] The system will conduct a comprehensive assessment of potential risks during treatment. The risk of immune rejection is calculated based on the patient's T cell status and the matching of the stem cell source. If the risk probability exceeds the threshold (such as 30%), the system will generate an early warning and make mitigation recommendations, such as adjusting the stem cell injection route or using immunomodulators in combination.

[0154] This module significantly improves the personalization and scientific nature of treatment plans, while reducing the probability of treatment failure and adverse reactions through comprehensive risk assessment. Doctors can obtain patients' treatment progress and risk warnings in real time, greatly improving decision-making efficiency.

[0155] Implementation Effects and Advantages

[0156] Through the above technical solutions, this system effectively solves the problem of complex data analysis and personalized program design in stem cell therapy. Its advantages are mainly reflected in the following aspects:

[0157] The data acquisition module can quickly integrate multi-source heterogeneous data and significantly improve the quality and availability of data.

[0158] The AI ​​model building module uses deep learning technology to mine complex relationships between data and achieve high-precision predictions.

[0159] The treatment decision support module can generate accurate treatment plans based on the individual characteristics of patients, while effectively evaluating and warning of potential risks.

[0160] The overall system is efficient and intelligent, which reduces the workload of doctors while improving the success rate and safety of patient treatment.

[0161] In this embodiment, the prediction of treatment effect is achieved by:

[0162] Generally speaking, treatment effect prediction includes classification prediction of treatment effectiveness and regression prediction of survival time. The model input is the characteristic data of patients and stem cells, and the output is quantitative prediction results.

[0163] As an option, classification prediction is implemented using a multi-class classification model, which outputs the probability distribution of treatment effectiveness. The calculation formula is:

[0164]

[0165] Among them, X is the input feature vector, W jis the weight of the jth category, and P(y=j|X) is the predicted probability that the sample belongs to category j. For example, when the stem cell dose is fixed, the model may predict a probability of 70% for “treatment effectiveness”.

[0166] Specifically, the prediction of survival time uses a regression model, whose output is a continuous value. The calculation of the predicted survival time value is based on the linear weighted summation of the input features of the model, and the formula is as follows:

[0167]

[0168] in, is the predicted survival time, W is the model weight, and b is the bias term. Under certain patient-specific conditions, such as patients with poor immune status or a longer course of disease, the model may predict a survival time of 3.8 years.

[0169] In one possible implementation, the system combines the classification and regression results and calculates the patient's treatment expectation through comprehensive weights. Assume that the weight W eff and w surv Used for classification results and regression results respectively, the comprehensive expected calculation formula is:

[0170]

[0171] in,

[0172] For the comprehensive evaluation results.

[0173] In this embodiment, the implementation of personalized treatment plan recommendation is:

[0174] In general, the system dynamically adjusts key parameters of stem cell therapy based on the predicted results, such as dosage, infusion frequency, treatment route, etc. This module relies on optimization algorithms to find the best balance between target effect and risk control.

[0175] Alternatively, the optimization process is implemented through a multi-objective optimization function, whose goal is to maximize the treatment effectiveness E eff and minimize treatment risk R risk . Its optimization objective function is defined as:

[0176] maxF(X,C)=E eff -λ·R risk

[0177] Among them, X is the patient characteristics, C is the stem cell parameter combination, and λ is the risk control coefficient.

[0178] Specifically, in some cases, the system will recommend increasing the stem cell dose to enhance the therapeutic effect, but if the model predicts that too high a dose will cause immune rejection, the system will adjust the dose to a safe range. For example, if the system assesses that the treatment effectiveness is 70% at the current dose and the risk probability is 40%, the system may recommend reducing the dose to reduce the risk.

[0179] In one possible implementation, the recommended treatment regimen includes the following elements:

[0180] Stem cell dose, for example 200 million viable cells per injection.

[0181] Infusion route, such as intravenous infusion or local injection.

[0182] Frequency and interval, for example, one infusion every two weeks for 6 times.

[0183] In some embodiments, the system provides adjustable treatment plans based on the patient's economic conditions, such as selecting a more economical culture method or reducing the frequency of treatment.

[0184] In this embodiment, the implementation of risk assessment and early warning is:

[0185] Generally, risk assessment includes quantitative analysis of immune rejection risk, abnormal differentiation risk, and infection risk. The system predicts the risk probability by constructing a Bayesian network, and the calculation formula is:

[0186]

[0187] Among them, H is the patient's immune status data, R is the risk event, and P(R|H) is the probability of R occurring under patient condition H.

[0188] As an option, the comprehensive risk assessment can include the weighting of multiple risk factors. The calculation formula is:

[0189] R total =w 1 ·R immune +w 2 ·R diff +w 3 ·R infect

[0190] Among them, R immune , R diff , R infect are immune rejection, abnormal differentiation and infection risk, respectively. 1 、w 2 、w 3 is the weight of the risk factor.

