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app.py

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+import joblib
+import pandas as pd
+import streamlit as st
+from streamlit_shap import st_shap
+import shap
+import numpy as np
+
+# Load Model
+model1 = joblib.load('saved_model/model1.pkl')
+predictor = model1
+st.title('Prediction of postoperative delirium')
+st.write('#### For geriatric patients undergoing cardiac surgery')
+
+asa_options = {
+    "Ⅰ": -1.38814621350096,
+    "Ⅱ": 0.511717257330668,
+    "Ⅲ": -0.395963387679586,
+    "Ⅳ": -1.4681889211745,
+    "Ⅴ": -1.4681889211745
+}
+hypertension_options = {
+    "No": 3.68035036162672,
+    "Ⅰ": -0.544235316554776,
+    "Ⅱ": -0.844034612101507,
+    "Ⅲ": -0.943664453050348
+}
+dm_options = {
+    "No": 0.122040527036218,
+    "Non-insulin dependent": -0.638909566289975,
+    "Insulin dependent": -1.7072632945609
+}
+illness_options = {
+    "Yes": 1,
+    "No": 0
+}
+os_options = {
+    "": 2.70805020110221,
+    "Dermatological surgery": 2.22692108269007,
+    "Limb surgery": 0.752566153571913,
+    "Arthroplasty": 0.532164318353033,
+    "Spinal surgery": 1.6127011607198,
+    "Head and neck surgery": 2.52541407124607,
+    "Upper abdomen surgery": -0.649878597660549,
+    "Lower abdomen surgery": -0.464370149938076,
+    "Abdomen surgery": -2.20389571615324,
+    "Thoracic surgery": 0.416954872664665,
+    "cranial surgery": 0.0899556968720493,
+    "Thoracoabdominal surgery": 0.0899556968720493,
+    "Vascular surgery": 0.281010933634758
+}
+es_options = {
+    "No": 0.0175056050012399,
+    "yes": -1.97901454494049
+}
+eot_options = {
+    "": 2.944439,
+    "1h": 0.768487407184522,
+    "1h-1.5h": 0.523271451886264,
+    "1.5-2h": -0.170387892577323,
+    "2h-3h": -0.345346138774783,
+    ">=3h": -0.749296055496119
+}
+up_options = {
+    "": 3.59497984361465,
+    "No": 0.0374632790768398,
+    "+/-": -0.465955759499621,
+    "1+": -1.18050684872272,
+    "2+": -1.18050684872272,
+    "3+": -1.4681889211745
+}
+
+sccl_zh = '血清胱抑素C测定'
+asa_zh = 'ASA分级'
+hypertension_zh = '高血压_分级'
+dm_zh = '糖尿病_控制方式'
+os_zh = '手术风险评估_手术部位'
+es_zh = '急诊'
+eot_zh = '手术时长_分钟'
+up_zh = '尿蛋白定性'
+coovorbcdw_zh = 'RBC分布宽度CV'
+inr_zh = '国际标准化比值'
+
+sccl_en = 'Serum cystatin C level (mg/L)'
+asa_en = 'ASA classification'
+hypertension_en = 'Hypertension'
+dm_en = 'Diabetes mellitus'
+os_en = 'Operation site'
+es_en = 'Emergency surgery'
+eot_en = 'Estimated operation time'
+up_en = 'Urine protein'
+coovorbcdw_en = 'RDW-CV (%)'
+inr_en = 'International normalized ratio'
+
+# Form
+with st.form(key='form_parameters'):
+    col1, col2 = st.columns(2)  # Create two columns
+
+    # First column
+    with col1:
+        sccl = st.number_input('Serum cystatin C level (mg/L)')
+        asa = st.selectbox('ASA classification', list(asa_options.keys()))
+        asa_woe = asa_options.get(asa, 0)
+        hypertension = st.selectbox('Hypertension', list(hypertension_options.keys()))
+        hypertension_woe = hypertension_options.get(hypertension, 0)
+        dm = st.selectbox('Diabetes mellitus', list(dm_options.keys()))
+        dm_woe = dm_options.get(dm, 0)
+        os = st.selectbox('Operation site', list(os_options.keys()))
+        os_woe = os_options.get(os, 0)
+        es = st.selectbox('Emergency surgery', list(es_options.keys()))
+        es_woe = es_options.get(es, 0)
+
+        sccl_woe = 0
+        if sccl == '':
+            sccl_woe = 2.99885240089024
+            sccl = np.nan
+        elif float(sccl) < 0.87:
+            sccl_woe = 0.645372775418255
+        elif (float(sccl) >= 0.87) and (float(sccl) < 0.95):
+            sccl_woe = 0.369025188040581
