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generate_results.py
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generate_results.py
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import pandas as pd
import numpy as np
from collections import Counter
import math
import json
from venn import venn
import matplotlib.pyplot as plt
df = pd.read_csv("output/input_descriptives.csv")
# population size
num_patients = len(df["patient_id"].unique())
with open("output/num_patients.json", "w") as fp:
json.dump(num_patients, fp)
# number of ae attendances
num_ae_attendances = Counter(df["ae_attendance_count"])
with open("output/num_ae_attendances.json", "w") as fp:
json.dump(num_ae_attendances, fp)
# emergency hospitalisation
num_patients = len(df["patient_id"].unique())
num_patients_hosp = len(df[df["hospital_admission"].notna()]["patient_id"].unique())
num_patients_hosp_emergency = len(
df[df["emergency_hospital_admission"].notna()]["patient_id"].unique()
)
num_patients_hosp_prim_covid = len(
df[df["primary_covid_hospital_admission"].notna()]["patient_id"].unique()
)
num_patients_hosp_emergency_prim_covid = len(
df[df["emergency_primary_covid_hospital_admission"].notna()]["patient_id"].unique()
)
num_patients_hosp_covid = len(
df[df["covid_hospital_admission"].notna()]["patient_id"].unique()
)
num_patients_hosp_emergency_covid = len(
df[df["emergency_covid_hospital_admission"].notna()]["patient_id"].unique()
)
emergency_hospitalisation_dict = {
"hospital_admission": num_patients_hosp,
"emergency_hospital_admission": num_patients_hosp_emergency,
"admisssion_primary_covid": num_patients_hosp_prim_covid,
"admission_secondary_covid": num_patients_hosp_covid,
"emergency_admission_primary_covid": num_patients_hosp_emergency_prim_covid,
"emergency_admission_secondary_covid": num_patients_hosp_emergency_covid,
}
with open("output/emergency_hospitalisation.json", "w") as fp:
json.dump(emergency_hospitalisation_dict, fp)
# a&e attendance all
num_patients_attended_ae = len(
df[(df["hospital_admission"].notna() & df["all_ae_attendance_any_discharge"] == 1)][
"patient_id"
].unique()
)
num_patients_attended_ae_with_discharge = len(
df[
(df["hospital_admission"].notna() & df["all_ae_attendance_with_discharge"] == 1)
]["patient_id"].unique()
)
num_patients_attended_ae_with_hosp_discharge = len(
df[
(df["hospital_admission"].notna() & df["all_ae_attendance_hosp_discharge"] == 1)
]["patient_id"].unique()
)
num_patients_attended_ae_cov = len(
df[(df["hospital_admission"].notna() & df["all_ae_attendance_covid_status"] == 1)][
"patient_id"
].unique()
)
num_patients_attended_ae_resp = len(
df[
(
df["hospital_admission"].notna()
& df["all_ae_attendance_respiratory_status"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_cov_pc = len(
df[
(
df["hospital_admission"].notna()
& df["all_covid_primary_care_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_pos_test = len(
df[
(
df["hospital_admission"].notna()
& df["all_positive_covid_test_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_pos_test_month = len(
df[
(
df["hospital_admission"].notna()
& df["all_positive_covid_test_month_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
all_ae_dict = {
"attended_ae": num_patients_attended_ae,
"attended_ae_with_discharge": num_patients_attended_ae_with_discharge,
"attended_ae_hosp_discharge": num_patients_attended_ae_with_hosp_discharge,
"attended_ae_cov": num_patients_attended_ae_cov,
"attended_ae_resp": num_patients_attended_ae_resp,
"attended_ae_cov_pc": num_patients_attended_ae_cov_pc,
"attended_ae_pos_test": num_patients_attended_ae_pos_test,
"attended_ae_pos_test_month": num_patients_attended_ae_pos_test_month,
