CN116230224A - Method and system for predicting adverse events of heart failure based on time sequence model - Google Patents
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Abstract
The invention discloses a method and a system for predicting adverse events of heart failure based on a time sequence model, wherein firstly, heart failure patient data are extracted; preprocessing the extracted heart failure patient data to extract required variables; performing missing value filling on the extracted data by using a Fancyimpute tool; training the time sequence information of the patient by using Bi-LSTM; learning the importance of different variables in each visit by the patient with an attention mechanism; the contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized. The invention processes the problems of data missing and data unbalance by processing the data missing value and using the contrast loss, better obtains the representation of the patient, improves the prediction performance of the model, better learns the relation between the time sequence information and the variable accessed by the heart failure patient each time, improves the system interpretability and simultaneously provides a reliable basis for judging the adverse events of doctors.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a method and a system for predicting adverse events of heart failure based on a time sequence model.
Background
Heart Failure (HF), abbreviated as Heart Failure, is a disease in which the structure or function of the Heart is abnormal. Heart failure is the end-stage of the progression of various heart diseases and manifests itself as a complex set of clinical complex phenomena. Such as impaired ventricular filling, reduced ejection function, ventricular dysfunction, insufficient cardiac output, stagnant blood in the pulmonary and/or systemic circulation, and insufficient blood perfusion in organs and tissues. Among them, dyspnea, susceptible fatigue, pulmonary congestion and peripheral edema are the main clinical manifestations of heart failure. As one of the world-recognized chronic cardiovascular diseases, heart failure has the manifestations of high prevalence, high medical cost, poor prognosis effect and the like. Heart failure has evolved into a significant public health problem worldwide.
How to evaluate the mortality rate within 5 years after the prognosis of the heart failure patient aiming at the specific condition of the heart failure patient, and according to the mortality rate condition evaluated by the patient, a doctor can assign a more reasonable and scientific prognosis improvement scheme, which is an important means for preventing the illness from being more serious, improving the prognosis effect of the patient, positively influencing the life quality of the patient and further reducing the medical expense.
There have been studies using CNN methods to predict whether adverse patient events occur, employing general health status representation learning models, using dilation convolution with a multi-scale acceptance domain to extract multi-time scale clinical features. While CNNs can effectively preserve neighborhood relationships and spatial locality of inputs, they are limited in temporal data mining due to the lack of partial and global correlation loss. Furthermore, most existing CNN-based methods assume that medical events during hospital visits are recorded strictly in chronological order, which is not typically the case in real electronic medical records. It further affects the overall performance of these methods. There have also been some studies to begin modeling different types of medical sequence building sequence hidden states and modeling their interrelationships with hidden neurons, and although these approaches take into account differences in different types of medical data, the interrelationship between heterogeneous data has not been fully explored. Furthermore, most methods do not effectively fuse multiple aspects of medical information because they simply connect related feature vectors from different types of data to construct the final patient representation.
Disclosure of Invention
The invention aims to: aiming at the problems existing in the prior art, the invention provides a method and a system for predicting adverse events of heart failure based on a time sequence model.
The technical scheme is as follows: the invention provides a method for predicting heart failure adverse events based on a time sequence model, which specifically comprises the following steps:
(1) Extracting patient data diagnosed with heart failure from a public data set MIMIMIIC-III, and preprocessing the data to extract needed information;
(2) Performing missing value filling on the extracted data by using a Biscaler in the Fancyimpute tool;
(3) Training the supplemented data to learn the time sequence information of the patient by using Bi-LSTM;
(4) Learning the importance of different variables in each visit by the patient based on an attentiveness mechanism;
(5) The contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized.
Further, the implementation process of preprocessing the data in the step (1) to extract the needed information is as follows:
demographic information, laboratory tests, medications, surgery, residence time and number of ICU's closely related to heart failure are extracted;
introducing a large class of diseases, surgery and extracting only the most relevant laboratory detection, and extracting relevant disease information, medication information and surgery information;
and assigning the major diseases of the patient to 1 according to ICD9 codes of the patient to form 66-dimensional user information.