[0191] Specifically, when a risk probability exceeds a preset threshold (e.g. 30%), the system will trigger an early warning message. For example, if the probability of immune rejection of a patient under high-dose stem cell therapy is predicted to be 45%, the system will recommend adjusting the treatment plan and recommend the use of immunosuppressants.

[0192] In a possible implementation, the warning information includes the following:

[0193] The likelihood of a risk occurring, such as "the risk of abnormal differentiation is 35%."

[0194] Risk mitigation recommendations, such as “reduced dose and increased infusion intervals are recommended.”

[0195] Detailed treatment options, such as "using intravenous route instead of local injection".

[0196] 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. A human case multifunctional stem cell therapy analysis system based on AI algorithm, characterized in that: include: Data collection and integration module, used to collect, clean, standardize and store data information related to human pluripotent stem cell therapy from multi-source heterogeneous data; AI model building and optimization module, which is used to extract features, train models, optimize and evaluate data to generate prediction models; Treatment effect prediction and decision-making support module, which is used to analyze the treatment effect of patients based on the prediction model, generate personalized treatment plans, and provide risk assessment and early warning functions during the treatment process; The data interaction module is used to realize the transmission and logical association of data between the above modules.

2. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 1, characterized in that: The data collection and integration module includes: Data collection unit, used to collect basic patient information, medical history records and clinical examination data from the hospital information system, collect stem cell sources, culture conditions and cell quality information from the laboratory information management system, collect image data from the image storage and transmission system, and obtain stem cell-related gene and protein data from external databases; Data cleaning unit, used to remove outliers, duplicate values ​​and incomplete data, and fill in missing values; The data standardization unit is used to normalize the collected numerical data, encode the classified data, and perform structured conversion on unstructured text data and image data.

3. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 1, characterized in that: The AI ​​model building and optimization module includes: Feature extraction unit, used to extract disease severity, immune status and other key features from patient data, extract cell purity, viability, differentiation potential and other features from stem cell data, and construct interactive features between patient immune status and stem cell immune regulation function; A model training unit, used to model the feature data using a deep learning method, wherein the deep learning method includes a multi-layer perceptron model for numerical feature modeling, a convolutional neural network for image feature modeling, and fusing features output by different models; The model optimization unit is used to optimize the model hyperparameters, use regularization methods to avoid overfitting, and evaluate and adjust based on the validation data set.

4. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 1, characterized in that: The treatment effect prediction and decision-making assistance module includes: Treatment effect prediction unit, which is used to predict treatment effectiveness and patient survival time through AI models based on patient characteristics and stem cell characteristics; A personalized treatment plan recommendation unit, which is used to optimize treatment parameters, including stem cell dosage, infusion method, and treatment duration, based on the predicted results, and generate a patient-specific treatment plan; The risk assessment and early warning unit is used to evaluate the risk of immune rejection, infection and abnormal differentiation during the treatment process based on the prediction results, and trigger early warning information when the risk exceeds the preset threshold.

5. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 3, characterized in that: The feature extraction unit comprises: The calculation module of the disease severity score calculates the disease severity by weighted synthesis of the patient's symptom score, the deviation of the examination index and the duration of the disease; The interactive feature calculation module of stem cell immunoregulatory function and patient immune status quantifies the interaction between the two by calculating the matching value between the immune factors secreted by stem cells and the proportion of T cell subpopulations of patients.

6. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 3, characterized in that: The model training unit uses a multi-layer perceptron to model numerical features, and the multi-layer perceptron includes an input layer, multiple hidden layers and an output layer. Each hidden layer uses an activation function to perform nonlinear mapping on the input signal. For image data, a convolutional neural network is used to extract spatial features, including a convolutional layer and a pooling layer. The convolutional layer is used to extract feature patterns in the image, and the pooling layer is used to reduce dimensionality to reduce computational complexity.

7. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 3, characterized in that: The model optimization unit adjusts the model parameters through an optimization algorithm, wherein the optimization algorithm includes an Adam optimizer for dynamically adjusting the learning rate according to the gradient of the loss function and constraining the model weights through regularization technology, thereby improving the generalization ability of the model.

8. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 4, characterized in that: The personalized treatment plan recommendation unit determines the optimal treatment plan through an optimization function. The optimization function aims to maximize the treatment effect and minimize the treatment risk. It generates a treatment parameter combination that meets the conditions through a multi-objective optimization method and dynamically adjusts the recommended plan based on the individual characteristics of the patient.

9. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 4, characterized in that: The risk assessment and early warning unit quantitatively assesses the treatment risk by constructing a Bayesian network, calculates the probability of immune rejection based on patient characteristics and stem cell properties, generates risk early warning information when the risk probability value exceeds a set threshold, and provides corresponding risk mitigation suggestions.

10. The human case pluripotent stem cell treatment analysis system based on AI algorithm according to claim 1, characterized in that: The data interaction module includes: Data stream transmission unit, used to realize dynamic data transmission between modules; Data log management unit, used to record the data processing and transmission process to ensure the traceability and transparency of data processing; The data security management unit is used to encrypt data to ensure the security of data transmission and storage.

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