}
with open("output/ae_all.json", "w") as fp:
json.dump(all_ae_dict, fp)
# a&e attendance in emergency hospital admissions
num_patients_attended_ae = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_ae_attendance_any_discharge"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_with_discharge = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_ae_attendance_with_discharge"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_with_hosp_discharge = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_ae_attendance_hosp_discharge"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_cov = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_ae_attendance_covid_status"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_resp = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_ae_attendance_respiratory_status"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_cov_pc = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_covid_primary_care_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_pos_test = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_positive_covid_test_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_pos_test_month = len(
df[
(
df["emergency_hospital_admission"].notna()
& df["any_positive_covid_test_month_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
any_ae_dict = {
"attended_ae": num_patients_attended_ae,
"attended_ae_with_discharge": num_patients_attended_ae_with_discharge,
"attended_ae_hosp_discharge": num_patients_attended_ae_with_hosp_discharge,
"attended_ae_cov": num_patients_attended_ae_cov,
"attended_ae_resp": num_patients_attended_ae_resp,
"attended_ae_cov_pc": num_patients_attended_ae_cov_pc,
"attended_ae_pos_test": num_patients_attended_ae_pos_test,
"attended_ae_pos_test_month": num_patients_attended_ae_pos_test_month,
}
with open("output/ae_any.json", "w") as fp:
json.dump(any_ae_dict, fp)
# a&e attendance in emergency covid hospital admissions
num_patients_attended_ae = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["ae_attendance_any_discharge"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_with_discharge = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["ae_attendance_with_discharge"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_cov = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["ae_attendance_covid_status"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_resp = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["ae_attendance_respiratory_status"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_cov_pc = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["covid_primary_care_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_pos_test = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["positive_covid_test_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
num_patients_attended_ae_pos_test_month = len(
df[
(
df["emergency_covid_hospital_admission"].notna()
& df["positive_covid_test_month_before_ae_attendance"]
== 1
)
]["patient_id"].unique()
)
ae_dict = {
"attended_ae": num_patients_attended_ae,
"attended_ae_with_discharge": num_patients_attended_ae_with_discharge,
"attended_ae_cov": num_patients_attended_ae_cov,
"attended_ae_resp": num_patients_attended_ae_resp,
"attended_ae_cov_pc": num_patients_attended_ae_cov_pc,
"attended_ae_pos_test": num_patients_attended_ae_pos_test,
"attended_ae_pos_test_month": num_patients_attended_ae_pos_test_month,
}
with open("output/ae.json", "w") as fp:
json.dump(ae_dict, fp)
# discharge destination in those with primary cov in emergency hosp admissions (who went to ae)
prim_cov_ae_discharge = df[
(
df["emergency_covid_hospital_admission"].notna()