Further, the implementation process of the step (3) is as follows:
the demographics, diagnosis information, medication information, operation information, ICU stay information and laboratory detection of the patient are combined to generate 66-dimensional information x i ；
Using a corrected linear unit and linear mapping function to obtain an access representation, embedding patient information as a low-dimensional vector representation v i The calculation formula is as follows:
v i ＝ReLU(W v x i +b c )
wherein ,Wv ∈R m×L Is a weight matrix that can rank the importance of each medical code, m is an embedded vector v i Is of a size of (2);
using Bi-LSTM as input to learn time series information of each patient, each forward LSTM cell has a memory cell state S i Controlled by three Sigmoid gates: forgetting door F i Input door l i And an output gate O i The method comprises the steps of carrying out a first treatment on the surface of the Forgetting door F i Is used to determine that the slave storage unit S should be i Which information is discarded, and input gate l i Is used to determine which information is to be stored; output door O i Will determine the output battery state S i Information of (2); through these three gates, the hidden state of the forward LSTM cell
The calculation formula of (2) is as follows:
F i ＝σ(W f [h i-1 ；v i ]+b f )
I i ＝σ(W i [h i-1 ；v i ]+b i )
O i ＝σ(W o [h i-1 ；v i ]+b o )
wherein ,[hi-1 ；v i ]∈R q+m Is the previous hidden state h i-1 And current access embedding vector v i Q represents each hidden state h i Dimension, W f ，W i ，W o ，W s ∈R q×(q+m) B for the weight matrix to be learned f ，b i ，b o ，b s ∈R q For the bias vector, σ is a logical s-shaped function,
representing element-level multiplication, likewise resulting in a hidden state of a backward LSTM cell>
Then obtaining the hidden state h of the Bi-LSTM cell i The calculation formula is as follows:
further, the implementation process of the step (4) is as follows:
deriving a context vector C based on a location-based attention mechanism t ：
wherein ,hi Represents the hidden state of the ith access, alpha ti Is from the current hidden state h i Capturing a vector of weights; alpha ti Calculated by the following formula:
α ti ＝W α h i +b α
α t ＝softmax([α t1 ，α t2 ，…，α t(t-1) ])
wherein ,Wα ∈R q and bα E, R is a parameter to be learned, and represents weight and deviation respectively;
obtaining final representation of patient through attention mechanism and Bi-LSTM aggregation time sequence information and mode information of patient access
The calculation formula is as follows:
further, the implementation process of the step (5) is as follows:
the final patient representation vector is placed into a contrast loss function, which classifies the patient into two categories, the loss function is as follows:
wherein ,
representing two sample features X 1 and X2 The euclidean distance of (2) is represented by P, Y is a label of whether two samples are matched, y=1 represents that the two samples are similar or matched, y=0 represents that the two samples are not matched, m is a set threshold value, and N is the number of samples.
Based on the same inventive concept, the invention also provides a heart failure adverse event prediction system based on a time sequence model, which comprises the following steps:
the information extraction module is used for acquiring a heart failure patient data set from the MIMIMIC-III data set, preprocessing the data, and extracting demographic information, laboratory detection information, related disease information, medication information, operation information and ICU stay information of a patient;
the information supplementing module is used for supplementing the missing value of the extracted heart failure patient information by using a Biscaler in the Fancyimpute tool;
the heart failure patient adverse event prediction module trains the supplemented data to learn time sequence information of the patient by using Bi-LSTM; learning the importance of different variables in each visit by the patient with an attention mechanism; the contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized.
The beneficial effects are that: compared with the prior art, the invention has the beneficial effects that: 1. the time sequence information is considered, and the information such as demographics of the patient, laboratory detection, diagnosis results of hospitalization, patient observation record data, medication of hospitalization period, operation information of hospitalization period, ICU stay information and the like is comprehensively considered, so that multiple tasks such as mortality rate of different time windows of admission, readmission, intubation and the like of heart failure patients can be accurately predicted without any medical expert assistance; 2. the Biscaler is used for obtaining a double normalization matrix through iterative estimation of column mean values and standard deviation, so that the problem of data sparseness is solved, and the prediction capability of a model is improved; 3. according to the invention, the non-supervision study is performed through the contrast study, the samples are divided into two types, and patients do not need to be divided according to the labels, so that the problem that the heart failure patients have serious unbalance is solved.
