Exemplo n.º 1
0
__author__ = 'Andrew'
import FakePatients as fp
import time
import matplotlib.pyplot as plt
import scipy.stats as stats
import math
from FakePatients import split_age

all_data = fp.load_data_with_sline()
# Here are sline which are presented almost every day (at least 360 days in year )
fit_sline = ['276', '070', '090', '390', '274', '135', '129', '250', '050', '255', '280', '283', '145', '065', '085'
             , '245', '262', '267', '125', '387', '165', '132', '296']
YEAR = 2012
FULL_YEARS = [2011, 2012, 2013]

def show_sline_freq(year=YEAR):
    """Prints sline counts for a year"""
    sline_freq = {}
    for tuple in all_data:
        sline = tuple[14]
        if sline is None:
            continue

        admit_date = tuple[2]
        if len(admit_date) == 0:
            continue

        datetime = time.strptime(admit_date, "%Y-%m-%d")
        if int(datetime.tm_year) != year:
            continue
Exemplo n.º 2
0
__author__ = 'Andrew'
import FakePatients as fp
import matplotlib.pyplot as plt
from sklearn.neighbors.kde import KernelDensity
import numpy as np
import random
import csv
from datetime import timedelta
import math

training_threshold = 10
alert_count = 50
raw_data, missed_drg = fp.load_data_with_sline()
data = fp.change_to_dict(fp.filter_incomplete_data(raw_data))
print "Rows with following DRG were skipped:",
print missed_drg
print "Filter out %d of %d" % (len(raw_data) - len(data), len(raw_data))

def history(all_data, (sex, age, sline), days=30):
    """Return historical data for selected combination of (sex, age, sline)"""
    start_date = None
    end_date = None
    hist_data = {}
    for row in all_data:
        admit_date = row[2]
        agef_in_years = int(row[9])
        agef = fp.split_age(agef_in_years)
        sexf = int(row[10])
        slinef = row[14]
        rlos = row[5]