& df["ae_attendance_any_discharge"]
== 1
)
]["discharge_destination"]
missing = prim_cov_ae_discharge.isna().sum()
destination_dict = Counter(prim_cov_ae_discharge[prim_cov_ae_discharge.notnull()])
destination_dict["missing"] = missing
discharge_dict = {
1066341000000100: "Ambulatory Emergency Care",
19712007: "Patient Transfer",
183919006: "Hospice",
1066361000000104: "High dependency unit",
305398007: "Mortuary",
1066381000000108: "Special baby care unit",
1066331000000109: "Emergency department short stay ward",
306705005: "Police custody",
306706006: "Ward",
306689006: "Home",
306694006: "Nursing Home",
306691003: "Residential Home",
1066351000000102: "Hospital at home",
1066401000000108: "Neonatal ICU",
1066371000000106: "Coronary Care Unit",
50861005: "Legal Custody",
1066391000000105: "ICU",
"missing": "missing",
}
percent_dict = {}
data = []
total = 0
other_count = 0
drop_keys = []
# Drop dictionary pairs if value <10
for key, value in destination_dict.items():
if value < 10:
other_count += value
drop_keys.append(key)
for key in drop_keys:
del destination_dict[key]
destination_dict["Other"] = other_count
for key, value in destination_dict.items():
total += value
for key, value in destination_dict.items():
if key == "Other":
percent = (value / total) * 100
row = ["Other", percent]
data.append(row)
else:
percent = (value / total) * 100
row = [discharge_dict[key], percent]
data.append(row)
discharge_destination_df = pd.DataFrame(data, columns=["Discharge Destination", "%"])
discharge_destination_df.to_csv("output/discharge_destination.csv")
# models
df = pd.read_csv("output/input.csv")
positive_covid_patients_sus = df[
df["emergency_primary_covid_hospital_admission"].notna()
]
negative_covid_patients_sus = df[
~df["emergency_primary_covid_hospital_admission"].notna()
]
# model_a
positive_covid_patients_a = df[(df["ae_attendance_hosp_discharge"] == 1)]
negative_covid_patients_a = df[(df["ae_attendance_hosp_discharge"] == 0)]
sus_patients_positive = set(list(positive_covid_patients_sus["patient_id"]))
model_a_patients_positive = set(list(positive_covid_patients_a["patient_id"]))
sus_patients_negative = set(list(negative_covid_patients_sus["patient_id"]))
model_a_patients_negative = set(list(negative_covid_patients_a["patient_id"]))
print(len(set(sus_patients_positive)))
sus_pos_ecds_pos = len(
list(set(sus_patients_positive) & set(model_a_patients_positive))
)
sus_pos_ecds_neg = len(
list(set(sus_patients_positive) & set(model_a_patients_negative))
)
sus_neg_ecds_pos = len(
list(set(sus_patients_negative) & set(model_a_patients_positive))
)
sus_neg_ecds_neg = len(
list(set(sus_patients_negative) & set(model_a_patients_negative))
)
sensitivity_a = (sus_pos_ecds_pos / (sus_pos_ecds_pos + sus_pos_ecds_neg)) * 100
specificity_a = (sus_neg_ecds_neg / (sus_neg_ecds_pos + sus_neg_ecds_neg)) * 100
PPV_a = (sus_pos_ecds_pos / (sus_pos_ecds_pos + sus_neg_ecds_pos)) * 100
NPV_a = (sus_neg_ecds_neg / (sus_neg_ecds_neg + sus_pos_ecds_neg)) * 100
MCC_a = (
(sus_pos_ecds_pos * sus_neg_ecds_neg) - (sus_neg_ecds_pos * sus_pos_ecds_neg)
) / math.sqrt(
(sus_pos_ecds_pos + sus_neg_ecds_pos)
* (sus_pos_ecds_neg + sus_neg_ecds_neg)
* (sus_pos_ecds_pos + sus_pos_ecds_neg)
* (sus_neg_ecds_pos + sus_neg_ecds_neg)
)
output = pd.DataFrame(
[
[sus_pos_ecds_pos, sus_neg_ecds_pos, (sus_pos_ecds_pos + sus_neg_ecds_pos)],
[sus_pos_ecds_neg, sus_neg_ecds_neg, (sus_pos_ecds_neg + sus_neg_ecds_neg)],
[
(sus_pos_ecds_pos + sus_pos_ecds_neg),
(sus_neg_ecds_pos + sus_neg_ecds_neg),
(sus_pos_ecds_pos + sus_pos_ecds_neg + sus_neg_ecds_pos + sus_neg_ecds_neg),
],
],
columns=["SUS-positive", "SUS-negative", "Total"],
