CN112530584A - Medical diagnosis assisting method and system - Google Patents
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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Abstract
The invention provides a medical diagnosis auxiliary method, which belongs to the technical field of computer assistance and comprises the following steps: classifying by adopting various classification models based on various medical data to obtain a plurality of classification decision values; and performing decision fusion on the plurality of classification decision values to obtain one classification decision value which is used as a classification result to be output. The invention also provides a medical diagnosis auxiliary system. According to the invention, through the mode of respectively operating and finally performing decision fusion on various medical data and various classification models, the problems of incomplete data source, single model and the like can be solved to a great extent, and the overall accuracy of the universal medical diagnosis auxiliary system is effectively improved from the technical framework level.
Description
Technical Field
The invention relates to a medical diagnosis assisting method and system, and belongs to the technical field of computer assistance.
Background
With the development of the related art of artificial intelligence, a great number of systems for assisting medical diagnosis based on the artificial intelligence technology appear in the prior art, and for example, the invention patent with the application number of CN202010592658.1 discloses a medical data processing method, device, equipment and storage medium.
However, the artificial intelligence correlation technique needs to adopt different solutions for specific application scenarios, and especially should consider the actual situation of data in the specific application scenarios.
Based on this principle, the inventors of the present application found that: in the aspect of medical diagnosis, the prior art does not consider the actual conditions of data such as classification and features, and typically, the data actually used in medical diagnosis behaviors include not only medical images and medical history texts, but also medical examination indexes, and the like.
Disclosure of Invention
In order to solve the technical problems, the invention provides a medical diagnosis auxiliary method, which can solve the problems of incomplete data source, single model and the like to a great extent by respectively operating various medical data and various classification models and finally performing decision fusion, and effectively improve the overall accuracy of a universal medical diagnosis auxiliary system from the technical framework level.
The invention is realized by the following technical scheme.
The invention provides a medical diagnosis assisting method, which comprises the following steps:
classifying by adopting various classification models based on various medical data to obtain a plurality of classification decision values;
and performing decision fusion on the plurality of classification decision values to obtain one classification decision value which is used as a classification result to be output.
The medical data at least comprises demographic characteristics, medical examination indexes, medical images and medical record texts.
And the decision fusion is to select a classification decision value by adopting a voting method or a weight method.
The voting method comprises the following steps:
initialization: establishing a statistical result list and setting the value to zero;
counting the classified tickets: counting and counting the classification decision values and recording the counting values into a counting result list;
sorting the statistical values: reversely ordering the statistical result list;
selecting a maximum value: and randomly selecting one item in the maximum item in the statistical result list as a result.
The weight method comprises the following steps:
initializing the weight matrix: respectively taking the total number of the decision values and the number of the classification categories as the number of rows and the number of columns, establishing a weight matrix and filling preset weight parameters;
converting a decision matrix: aligning the classified decision values according to classification categories, converting and splicing the classified decision values into a decision matrix;
matrix multiplication: multiplying the weight matrix and the decision matrix item by item to obtain a result matrix;
and (3) summing the weights: summing the result matrix according to classification and category to obtain a summary value;
and returning a result: and selecting the classification category with the largest summary value as a result.
And respectively preprocessing the plurality of medical data.
And preprocessing the demographic characteristics by adopting a model constructed based on an XGboost algorithm.
The XGboost algorithm comprises more than ten parameters, the parameters are selected and optimized by adopting a network search algorithm, and ten-fold cross validation is adopted when a model is constructed.
The sum of the weight parameters is 1.
Based on the same inventive concept, the invention also provides a medical diagnosis auxiliary system, which comprises at least one processor; and
a memory coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to implement the method of any one of the above.
The invention has the beneficial effects that: through the mode of respectively operating and finally performing decision fusion on various medical data and various classification models, the problems of incomplete data source, single model and the like can be solved to a great extent, and the overall accuracy of the universal medical diagnosis auxiliary system is effectively improved from the technical framework level.