Drawings
FIG. 1 is a flow chart of a method of predicting adverse events of heart failure based on a temporal model.
Fig. 2 is a schematic diagram of a system for predicting adverse events of heart failure based on a time series model.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention provides a method for predicting adverse events of heart failure based on a time sequence model, which is shown in figure 1 and comprises the following steps:
step 1, extracting patient data diagnosed with heart failure from a public data set MIMIMIMIIC-III; the extracted heart failure patient data is preprocessed to extract the required information.
The MIMIC-III dataset is a multivariate time series dataset consisting of sparse and irregularly sampled physiological signals, and has two main types of underlying data: one type is clinical data extracted from EHR, including demographic information, diagnostic information, laboratory test information, medical imaging information, vital signs, etc. of the patient; the second type of data is waveform data collected by bedside monitoring equipment and related vital sign parameters and event records.
First, ICD-9 codes corresponding to heart failure in the MIMIMIIC-III dataset are determined, and the corresponding heart failure patient is extracted from the MIMIMIC-III dataset using Pandas as the initial dataset.
The extracted heart failure patient data set includes information such as demographics of the patient, laboratory tests, diagnosis results of hospitalization, patient observation record data, medication during hospitalization, operation information during hospitalization, and ICU stay information, and since adverse events of the heart failure patient are predicted, it is necessary to extract demographics information, laboratory tests, medication, operation, and ICU stay time and number closely related to heart failure.
Since there are more than 2000 disease ICD codes, and there are nearly two thousands of operations and medications, if all remain, the problem of dimensional explosions and data sparseness can occur with one-hot codes, so by introducing a large class of diseases, operations and extracting only the most relevant laboratory tests. 26 related diseases are extracted in this embodiment, including: cardiomyopathy, myocarditis, pericardial disease, coronary heart disease class I, diabetes, renal failure, congenital cardiovascular disease, drug abuse, hyperthyroidism, connective tissue disease, hyperlipidemia, other heart diseases, cardiac arrhythmias, valvular disease, endocarditis, pulmonary circulatory disorders, respiratory failure, peripheral vascular disease, hypertension, renal disease, abnormal cardiac structure, obesity, alcohol addiction, sudden cardiac arrest and sudden cardiac death, smoking, coronary heart disease class II. Class 7 medication: polycosans, sartan, beta-receptor, calcium channel blocker, digitalis, diuretics and nitrates. 6 operations: heart transplantation, heart resynchronization therapy, implantable cardioverter/defibrillator, left ventricular assist device, coronary artery surgery, valve surgery.
Since the disease and surgery are both represented using ICD9 codes, the computer is unable to recognize, and therefore, the patient's major disease needs to be assigned 1 according to the patient's ICD9 code. A 66-dimensional user information is formed.
And 2, performing missing value filling on the extracted data by using a Biscaler in a Fancyimpute tool.
The heart failure patient data extracted by the method can cause the problems of data sparseness and the like, and the model can not learn useful characteristics, so that the prediction performance is reduced. Therefore, the missing values need to be complemented, most of the complement methods are used at present to replace missing items by the average value or the median value of each column, but the data lacks individuality, the detection information of each patient is the same, the deep learning model is not capable of learning different characteristics. As shown in FIG. 2, the present invention adopts Biscaler in the Fancyimpute toolkit for data population.
Step 3: training the supplemented data with Bi-LSTM to learn the patient's timing information.
Model training is carried out by using the filled data, and each diagnosis information, medication information, operation information, ICU stay information and laboratory detection of a patient are combined to generate 66-dimensional information x i 。
Using a corrected linear unit (ReLU) and linear mapping function to obtain an access representation, patient information is embedded as a low-dimensional vector representation v i The calculation formula is as follows:
v i ＝ReLU(W v x i +b c )
wherein ,Wv ∈R m×L Is a weight matrix that can rank the importance of each medical code, m is an embedded vector v i Is of a size of (a) and (b).