index=["ECDS-positive", "ECDS-negative", "Total"],
)
output.to_csv("output/model_a.csv")
# model_b
positive_covid_patients_b = df[
(df["ae_attendance_hosp_discharge"] == 1) & (df["ae_attendance_covid_status"] == 1)
]
negative_covid_patients_b = df[
df["ae_attendance_hosp_discharge"]
== 0
| (
(df["ae_attendance_hosp_discharge"] == 1)
& (df["ae_attendance_covid_status"] == 0)
)
]
model_b_patients_positive = set(list(positive_covid_patients_b["patient_id"]))
model_b_patients_negative = set(list(negative_covid_patients_b["patient_id"]))
sus_pos_ecds_pos = len(
list(set(sus_patients_positive) & set(model_b_patients_positive))
)
sus_pos_ecds_neg = len(
list(set(sus_patients_positive) & set(model_b_patients_negative))
)
sus_neg_ecds_pos = len(
list(set(sus_patients_negative) & set(model_b_patients_positive))
)
sus_neg_ecds_neg = len(
list(set(sus_patients_negative) & set(model_b_patients_negative))
)
sensitivity_b = (sus_pos_ecds_pos / (sus_pos_ecds_pos + sus_pos_ecds_neg)) * 100
specificity_b = (sus_neg_ecds_neg / (sus_neg_ecds_pos + sus_neg_ecds_neg)) * 100
PPV_b = (sus_pos_ecds_pos / (sus_pos_ecds_pos + sus_neg_ecds_pos)) * 100
NPV_b = (sus_neg_ecds_neg / (sus_neg_ecds_neg + sus_pos_ecds_neg)) * 100
MCC_b = (
(sus_pos_ecds_pos * sus_neg_ecds_neg) - (sus_neg_ecds_pos * sus_pos_ecds_neg)
) / math.sqrt(
(sus_pos_ecds_pos + sus_neg_ecds_pos)
* (sus_pos_ecds_neg + sus_neg_ecds_neg)
* (sus_pos_ecds_pos + sus_pos_ecds_neg)
* (sus_neg_ecds_pos + sus_neg_ecds_neg)
)
output = pd.DataFrame(
[
[sus_pos_ecds_pos, sus_neg_ecds_pos, (sus_pos_ecds_pos + sus_neg_ecds_pos)],
[sus_pos_ecds_neg, sus_neg_ecds_neg, (sus_pos_ecds_neg + sus_neg_ecds_neg)],
[
(sus_pos_ecds_pos + sus_pos_ecds_neg),
(sus_neg_ecds_pos + sus_neg_ecds_neg),
(sus_pos_ecds_pos + sus_pos_ecds_neg + sus_neg_ecds_pos + sus_neg_ecds_neg),
],
],
columns=["SUS-positive", "SUS-negative", "Total"],
index=["ECDS-positive", "ECDS-negative", "Total"],
)
output.to_csv("output/model_b.csv")
# model_c
positive_covid_patients_c = df[
(df["ae_attendance_hosp_discharge"] == 1)
& (
(df["ae_attendance_covid_status"] == 1)
| (
(df["ae_attendance_respiratory_status"] == 1)
& (df["positive_covid_test_before_ae_attendance"] == 1)
)
)
]
negative_covid_patients_c = df[
df["ae_attendance_hosp_discharge"]
== 0
| (
(df["ae_attendance_hosp_discharge"] == 1)
& ~(
(df["ae_attendance_covid_status"] == 1)
| (
(df["ae_attendance_respiratory_status"] == 1)
& (df["positive_covid_test_before_ae_attendance"] == 1)
)
)
)
]
model_c_patients_positive = set(list(positive_covid_patients_c["patient_id"]))
model_c_patients_negative = set(list(negative_covid_patients_c["patient_id"]))
sus_pos_ecds_pos = len(
list(set(sus_patients_positive) & set(model_c_patients_positive))
)
sus_pos_ecds_neg = len(
list(set(sus_patients_positive) & set(model_c_patients_negative))
)
sus_neg_ecds_pos = len(
list(set(sus_patients_negative) & set(model_c_patients_positive))
)
sus_neg_ecds_neg = len(
list(set(sus_patients_negative) & set(model_c_patients_negative))
)
sensitivity_c = (sus_pos_ecds_pos / (sus_pos_ecds_pos + sus_pos_ecds_neg)) * 100
specificity_c = (sus_neg_ecds_neg / (sus_neg_ecds_pos + sus_neg_ecds_neg)) * 100
PPV_c = (sus_pos_ecds_pos / (sus_pos_ecds_pos + sus_neg_ecds_pos)) * 100
NPV_c = (sus_neg_ecds_neg / (sus_neg_ecds_neg + sus_pos_ecds_neg)) * 100
MCC_c = (
(sus_pos_ecds_pos * sus_neg_ecds_neg) - (sus_neg_ecds_pos * sus_pos_ecds_neg)
) / math.sqrt(
(sus_pos_ecds_pos + sus_neg_ecds_pos)
* (sus_pos_ecds_neg + sus_neg_ecds_neg)
* (sus_pos_ecds_pos + sus_pos_ecds_neg)
* (sus_neg_ecds_pos + sus_neg_ecds_neg)
)
output = pd.DataFrame(
[
[sus_pos_ecds_pos, sus_neg_ecds_pos, (sus_pos_ecds_pos + sus_neg_ecds_pos)],
