File size: 5,159 Bytes
fba85dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
import streamlit as st
import streamlit.components.v1 as components
import numpy as np
import pickle
import pandas as pd
pd.set_option('display.max_columns', 500)
def impute_missing_values(df_,dict_impute):
return df_.fillna(dict_impute)
to_rescale_ = ['diff_MS','MS_d','pr_pre_tavi',
'surface_systole','MS_s','age',
'ncc_calcif_n','calc_risque_n']
to_encode_ = ['syncope_Oui', 'lcc_calc_1.0', 'bloc_branche_pre_bbd']
st.title("Pacemaker Implantation (IPM) risk prediction")
st.subheader(f':red[Pre procedural] Tavi implantation model')
cols = to_rescale_+to_encode_
with st.form(key='cols_form'):
c1,c2= st.columns(2)
c3,c4 = st.columns(2)
c5,c6,c7,c8 = st.columns(4)
c9,c10,c11 = st.columns(3)
with c1:
age = st.number_input('Age (year)',value=83)
with c2:
syncope_Oui = st.selectbox("Syncope",key="syncope",options=["Yes", "No"])
with c3:
pr_pre_tavi = st.number_input('Pre-TAVI PR time (ms)',value=186)
with c4:
bloc_branche_pre_bbd = st.selectbox("Right bundle branch block",key="prebbd",options=["Yes", "No"])
with c5:
lcc_calc_1 = st.selectbox("LCC calcification grade",key='lcccalc1',options=[0,1,2,3])
with c6:
ncc_calcif_n = st.selectbox("NCC calcification grade",key='nccalcn',options=[0,1,2,3])
with c7:
rcc_calcif_n = st.selectbox("RCC calcification grade",key='rccalcn',options=[0,1,2,3])
with c8:
isc_calcif_n = st.selectbox("IS calcification",key='isccalcn',options=["Yes", "No"])
with c9:
MS_s = st.number_input('Systolic membranous septal length(mm)',value=8.0)
with c10:
MS_d = st.number_input('Diastolic membranous septal length (mm)',value=8.9)
with c11:
surface_systole = st.number_input('Systolic aortic annular surface (mm²)',value=483)
submitButton = st.form_submit_button(label = 'Predict')
#load model, set cache to prevent reloading
@st.cache_resource()
def load_model():
with open(r"models/ensemble_models_pre.pkl", "rb") as input_file:
models = pickle.load(input_file)
return models
#@st.cache_resource()
def load_scaler():
with open(r"models/ensemble_scaler_pre.pkl", "rb") as input_file:
scalers = pickle.load(input_file)
return scalers
@st.cache_resource()
def load_impute():
with open(r"models/ensemble_dict_impute_pre.pkl", "rb") as input_file:
dicts_impute = pickle.load(input_file)
return dicts_impute
with st.spinner("Loading Model...."):
models = load_model()
dicts_impute = load_impute()
scalers = load_scaler()
#st.write("Predicting Class...")
with st.spinner("Prediction..."):
# preprocessing
diff_MS = MS_d - MS_s
lcc_calc_1 = 1 if lcc_calc_1 == 1 else 0
syncope_Oui = 1 if syncope_Oui == "Yes" else 0
isc_calcif_n = 1 if isc_calcif_n == "Yes" else 0
bloc_branche_pre_bbd = 1 if bloc_branche_pre_bbd == "Yes" else 0
calc_risque_n = ncc_calcif_n + rcc_calcif_n + isc_calcif_n
age = int(age)*365
bool_warning = False
if MS_d<1.5 or MS_d>15.30:
bool_warning = True
if MS_s<2.0 or MS_s> 14.61:
bool_warning = True
if pr_pre_tavi<90 or pr_pre_tavi>344:
bool_warning = True
if surface_systole<300 or surface_systole>724:
bool_warning = True
if age<21156 or age>34939:
bool_warning = True
if bool_warning:
st.write(":warning: Warning Results might not be reliable because:")
if MS_d<1.5 or MS_d>15.30:
st.write("Our study population had a dMS (mm) between 1.5 and 15.30 (mm)")
if MS_s<2.0 or MS_s> 14.61:
st.write("Our study population had a sMS (mm) between 2 and 14.61 (mm) ")
if pr_pre_tavi<90 or pr_pre_tavi>344:
st.write("Our study population had a pre-TAVI PR surface between 90 and 344 (ms)")
if surface_systole<300 or surface_systole>724:
st.write("Our study population had a systolic aortic annular surface between 300 and 724 (mm²) ")
if age<21156 or age>34939:
st.write("Our study population had an age between 58 and 95 years old ")
pred_arr = np.array([[diff_MS,MS_d,pr_pre_tavi,
surface_systole,MS_s,age,
ncc_calcif_n,calc_risque_n,
syncope_Oui, lcc_calc_1, bloc_branche_pre_bbd]])
pred_df = pd.DataFrame(pred_arr,columns=cols)
print(pred_df.head())
pred_dfs = [impute_missing_values(pred_df,dict_impute) for dict_impute in dicts_impute]
dfs_scaled_ = [scaler.transform(df_[to_rescale_]) for df_,scaler in zip(pred_dfs,scalers)]
dfs_scaled = [pd.DataFrame(columns=to_rescale_,data=df_scaled_) for df_scaled_ in dfs_scaled_]
dfs_cat = [pd.concat([df_scaled,pred_df[to_encode_]],axis=1) for df_scaled in dfs_scaled]
pred= round( np.array([model.predict_proba(pred_df_)[0][1] for pred_df_,model in zip(dfs_cat,models)]).mean()*100,2)
if pred>90:
st.write("PMI risk probability >90 %")
elif pred<10:
st.write("PMI risk probability <10 %")
else:
st.write("PMI risk probability :",pred,' %')
|