Drawings
FIG. 1 is a schematic system flow diagram of one embodiment of the present invention;
FIG. 2 is a schematic system flow diagram of another embodiment of the present invention;
FIG. 3 is a schematic diagram of a decision fusion process according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a decision fusion process according to another embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described below, but the scope of the claimed invention is not limited to the described.
A medical diagnosis assistance method shown in fig. 1 and 2 includes the following steps:
classifying by adopting various classification models based on various medical data to obtain a plurality of classification decision values;
and performing decision fusion on the plurality of classification decision values to obtain one classification decision value which is used as a classification result to be output.
Therefore, the medical data classification method is classified based on various medical data to solve the problem of incomplete data sources; and classifying by adopting various classification models to solve the problem of single model, and finally realizing the unification of multi-classification results by a decision fusion mode to finish diagnosis.
Specifically, the medical data at least includes demographic characteristics, medical examination indexes, medical images, and medical history texts.
Further, as a preferred scheme of decision fusion, decision fusion is to select a classification decision value by adopting a voting method or a weight method.
Wherein:
as shown in fig. 3, the voting method includes the following steps:
initialization: establishing a statistical result list and setting the value to zero;
counting the classified tickets: counting and counting the classification decision values and recording the counting values into a counting result list;
sorting the statistical values: reversely ordering the statistical result list;
selecting a maximum value: and randomly selecting one item in the maximum item in the statistical result list as a result.
It can be seen that the voting method has the main advantages of convenient use, and is mainly suitable for the condition that the auxiliary examination can provide a decisive basis for the diagnosis of diseases, and the information provided by various clinical data is equally important for obtaining a diagnosis conclusion. ② as shown in FIG. 4, the weighting method includes the following steps:
initializing the weight matrix: respectively taking the total number of the decision values and the number of the classification categories as the number of rows and the number of columns, establishing a weight matrix and filling preset weight parameters, wherein the total sum of the weight parameters is 1;
converting a decision matrix: aligning the classified decision values according to classification categories, converting and splicing the classified decision values into a decision matrix;
matrix multiplication: multiplying the weight matrix and the decision matrix item by item to obtain a result matrix;
and (3) summing the weights: summing the result matrix according to classification and category to obtain a summary value;
and returning a result: and selecting the classification category with the largest summary value as a result.
It can be seen that the weight method is mainly suitable for the condition that information provided by various clinical data has different weights for obtaining a diagnosis conclusion, the use threshold is relatively high, and the preset weight parameters in the weight matrix need to be manually set according to the diagnosis rule or expert experience.
Respectively preprocessing a plurality of medical data:
for processing (and classifying) medical images and medical record texts, the prior art provides a large number of schemes, and reference processing is only needed. Generally, medical examination indexes and demographic characteristics are processed and classified in a similar mode, and preferably, the demographic characteristics are preprocessed by a model constructed based on an XGboost algorithm. Specifically, the XGboost algorithm comprises more than ten parameters, the parameters are selected and optimized by adopting a network search algorithm, and ten-fold cross validation is adopted when the model is constructed.
Based on the same inventive concept, the invention also provides a medical diagnosis auxiliary system, which comprises at least one processor; and
a memory coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to implement the above-described method.
Example 1
By adopting the scheme, as shown in fig. 1 and 3, various medical data are preprocessed and then summarized, various classification models are selected from a classification model pool to classify the summarized data, decision values of the various classification models are summarized to perform decision fusion, and a voting method is adopted for the decision fusion.
The main operations of the voting method are as follows: firstly, establishing a disease category dictionary for counting voting conditions; then, circularly reading the decision value of each sub-model, adopting a dictionary to carry out category and ticket number statistics, and sequencing the ticket numbers; then, adopting a relative majority vote method to carry out statistical calculation on the voting structure, and when the number of votes obtained in a certain category is the only maximum, outputting the category as a final conclusion by an algorithm; and when a plurality of categories get the most votes at the same time, randomly selecting one of the categories as a final conclusion.
