Harvard:Biophysics 101/Notebook:ZS/2007-5-3

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

#Ultimately: This code is able to output a formal disease name from informal info.
#INPUT: RS NUMBER
#Output1 : Mesh terms relevant to RS NUMBER
#INPUT2 : Mesh terms relevant to RS NUMBER
#Output2 : The single major mesh term (eg. disease name)
#INPUT3: The single major mesh term (eg. disease name)
#Output3: CDC# to standardize mesh term, formal disease name, and incidence

#This code does a couple of things:
#1) **Cynthia**: Takes in a rs# and outputs a list of mesh terms and important mesh terms
#2) **Zach**: Takes Cynthia's list of important mesh terms, 
#    *removes short words from mesh terms, and hits against paper titles from which it was taken
#    *for the mesh terms with the most paper title hits, it says that mesh term is the MAIN TERM
#    *feeds the main term to http://icd9cm.chrisendres.com
#3) **Zach**: Enables lookup of ICD9 ID numbers and formal name when given a MAIN 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.
#4) **Zach** Enables lookup of prevalance data in database
#    *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. Havent looked up but can theoretically
#    *link to main CDC db at http://wonder.cdc.gov/.

import os
import string
import urllib
import sys
from Bio import PubMed
from Bio import Medline

rsNumber = "rs11200638" # MODIFY THIS!
print "INPUT: ", rsNumber

###cynthias section###

# parses a mesh term to remove * and /
def parse_term(str, bool):
    parsed_term = str
    if(bool):
        parsed_term = parsed_term.replace('*', '')
    if str.find('/') != -1:
       parsed_term = parsed_term.replace('/', ' ')
    return parsed_term

# parses list of mesh terms
# returns embedded list, one with all terms and one major  terms
def parse_mesh(list):
    all_mesh_terms = []
    major_mesh_terms = []
    mesh_term = ''
    for i in range(len(list)):
        major = False
        if list[i].find('*') == -1:
            mesh_term = parse_term(list[i], major)
            all_mesh_terms.append(mesh_term)
        else:
            major = True
            mesh_term = parse_term(list[i], major)
            major_mesh_terms.append(mesh_term)
            all_mesh_terms.append(mesh_term)
    all_mesh = [all_mesh_terms, major_mesh_terms]
    return all_mesh
    
article_ids = PubMed.search_for("rs11200638")
rec_parser = Medline.RecordParser()
medline_dict = PubMed.Dictionary(parser = rec_parser)

all_mesh = []
all_mesh_terms = []
major_mesh_terms = []
for did in article_ids[0:5]:
    cur_record = medline_dict[did]
   # print '\n', cur_record.title, cur_record.authors, cur_record.source
    mesh_headings = cur_record.mesh_headings
    if len(mesh_headings) != 0:
        all_mesh = parse_mesh(mesh_headings)
        all_mesh_terms.extend(all_mesh[0])
        major_mesh_terms.extend(all_mesh[1])

#print '\n', all_mesh_terms, '\n', major_mesh_terms
######cynthia's section end#####


#####
#Problem to solve: Although Cynthia's output parses out the
#major mesh terms from the paper, each mesh term can give a hit
#ie: it does not figure out which is really the "major" major mesh term.
#I intend to solve it by hitting against the title to see if there are matches
#and by removing connecting words from the mesh terms.
#####

##modifying cynthia's output

new_term = []
for term in major_mesh_terms:
    test = term.split(" ") #split by " "
    temp = ""
    for test_term in test:
        if len(test_term) <= 3: #if it is a small connecting word
            pass
        else:
            if temp != "":
                temp = temp + "+"
                temp = temp + test_term
            else:
               temp = test_term
    #print temp
    new_term.append(temp)

titles = []
#create list of article id's
for did in article_ids:
    cur_record = medline_dict[did]
    titles.append(cur_record.title)

#titles = a list of all titles returned by PubMed
count = 0 #counter
bestterm = [0,"ERROR"]
for term in new_term:
    count = 0
    test = term.split("+") #split by "+"
    for test_term in test: #hit each word in term against title
        for title in titles:
            if str.upper(title).find(str.upper(test_term)) != -1: #if found the term in title
                count += 1
    if count > bestterm[0]:
        bestterm[0] = count
        bestterm[1] = term

