Harvard:Biophysics 101/Notebook:ZS/2007-4-18

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Back in business! No more M-CRAP :D

What I plan to do in the near future

We've established that there is a demand to classify rs#'s based on clinical importance - one way to go about doing this relatively painlessly is to determine how many people are diagnosed with the disorder when given a disease name. With that in mind, I found a excel file from California which has disease names linked to the CDC naming convention, along with number diagnosed; if I can mine this file given a disease ID, or better yet just an rs#, then we have a basis by which to judge clinical importance.

Note: I'm really sorry guys, but I won't be able to work on this until after the 16th - I have the MCAT on the morning of, but afterwards I'll get to it. Hopefully it won't be too complicated though.

Resources for disease frequency

Progress

4.18.07: Building off the discussion on 4.17.07, completed a module which takes as input OMIM keywords supplied by Resmi and co. and gives as output ICD9 IDs and prevalence information, based on data from the state of California.

To do: Make it more legible, conglomerate statistics, figure out a way to rank prevalence. Extension required: Find a dB which has ICD9 ID's and genetic prevalance - that would be a gold mine. literally.

Useful links (my own ref:)

#ICD9_prevalance.py
#Zachary Sun
#4.18.07, Biophysics 101

#This code does a couple of things:
#1) Enables lookup of ICD9 ID numbers when given a search term
#    *This is particularly useful as all data (WHO, CDC) is based
#    *on the ICD9 ID, which is a method of classifying all known
#    *diseases. This code is able to queue a website, http://icd9cm.chrisendres.com
#    *which has the database of diseases - it returns the best hits
#    *in dirty HTML, which can then be parsed to obtain ICD9 #'s and description.
#2) Enables lookup of prevalance data in databased
#    *Currently, I only have it hooked up to the State of CA prevalance data from
#    *http://www.oshpd.ca.gov/hqad/PatientLevel/ICD9_Codes/index.htm, it does a
#    *lookup based on #1 and returns prevalance data. As soon as I find more databases
#    *I can extend the search; I haven't found much good though save the main CDC db
#    *at http://wonder.cdc.gov/.

#To do: clean up output

import os
import string
import urllib

fh = open(os.path.join(os.curdir, "dx05.txt")) #the CA data file
line = fh.readline()
disease_name = "asthma" #INSERT DISEASE NAME HERE FOR TESTING
ICD9code = []
found = 0


####
#queueing http://icd9cm.chrisendres.com for code lookup
####
queue_name = 'http://icd9cm.chrisendres.com/index.php?action=search&srchtype=diseases&srchtext='
queue_name = queue_name + disease_name

    
code_lookup = urllib.urlopen(queue_name).read() #send queue request to site, returns dirty html
out = open("index.txt", "w") #write dirty html to file
out.write(code_lookup)
out.close()

readCode = open("index.txt", "r") #read dirty html
lookup_line = readCode.readline()
print "***ICD9 and hits, arranged by importance***\n"
while lookup_line:
    w= lookup_line.find("<div class=dlvl>") #the unique marker before the disease
    if w != -1: #if it is found
        tempCode = lookup_line[32:40] #code in this section
        tempCode = string.split(tempCode, ' ') #split into number
        ICD9code.append(tempCode[0])
        print lookup_line[32:len(lookup_line)-7] #disable in final version - shows the hits found
    lookup_line = readCode.readline()


####
#searching incidence data in CA data file
####
print "\n\n***Prevalance data per IDC9 above***\n"
while line: #for every line in the data
    line = line[:-1] #remove /n
    lineVector = string.split(line, ',') #split to vector
    for code in ICD9code:
        if lineVector[0].find(code) != -1: #if disease hit
            print "Found ID ",code," , incidence: " , lineVector[1]
            break
    line = fh.readline()

output:

***ICD9 and hits, arranged by importance***

493.0 Extrinsic asthma
493.1 Intrinsic asthma
493.9 Asthma, unspecified
495.8 Other specified allergic alveolitis and pneumonitis
493.2 Chronic obstructive asthma
V17.5 Asthma</div>
493.8 Other forms of asthma</div>
493.82 Cough variant asthma</div>
507.8 Due to other solids and liquids
786.07 Wheezing


***Prevalance data per IDC9 above***

Found ID  493.0  , incidence:  7033
Found ID  493.0  , incidence:  2354
Found ID  493.0  , incidence:  4307
Found ID  493.1  , incidence:  69
Found ID  493.1  , incidence:  27
Found ID  493.1  , incidence:  52
Found ID  493.2  , incidence:  29291
Found ID  493.2  , incidence:  1447
Found ID  493.2  , incidence:  20240
Found ID  493.8  , incidence:  332
Found ID  493.8  , incidence:  36
Found ID  493.9  , incidence:  123688
Found ID  493.9  , incidence:  4839
Found ID  493.9  , incidence:  21918
Found ID  495.8  , incidence:  38
Found ID  507.8  , incidence:  232
Found ID  786.07  , incidence:  932
Found ID  V17.5  , incidence:  1766
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