Example 2
By adopting the scheme, as shown in fig. 2 and 4, after various medical data are preprocessed, different classification models are respectively adopted for classification; and the decision fusion adopts a weight method.
The main operation of adopting the weight method is as follows: the class probability after the analysis of each modal data is taken as a decision value of the modal data, and the decision value is taken as the input of the algorithm; firstly, aligning sub-model decision values according to categories, converting and splicing the sub-model decision values into a decision matrix; next, multiplying the decision matrix by corresponding elements of the weight array, and summing the weighted probabilities of each category according to columns; and finally, outputting the category with the maximum probability as a final conclusion.
Example 3
And another realization of fusing the scheme is to obtain a more accurate auxiliary diagnosis conclusion by analyzing a plurality of clinical data generated in the fusion diagnosis process. The following three stages are adopted specifically:
the first stage is as follows: data pre-processing
The first step is as follows: electronic medical record text data preprocessing
Removing words which appear in the electronic medical record text at high frequency but are irrelevant to content expression based on the stop word corpus; mapping the text vocabulary of the electronic medical record into vectors by applying Word2Vec model technology to provide a basic semantic model for a subsequent classification task;
based on the basic semantic model, generating a word vector by applying a Skip-Gram algorithm;
carrying out data augmentation processing on the electronic medical record text data:
aiming at the problem that the electronic medical record text Data is easy to influence the model precision and robustness due to the characteristics of small Data scale, unbalanced category and the like, the text Data augmentation technology EDA (easy Data augmentation) is adopted to increase the Data which can be used for model training. The specific treatment method comprises the following steps: the new data is generated by four means of synonym replacement, random insertion, random exchange and random deletion so as to achieve the augmentation effect. And the synonym replacement is to randomly select words from the original sentence and replace the words by using the words in the synonym stock. Random insertion is to insert the alternative synonym into a random position in the original sentence. Random exchange refers to randomly selecting two words in a sentence and exchanging their positions. Random deletion will randomly delete words in the original sentence with a certain probability.
The second step is that: medical image data preprocessing
Spatial registration: mapping the original medical image to a standard space to realize space registration;
correcting a bias field: using an FSL tool to realize bias field correction of the image;
automatic extraction of human tissues: based on the existing human tissue automatic extraction technology, part of tissues in the image are automatically extracted;
other pretreatment: cutting, size reforming, voxel normalization and other general preprocessing of the image;
data augmentation processing: horizontal flipping, vertical flipping, rotational transformation, etc. of medical images.
The third step: inspection index data preprocessing
In the preprocessing stage, feature selection such as dimension reduction is not carried out on input data, and only common data cleaning steps such as missing value completion are designed;
before the data to be analyzed is transmitted, discrete variables and category labels in the data such as personal basic information, examination and inspection indexes of a patient are coded in a unique coding mode, so that the distance between the features is calculated more reasonably.
And a second stage: respectively constructing diagnosis models aiming at various data
The first step is as follows: diagnosis model based on electronic medical record text data
The electronic medical record mainly comprises descriptive words or phrases of patient's chief complaints on disease symptoms, patient's current medical history, past medical history, family medical history and the like, and is usually generated in the inquiry stage in the form of natural language. The content of electronic medical records is also very different due to differences between the recording staff and the disease. The medical record text phrases obtained by single inquiry are short and refined, and are more inclined to short sentences compared with common texts, important information is uniformly distributed in the sentences, and the inter-sentence dependence is weaker. Most of medical record texts obtained by long-term observation of patients are more detailed and contain time information which is vital to disease diagnosis, so that two models, namely a TextCNN model and a TextRNN model, are adopted in the design of an electronic medical record text data diagnosis model to respectively process different types of electronic medical record text data.