[sus_pos_ecds_neg, sus_neg_ecds_neg, (sus_pos_ecds_neg + sus_neg_ecds_neg)],
[
(sus_pos_ecds_pos + sus_pos_ecds_neg),
(sus_neg_ecds_pos + sus_neg_ecds_neg),
(sus_pos_ecds_pos + sus_pos_ecds_neg + sus_neg_ecds_pos + sus_neg_ecds_neg),
],
],
columns=["SUS-positive", "SUS-negative", "Total"],
index=["ECDS-positive", "ECDS-negative", "Total"],
)
output.to_csv("output/model_c.csv")
venn(
{
"Satisfying A&E attendance": model_c_patients_positive,
"Emergency primary COVID-19 hospital admission": sus_patients_positive,
}
)
plt.savefig("output/venn.jpeg")
# dictionary of model results
performance_dict = {
"A": {
"sensitivity": round(sensitivity_a, 2),
"specificity": round(specificity_a, 2),
"ppv": round(PPV_a, 2),
"npv": round(NPV_a, 2),
"mcc": round(MCC_a, 2),
},
"B": {
"sensitivity": round(sensitivity_b, 2),
"specificity": round(specificity_b, 2),
"ppv": round(PPV_b, 2),
"npv": round(NPV_b, 2),
"mcc": round(MCC_b, 2),
},
"C": {
"sensitivity": round(sensitivity_c, 2),
"specificity": round(specificity_c, 2),
"ppv": round(PPV_c, 2),
"npv": round(NPV_c, 2),
"mcc": round(MCC_c, 2),
},
}
with open("output/model_performance_updated.json", "w") as fp:
json.dump(performance_dict, fp)
# sensitivity
sensitivity_dict = {}
# drop recent pos test and resp
positive_covid_patients = df[
(df["ae_attendance_hosp_discharge"] == 1) & (df["ae_attendance_covid_status"] == 1)
]
negative_covid_patients = df[
df["ae_attendance_hosp_discharge"]
== 0
| (
(df["ae_attendance_hosp_discharge"] == 1)
& (df["ae_attendance_covid_status"] == 0)
)
]
model_patients_positive = set(list(positive_covid_patients["patient_id"]))
model_patients_negative = set(list(negative_covid_patients["patient_id"]))
sus_pos_ecds_pos = len(list(set(sus_patients_positive) & set(model_patients_positive)))
sus_pos_ecds_neg = len(list(set(sus_patients_positive) & set(model_patients_negative)))
sus_neg_ecds_pos = len(list(set(sus_patients_negative) & set(model_patients_positive)))
sus_neg_ecds_neg = len(list(set(sus_patients_negative) & set(model_patients_negative)))
MCC = (
(sus_pos_ecds_pos * sus_neg_ecds_neg) - (sus_neg_ecds_pos * sus_pos_ecds_neg)
) / math.sqrt(
(sus_pos_ecds_pos + sus_neg_ecds_pos)
* (sus_pos_ecds_neg + sus_neg_ecds_neg)
* (sus_pos_ecds_pos + sus_pos_ecds_neg)
* (sus_neg_ecds_pos + sus_neg_ecds_neg)
)
sensitivity_dict["pos_test"] = MCC
# drop cov_code
positive_covid_patients = df[
(df["ae_attendance_hosp_discharge"] == 1)
& (
(df["ae_attendance_respiratory_status"] == 1)
& (df["positive_covid_test_before_ae_attendance"] == 1)
)
]
negative_covid_patients = df[
df["ae_attendance_hosp_discharge"]
== 0
| (
(df["ae_attendance_hosp_discharge"] == 1)
& (
(df["ae_attendance_respiratory_status"] == 0)
| (df["positive_covid_test_before_ae_attendance"] == 0)
)
)
]
model_patients_positive = set(list(positive_covid_patients["patient_id"]))
model_patients_negative = set(list(negative_covid_patients["patient_id"]))
sus_pos_ecds_pos = len(list(set(sus_patients_positive) & set(model_patients_positive)))
sus_pos_ecds_neg = len(list(set(sus_patients_positive) & set(model_patients_negative)))
sus_neg_ecds_pos = len(list(set(sus_patients_negative) & set(model_patients_positive)))
sus_neg_ecds_neg = len(list(set(sus_patients_negative) & set(model_patients_negative)))
MCC = (
(sus_pos_ecds_pos * sus_neg_ecds_neg) - (sus_neg_ecds_pos * sus_pos_ecds_neg)
) / math.sqrt(
(sus_pos_ecds_pos + sus_neg_ecds_pos)
* (sus_pos_ecds_neg + sus_neg_ecds_neg)
* (sus_pos_ecds_pos + sus_pos_ecds_neg)
* (sus_neg_ecds_pos + sus_neg_ecds_neg)
)
sensitivity_dict["cov_code"] = MCC
with open("output/sensitivity.json", "w") as fp:
json.dump(sensitivity_dict, fp)