+        elif (float(sccl) >= 0.95) and (float(sccl) < 1.03):
+            sccl_woe = -0.10610555179212
+        elif (float(sccl) >= 1.03) and (float(sccl) < 1.13):
+            sccl_woe = 0.0898098195273885
+        elif float(sccl) >= 1.13:
+            sccl_woe = -0.925496780781878
+        sccl = float(sccl)
+
+
+    # Second column
+    with col2:
+        eot = st.selectbox('Estimated operation time', list(eot_options.keys()))
+        eot_woe = eot_options.get(eot, 0)
+        up = st.selectbox('Urine protein', list(up_options.keys()))
+        up_woe = up_options.get(up, 0)
+        coovorbcdw = st.number_input('RDW-CV (%)')
+        inr = st.number_input('International normalized ratio')
+
+        coovorbcdw_woe = 0
+        if coovorbcdw == '':
+            coovorbcdw_woe = 3.34041249059303
+            coovorbcdw = np.nan
+        elif float(coovorbcdw) < 12.7:
+            coovorbcdw_woe = 0.398231895412054
+        elif (float(coovorbcdw) >= 12.7) and (float(coovorbcdw) < 13.3):
+            coovorbcdw_woe = 0.048813472291148
+        elif (float(coovorbcdw) >= 13.3) and (float(coovorbcdw) < 13.7):
+            coovorbcdw_woe = -0.104034700025362
+        elif (float(coovorbcdw) >= 13.7) and (float(coovorbcdw) < 14.6):
+            coovorbcdw_woe = -0.0861214293356946
+        elif float(coovorbcdw) >= 14.6:
+            coovorbcdw_woe = -0.646032944021757
+        coovorbcdw = float(coovorbcdw)
+
+        inr_woe = 0
+        if inr == '':
+            inr_woe = 3.38627173144548
+            inr = np.nan
+        elif float(inr) < 0.92:
+            inr_woe = -0.000131790754127941
+        elif (float(inr) >= 0.92) and (float(inr) < 0.95):
+            inr_woe = 0.383156538484155
+        elif (float(inr) >= 0.95) and (float(inr) < 0.98):
+            inr_woe = 0.282044225138759
+        elif (float(inr) >= 0.98) and (float(inr) < 1.03):
+            inr_woe = -0.104889279197154
+        elif float(inr) >= 1.03:
+            inr_woe = -0.769604383885953
+        inr = float(inr)
+
+    st.markdown('---')
+    submitted = st.form_submit_button('Predict')
+
+# 构建预测样本（原始数据）
+data1 = {
+    hypertension_zh: hypertension,
+    up_zh: up,
+    dm_zh: dm,
+    os_zh: os,
+    asa_zh: asa,
+    eot_zh: eot,
+    sccl_zh: sccl,
+    coovorbcdw_zh: coovorbcdw,
+    inr_zh: inr,
+    es_zh: es
+}
+X_train_1 = pd.DataFrame(data1, index=[0])
+
+data2 = {
+    hypertension_zh: hypertension_woe,
+    up_zh: up_woe,
+    dm_zh: dm_woe,
+    os_zh: os_woe,
+    asa_zh: asa_woe,
+    eot_zh: eot_woe,
+    sccl_zh: sccl_woe,
+    coovorbcdw_zh: coovorbcdw_woe,
+    inr_zh: inr_woe,
+    es_zh: es_woe
+}
+X_train = pd.DataFrame(data2, index=[0])
+
+if submitted:
+    # Predict
+    y_prob_train = model1.predict_proba(X_train)
+
+    value = y_prob_train[0][0]
+    with st.container():
+        st.write('### The possibility of xx is \n'+ str(value))
+        st.write('*Threshold is xx*')
+        st.markdown('---')
+    with st.container():
+        st.write('## SHAP force plot for this patient')
+
+    explainer = shap.TreeExplainer(model1)
+    shap_values = explainer.shap_values(X_train)  # 这里的X_train替换成输进来的值,要woe版的数据，不是原始版数据，有个对应文件用来映射数值与woe
+    print(shap_values.shape)
+    X1_ = X_train_1.fillna(0)  # 这里替换成输进来的值的原始数据
+    # 替换英文名
+    X1_.columns = [hypertension_en, up_en, dm_en, os_en, asa_en, eot_en, sccl_en, coovorbcdw_en, inr_en, es_en]
+
+    # Visualize SHAP values
+    # Visualize SHAP values
+    st_shap(shap.force_plot(explainer.expected_value, shap_values[0], X1_.iloc[0]), height=400, width=1000)

saved_model/model1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ddc52af2cc3e719e73b40648f9022163329649d9d32131ac2c6ec299a78f802a
+size 72508