Patent ID: 11875882
Assignee: ZHEJIANG LAB
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 5:
6. The system for predicting end-stage renal disease complication risk based on contrastive learning according to claim 5, wherein the complication representation learning model defining component comprises:
a parameter definition block, configured to define hyper-parameters of the network structure, wherein the parameter definition block comprises an encoder and a projector;
a feature normalization block, configured to input the augmented structured data in pairs into the encoder, to obtain the initial complication representation, obtain the contrastive representation from the initial complication representation through the projector, and obtaining the normalization representation from the contrastive representation through feature normalization operation; and
a total loss definition block, configured to construct the total loss function using the normalization representation, a covariance item, a variance item, a category similarity measure item and an augmented similarity measure item;
wherein the category similarity measure item is calculated as follows:, s
    C
   
   (
   
    
     Z
     
      n
      ⁢
      o
      ⁢
      r
      ⁢
      m
     
    
    ,
    
     Z
     
      ′
      ⁢
      norm
     
    
   
   )
  
  =
  
   
    
     
      -
      1
     
     
      2
      ⁢
      
       (
       
        u
        +
        1
       
       )
      
      ⁢
      N
     
    
    ⁢
    
     
      ∑
      j
      
       2
       ⁢
       
        (
        
         u
         +
         1
        
        )
       
       ⁢
       N
      
     
     
      
       1
       
        i
        ≠
        j
       
      
      ⁢
      
       1
       
        
         y
         i
        
        =
        
         y
         j
        
       
      
      ⁢
      log
      ⁢
      
       
        E
        
         i
         ⁢
         j
        
       
       
        
         
          ∑
           
         
         k
         
          2
          ⁢
          
           (
           
            u
            +
            1
           
           )
          
          ⁢
          N
         
        
        ⁢
        
         1
         
          i
          ≠
          k
         
        
        ⁢
        
         1
         
          
           y
           i
          
          ≠
          
           y
           k
          
         
        
        ⁢
        
         E
         
          i
          ⁢
          k
         
        
       
      
      ⁢
      
       E
       
        i
        ⁢
        j
       
      
     
    
   
   =
   
    
     
      Z
      i
      
       n
       ⁢
       o
       ⁢
       r
       ⁢
       m
      
     
     ·
     
      Z
      j
      norm
     
    
    
     
      
      
       Z
       i
       norm
      
      
     
     ·
     
      
      
       Z
       j
       norm
      
      
     
    
   
  
 

where Znorm represents the normalization representation, N represents a positive sample size of a batch randomly sampled, u represents the number of samples augmented respectively by each positive sample matching one negative sample, 2(u+1)N represents a sample size of a batch after augmenting, comprising augmented samples, samples of a same category and samples of different categories, yi represents a category label of a sample i, yj represents a category label of a sample j, yk represents a category label of a sample k, ∥Zinorm∥ represents a norm of a vector Zinorm, ∥Zjnorm∥ represents a norm of a vector Zjnorm, Eij represents a cosine distance between the sample i and the sample j, and Eik represents a cosine distance between the sample i and the sample k; and
wherein the augmented similarity measure item is calculated as follows:, s
    A
   
   (
   
    
     Z
     norm
    
    ,
    
     Z
     
      ′
      ⁢
      norm
     
    
   
   )
  
  =
  
   
    
     -
     1
    
    
     2
     ⁢
     
      (
      
       u
       +
       1
      
      )
     
     ⁢
     N
    
   
   ⁢
   
    
     ∑
     j
     
      2
      ⁢
      
       (
       
        u
        +
        1
       
       )
      
      ⁢
      N
     
    
    
     
      1
      
       i
       ≠
       j
      
     
     ⁢
     
      1
      
       
        A
        i
       
       =
       
        A
        j
       
      
     
     ⁢
     log
     ⁢
     
      
       E
       
        i
        ⁢
        j
       
      
      
       
        
         ∑
          
        
        k
        
         2
         ⁢
         
          (
          
           u
           +
           1
          
          )
         
         ⁢
         N
        
       
       ⁢
       
        1
        
         i
         ≠
         k
        
       
       ⁢
       
        1
        
         
          A
          i
         
         ≠
         
          A
          k
         
        
       
       ⁢
       
        E
        
         i
         ⁢
         k
        
       
      
     
    
   
  
 

where Ai represents an augmented label of the sample i, Ai=Aj represents that the sample i and the sample j are obtained by augmenting a same sample, and Ai≠Ak represents that the sample i and the sample j are obtained by augmenting different samples.