Using Bi-LSTM as input to learn time series information of each patient, each forward LSTM cell has a memory cell state S i Controlled by three Sigmoid gates: forgetting door F i Input door l i And an output gate O i . Forgetting door F i Is used to determine that the slave storage unit S should be i Which information is discarded, and input gate l i Is used to determine which information is to be stored. Finally, output gate O i Will determine the output power
Pool state S i Is a piece of information of (a). Through these three gates, the hidden state of the forward LSTM cell
The calculation formula of (2) is as follows:
F i ＝σ(W f [h i-1 ；v i ]+b f )
I i ＝σ(W i [h i-1 ；v i ]+b i )
O i ＝σ(W o [h i-1 ；v i ]+b o )
wherein ,[hi-1 ；v i ]∈R q+m Is the previous hidden state h i-1 And current access embedding vector v i Is connected to the connection of (a). q represents each hidden state h i Is a dimension of (c). W (W) f ，W i ，W o ，W s ∈R q×(q+m) B for the weight matrix to be learned f ,b i ,b o ,b s ∈R q Is a bias vector. Sigma is a logical s-type function,
representing element-level multiplication operations. Similarly, we can also get a hidden state of the backward LSTM cell +.>
Then, the hidden state h of the Bi-LSTM cell can be obtained i The calculation formula is as follows:
step 4: the attention mechanism is used to learn the importance of the different variables in each visit by the patient.
Using only Bi-LSTM ignores some pattern information between accesses. Attention mechanisms are introduced to improve the interpretability of the model. Deriving a context vector C using a location-based attention mechanism t This helps capture more information for high risk prediction tasks and achieves higher performance in processing the temporal EHR data. Context vector C in the model used herein t The calculation method of (2) is as follows:
wherein hi Represents the hidden state of the ith access, alpha ti Is from the current hidden state h i A vector of weights is captured. Alpha ti Can be calculated by the following formula:
α n ＝W α h i +b α
α t ＝softmax([α t1 ，α t2 ，…，α t(t-1) ])
wherein ,Wα ∈R q and bα E R are parameters to be learned, which represent weights and deviations, respectively.
Obtaining final representation of patient through attention mechanism and Bi-LSTM aggregation time sequence information and mode information of patient access
The calculation formula is as follows:
step 5: the contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized.
The final patient representation vector is put into a contrast loss function, the patient is divided into two or more classes, the problem of sample imbalance is solved, and the loss function is as follows:
wherein ,
representing two sample features X 1 and X2 The euclidean distance (two norms) P of the samples represents the feature dimension of the samples, Y is a label whether the two samples match, y=1 represents that the two samples are similar or match, y=0 represents no match, m is a set threshold, and N is the number of samples.
As shown in fig. 2, the present invention further provides a system for predicting adverse events of heart failure based on a time series model, which comprises: the information extraction module is used for acquiring a heart failure patient data set from the MIMIMIC-III data set, preprocessing the data, and extracting demographic information, laboratory detection information, related disease information, medication information, operation information and ICU stay information of a patient; the information supplementing module is used for supplementing the missing value of the extracted heart failure patient information by using a Biscaler in the Fancyimpute tool; the heart failure patient adverse event prediction module trains the supplemented data to learn time sequence information of the patient by using Bi-LSTM; learning the importance of different variables in each visit by the patient with an attention mechanism; the contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized.
The invention has numerous methods and approaches to embodying this solution, and the above are just preferred embodiments of the invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be comprehended within the scope of the present invention.
Claims (6)
1. A method for predicting adverse events of heart failure based on a time sequence model, which is characterized by comprising the following steps:
(1) Extracting patient data diagnosed with heart failure from a public data set MIMIMIIC-III, and preprocessing the data to extract needed information;
(2) Performing missing value filling on the extracted data by using a Biscaler in the Fancyimpute tool;
(3) Training the supplemented data to learn the time sequence information of the patient by using Bi-LSTM;
(4) Learning the importance of different variables in each visit by the patient based on an attentiveness mechanism;
(5) The contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized.