The TextCNN is a convolutional neural network for a text classification task, and has the advantages that local correlation in a text can be captured, and a simple network framework enables a model to have strong extraction capability on text shallow features and is friendly to a short text classification task. And due to the high-speed parallelism of the CNN, the training time can be greatly reduced. Aiming at the electronic medical record of the short sentence type, the Embedding Layer (Embedding Layer) and partial parameters are modified and adjusted by adopting a TextCNN electronic medical record diagnosis model on the basis of an original TextCNN model.
The embedded layer of the model adopts word vectors generated by pre-training, and adopts a static mode for the pre-trained word vectors in the model training process, namely, the pre-trained word vectors are used for initializing the appeared words, and the words which do not appear in the pre-training process are initialized randomly, and the word vector parameters are not adjusted in the subsequent network weight updating process. Because the text expressed by the word vector is one-dimensional data, the convolution layer of the model adopts one-dimensional convolution and extracts the characteristics of different visual field sizes by designing convolution kernels with different sizes. Due to the characteristics of the convolution kernel, although TextCNN can capture whether a keyword appears in a text and the similarity intensity distribution, the number and sequence of the occurrence of the keyword are missed, which results in that CNN cannot model longer sequence information. Therefore, a TextRNN electronic medical record diagnosis model is set for the long text data of the electronic medical record, and comprises a 1-layer embedding layer, a 2-layer hiding layer and a 2-layer full-connection layer. The network keeps the setting of the embedding layer of the TextCNN unchanged, adopts 2 layers of 128 LSTM or GRU units to construct a hidden layer, averages the output of the LSTM or GRU units according to the sentence dimension, takes the averaged vector as the vector containing the whole sentence information, and inputs the vector into a full connection layer to finish the disease category diagnosis.
The second step is that: diagnostic model based on medical image data
Medical images are one of common auxiliary examination means, and three classification models of AlexNet, ResNet18 and ResNet50 are realized based on a convolution algorithm aiming at different positions of a human body so as to be suitable for medical image classification tasks under different data set scales.
The classical convolutional neural network AlexNet can minimize training time while ensuring model accuracy. In addition, due to the simple network structure, the overfitting condition on a small data set can be reduced, and the model can obtain a better generalization effect. The model comprises 5 convolutional layers, 3 maximum pooling layers and 3 full-link layers. In order to avoid the gradient vanishing condition which can occur in the training process, the ReLU activation function is used in the partial convolution layer and the full connection layer, so that the interdependence relation between parameters is reduced, and the calculation amount is reduced. Meanwhile, Dropout regularization functions are used between all the fully-connected layers, and part of nerve units are hidden with certain probability in training, so that the effect of reducing overfitting is achieved.
Although the 11-tier network architecture of AlexNet can accomplish most image classification tasks with a reasonable accuracy, the depth of the AlexNet model limits the possibility of achieving higher accuracy for classification tasks with sufficient training time and computational resources. Therefore, two network structures, namely a ResNet18 medical image diagnosis model and a ResNet50 medical image diagnosis model, are adopted for the tasks. Both the implementations of ResNet18 and ResNet50 follow the basic ResNet architecture, consisting of 1 convolutional layer, 4 residual blocks consisting of multiple convolutional layers and residual functions, and 1 fully-connected layer. Meanwhile, a ReLU activation function is used between each convolution layer, and Dropout is also performed after the full connection layer. The difference between the two is only in the number of convolutional layers and the parameter setting included in each residual block.
The third step: the diagnostic model based on the basic information and the inspection index data aims at structured data such as basic information (population characteristics) of patients and inspection indexes in clinical data, and the XGboost algorithm is used for constructing the model.
The XGboost algorithm comprises fifteen parameters, including a base classifier, a learning target, a learning step length, a sub-classifier node depth, a sub-classifier node weight and the like of the algorithm. Because of numerous parameters, the method uses a grid search algorithm to select and optimize the parameters so as to obtain a better parameter set and achieve a better training effect. In order to prevent the overfitting phenomenon of the model in the training process and enable the model to be trained by using data as much as possible under the condition of small data magnitude, ten-fold cross validation is used in the model training process so as to ensure the reliability of the model accuracy.
