I am currently working with a large set of health insurance claims data that includes some laboratory and pharmacy claims. The most consistent information in the data set, however, is made up of diagnosis (ICD-9CM) and procedure codes (CPT, HCSPCS, ICD-9CM).
My goals are to:
- Identify the most influential precursor conditions (comorbidities) for a medical condition like chronic kidney disease;
- Identify the likelihood (or probability) that a patient will develop a medical condition based on the conditions they have had in the past;
- Do the same as 1 and 2, but with procedures and/or diagnoses.
- Preferably, the results would be interpretable by a doctor
I have looked at things like the Heritage Health Prize Milestone papers and have learned a lot from them, but they are focused on predicting hospitalizations.
So here are my questions: What methods do you think work well for problems like this? And, what resources would be most useful for learning about data science applications and methods relevant to healthcare and clinical medicine?
EDIT #2 to add plaintext table:
CKD is the target condition, "chronic kidney disease", ".any" denotes that they have acquired that condition at any time, ".isbefore.ckd" means they had that condition before their first diagnosis of CKD. The other abbreviations correspond with other conditions identified by ICD-9CM code groupings. This grouping occurs in SQL during the import process. Each variable, with the exception of patient_age, is binary.