#print bestterm[1] #bestterm[1] is the main disease name

if(bestterm[1] == "ERROR"): # if not hit
    print "Warning: There are no relevant mesh terms for the passed rs#!"
    sys.exit(0)

fh = open(os.path.join(os.curdir, "dx05.txt")) #the CA data file
line = fh.readline()
disease_name = bestterm[1] #INSERT DISEASE NAME HERE FOR TESTING
ICD9code = []
ICD9dName = []
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()

tempName = ""
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
        #print tempCode
        ICD9code.append(tempCode[0])
        w = lookup_line.find("</div>")
        if w != -1: #if there is a </div> marker (junk html)
            tempName = lookup_line[32:w]
            tempName = tempName[tempName.find(" ")+1:]
            #print tempName
            ICD9dName.append(tempName)
        else:
            tempName = lookup_line[32:len(lookup_line)-7]
            tempName = tempName[tempName.find(" ")+1:]
            #print tempName
            ICD9dName.append(tempName)
    lookup_line = readCode.readline()

####
#searching incidence data in CA data file
#note: returns class of hits
####
print "\n\n***Prevalance data per IDC9 above***"
print "Note: returns incidence data/yr, including subclasses\n"

fh = open(os.path.join(os.curdir, "dx05.txt")) #the CA data file

#new data structure: [0] = code, [1] = name, [2] = incidence
ICD9_prev_output= [[],[],[]]

for (code,name) in zip(ICD9code,ICD9dName):
    fh.seek(0)
    line = fh.readline()
    totalIncidence = 0
    sumCount = 0
    while line: #for every line in the data
        line = line[:-1] #remove /n
        lineVector = string.split(line, ',') #split to vector
        if lineVector[0].find(code) != -1: #if disease hit
            totalIncidence += int(lineVector[1])
            sumCount += 1
        else:
            if sumCount > 0:
                print "ID: ", code, "name: ", name, "incidence: ", totalIncidence
                ICD9_prev_output[0].append(code)
                ICD9_prev_output[1].append(name)
                ICD9_prev_output[2].append(totalIncidence)
                totalIncidence = 0
                sumCount = 0
        line = fh.readline()
fh.close()

#returns the most relevant ICD9 information

#THIS IS THE DATA YOU PROBABLY WANT
mainCode = 0
mainName = 0
mainIncidence = 0

counter = 0
for c,n,incid in zip(ICD9_prev_output[0], ICD9_prev_output[1], ICD9_prev_output[2]):
    if incid > counter:
        counter = incid
        mainCode = c
        mainName = n
        mainIncidence = incid

print "THE MOST RELEVANT HIT:\n"
print "CDC CODE: ", mainCode
print "Disease Name: ", mainName
print "Incidence/yr in CA: " , mainIncidence
    


OUTPUT:

INPUT:  rs11200638
***ICD9 and hits, arranged by importance***



***Prevalance data per IDC9 above***
Note: returns incidence data/yr, including subclasses

ID:  362.52 name:  Exudative senile macular degeneration incidence:  23
ID:  362.51 name:  Nonexudative senile macular degeneration incidence:  22
ID:  362.53 name:  Cystoid macular degeneration incidence:  6
ID:  362.50 name:  Macular degeneration (senile), unspecified incidence:  11338
ID:  362.5 name:  Degeneration of macula and posterior pole incidence:  11560
ID:  362.63 name:  Lattice degeneration incidence:  21
ID:  429.1 name:  Myocardial degeneration incidence:  49
ID:  363.32 name:  Other macular scars incidence:  6
ID:  743.55 name:  Congenital macular changes incidence:  2
ID:  371.55 name:  Macular corneal dystrophy incidence:  10
THE MOST RELEVANT HIT:

CDC CODE:  362.5
Disease Name:  Degeneration of macula and posterior pole
Incidence/yr in CA:  11560
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