And a third stage: the auxiliary diagnosis models of the multi-modal data are fused to finally perform auxiliary diagnosis, so that the subsequent expansion of the system is facilitated, and when a fusion analysis method of heterogeneous multi-modal clinical data such as electronic medical record text data, medical image data, examination and inspection index data and the like is selected, a flexible multi-modal data fusion strategy with a wide application range, namely a decision-level fusion strategy, is adopted. The strategy allows different modal data to train respective models, and fusion analysis is performed on results of all sub models at a decision level, so that a global optimal decision is finally obtained. This means that the system application scenario can be extended by adding a new diagnostic model without affecting the original model and fusion method. The specific fusion method adopted by the decision-level fusion strategy is different according to different applicable scenes. In the stage, two common decision-level fusion methods, namely a voting method and a weight method, are realized, so that the method is suitable for fusion analysis of heterogeneous clinical data in most scenes.
In summary, the core idea of the present invention is to perform decision-level fusion on multi-modal clinical data, and first call a diagnosis model constructed by each modal data to classify and identify medical images, electronic medical record texts, patient basic information (demographic characteristics) and 3 types of data of examination and inspection indexes generated in an examination process. Then, the disease category label with the highest probability calculated by each model is used as a decision value of the submodel, a voting method or a weight method is selected according to the actual diagnosis rule or expert experience of the disease, all the obtained decision values are subjected to statistical calculation, and the disease category obtained by fusion analysis is finally output.
Claims (10)
1. A medical diagnosis assistance method characterized by: the method comprises the following steps:
classifying by adopting various classification models based on various medical data to obtain a plurality of classification decision values;
and performing decision fusion on the plurality of classification decision values to obtain one classification decision value which is used as a classification result to be output.
2. The medical diagnostic support method according to claim 1, characterized in that: the medical data at least comprises demographic characteristics, medical examination indexes, medical images and medical record texts.
3. The medical diagnostic support method according to claim 1, characterized in that: and the decision fusion is to select a classification decision value by adopting a voting method or a weight method.
4. A medical diagnostic support method according to claim 3, characterized in that: the voting method comprises the following steps:
initialization: establishing a statistical result list and setting the value to zero;
counting the classified tickets: counting and counting the classification decision values and recording the counting values into a counting result list;
sorting the statistical values: reversely ordering the statistical result list;
selecting a maximum value: and randomly selecting one item in the maximum item in the statistical result list as a result.
5. A medical diagnostic support method according to claim 3, characterized in that: the weight method comprises the following steps:
initializing the weight matrix: respectively taking the total number of the decision values and the number of the classification categories as the number of rows and the number of columns, establishing a weight matrix and filling preset weight parameters;
converting a decision matrix: aligning the classified decision values according to classification categories, converting and splicing the classified decision values into a decision matrix;
matrix multiplication: multiplying the weight matrix and the decision matrix item by item to obtain a result matrix;
and (3) summing the weights: summing the result matrix according to classification and category to obtain a summary value;
and returning a result: and selecting the classification category with the largest summary value as a result.
6. The medical diagnostic support method according to claim 1, characterized in that: and respectively preprocessing the plurality of medical data.
7. The medical diagnostic support method according to claim 2, characterized in that: and preprocessing the demographic characteristics by adopting a model constructed based on an XGboost algorithm.
8. The medical diagnostic support method according to claim 7, characterized in that: the XGboost algorithm comprises more than ten parameters, the parameters are selected and optimized by adopting a network search algorithm, and ten-fold cross validation is adopted when a model is constructed.
9. The medical diagnostic support method according to claim 4, characterized in that: the sum of the weight parameters is 1.
10. A medical diagnosis assistance system characterized by: comprises that
At least one processor; and
a memory coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to implement the method of any one of claims 1-9.
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