How to use ICD10 Code in a regression model in R

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I am trying to find the ICD10 codes which are causing certain disease. But ICD10 has alpha numeric classification e.g. A00.00 . There are 1000s of such classifications but I am not sure how to use them in my regression model. Any suggestion please.

Data Patient Existing ICD10 Diabetic (Y) P1 A00.10 1 P2 A00.20 0 P1 C00.1 1 P3 Z01 1 ....
Apr 11 in Machine Learning by Nandini
• 5,480 points
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1 answer to this question.

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Using the concept of comorbidities is a good method to do this. This is done by my R package icd for standardized collections of diseases, such as "Diabetes," "Cancer," and "Heart Disease." There are several comorbidity maps to choose from, so you may find one that matches your interests, for example. The PCCC maps in icd can be used for children, while the others are for adults and cover a wide range of diseases.

As an example, see the vignette in the introduction. These are ICD-9 codes, although ICD-10 can be used instead.

data <- data.frame(
   visit_id = c(1000, 1000, 1000, 1000, 1001, 1001, 1002),
   icd9 = c("40201", "2258", "7208", "25001", "34400", "4011", "4011"),
   poa = c("Y", NA, "N", "Y", "X", "Y", "E"),
   stringsAsFactors = FALSE
   )
data
  visit_id  icd9  poa
1     1000 40201    Y
2     1000  2258 <NA>
3     1000  7208    N
4     1000 25001    Y
5     1001 34400    X
6     1001  4011    Y
7     1002  4011    E
icd::comorbid_ahrq(data)
CHF Valvular  PHTN   PVD  HTN Paralysis NeuroOther Pulmonary    DM  DMcx Hypothyroid Renal Liver
1000  TRUE    FALSE FALSE FALSE TRUE     FALSE      FALSE     FALSE  TRUE FALSE       FALSE FALSE FALSE
1001 FALSE    FALSE FALSE FALSE TRUE      TRUE      FALSE     FALSE FALSE FALSE       FALSE FALSE FALSE
1002 FALSE    FALSE FALSE FALSE TRUE     FALSE      FALSE     FALSE FALSE FALSE       FALSE FALSE FALSE
       PUD   HIV Lymphoma  Mets Tumor Rheumatic Coagulopathy Obesity WeightLoss FluidsLytes BloodLoss
1000 FALSE FALSE    FALSE FALSE FALSE      TRUE        FALSE   FALSE      FALSE       FALSE     FALSE
1001 FALSE FALSE    FALSE FALSE FALSE     FALSE        FALSE   FALSE      FALSE       FALSE     FALSE
1002 FALSE FALSE    FALSE FALSE FALSE     FALSE        FALSE   FALSE      FALSE       FALSE     FALSE
     Anemia Alcohol Drugs Psychoses Depression
1000  FALSE   FALSE FALSE     FALSE      FALSE
1001  FALSE   FALSE FALSE     FALSE      FALSE
1002  FALSE   FALSE FALSE     FALSE      FALSE

Diabetes Mellitus is represented by the letter "DM," whereas diabetes with complications, such as retinopathy or renal failure, is represented by the letter "DMcx." This is with the standard Elixhauser classifications as modified by the US AHRQ.

You can utilize binary flags for illness stages in any statistical or machine learning model if you have them.

answered Apr 12 by Dev
• 6,000 points

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