2. The method for predicting adverse events of heart failure based on a time sequence model according to claim 1, wherein the preprocessing of the data in step (1) extracts the required information implementation process comprises the following steps:
demographic information, laboratory tests, medications, surgery, residence time and number of ICU's closely related to heart failure are extracted;
introducing a large class of diseases, surgery and extracting only the most relevant laboratory detection, and extracting relevant disease information, medication information and surgery information;
and assigning the major diseases of the patient to 1 according to ICD9 codes of the patient to form 66-dimensional user information.
3. The method for predicting adverse events of heart failure based on a time series model as claimed in claim 1, wherein the implementation process of the step (3) is as follows:
the demographics, diagnosis information, medication information, operation information, ICU stay information and laboratory detection of the patient are combined to generate 66-dimensional information x i ；
Using a corrected linear unit and linear mapping function to obtain an access representation, embedding patient information as a low-dimensional vector representation v i The calculation formula is as follows:
v i ＝ReLU(W v x i +b c )
wherein ,Wv ∈R m×L Is a weight matrix that can rank the importance of each medical code, m is an embedded vector v i Is of a size of (2);
using Bi-LSTM as input to learn time series information of each patient, each forward LSTM cell has a memory cell state S i Controlled by three Sigmoid gates: forgetting door F i Input door l i And an output gate O i The method comprises the steps of carrying out a first treatment on the surface of the Forgetting door F i Is used to determine that the slave storage unit S should be i Which information is discarded, and input gate l i Is used to determine which information is to be stored; output door O i Will determine the output battery state S i Information of (2); through these three gates, the hidden state of the forward LSTM cell
The calculation formula of (2) is as follows:
F i ＝σ(W f [h i-1 ；v i ]+b f )
I i ＝σ(W i [h i-1 ；v i ]+b i )
O i ＝σ(W o [h i-1 ；v i ]+b o )
wherein ,[hi-1 ；v i ]∈R q+m Is the previous hidden state h i-1 And current access embedding vector v i Q represents each hidden state h i Dimension, W f ，W i ，W o ，W s ∈R q×(q+m ) B for the weight matrix to be learned f ，b i ，b o ，b s ∈R q For the bias vector, τ is a logical s-type function,
representing element-level multiplication operations, likewise yielding a hidden state of a backward LSTM cell
Then obtaining the hidden state h of the Bi-LSTM cell i The calculation formula is as follows: />
4. The method for predicting adverse events of heart failure based on a time series model as claimed in claim 1, wherein the implementation process of the step (4) is as follows:
deriving a context vector C based on a location-based attention mechanism t ：
wherein ,hi Represents the hidden state of the ith access, alpha ti Is from the current hidden state h i Capturing a vector of weights; alpha ti Calculated by the following formula:
α ti ＝W α h i +b α
α t ＝softmax([α t1 ，α t2 ，...，α t(t-1) ])
wherein ,Wα ∈R q and bα E, R is a parameter to be learned, and represents weight and deviation respectively;
obtaining final representation of patient through attention mechanism and Bi-LSTM aggregation time sequence information and mode information of patient access
The calculation formula is as follows:
5. the method for predicting adverse events of heart failure based on a time series model as claimed in claim 1, wherein the implementation process of the step (5) is as follows:
the final patient representation vector is placed into a contrast loss function, which classifies the patient into two categories, the loss function is as follows:
wherein ,
representing two sample features X 1 and X2 The euclidean distance of (2) is represented by P, Y is a label of whether two samples are matched, y=1 represents that the two samples are similar or matched, y=0 represents that the two samples are not matched, m is a set threshold value, and N is the number of samples.
6. A time series model-based heart failure adverse event prediction system employing the method of any one of claims 1 to 5, comprising:
the information extraction module is used for acquiring a heart failure patient data set from the MIMIMIC-III data set, preprocessing the data, and extracting demographic information, laboratory detection information, related disease information, medication information, operation information and ICU stay information of a patient;
the information supplementing module is used for supplementing the missing value of the extracted heart failure patient information by using a Biscaler in the Fancyimpute tool;
the heart failure patient adverse event prediction module trains the supplemented data to learn time sequence information of the patient by using Bi-LSTM; learning the importance of different variables in each visit by the patient with an attention mechanism; the contrast loss function is used as a training loss function, so that the problem of data unbalance is solved, and the prediction of adverse events of heart failure patients is realized.
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