Update app.py

app.py

@@ -1,6 +1,10 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+import csv
+import io
+import random
+from datetime import datetime, timedelta
 
 # Seed for reproducibility
 np.random.seed(42)
@@ -8,128 +12,40 @@ np.random.seed(42)
 # Function to generate synthetic BreastCancer data
 def generate_breast_cancer_data(num_patients):
     primary_keys = [f"PPK_{i+1:05d}" for i in range(num_patients)]
-    
-    ages = []
-    menopausal_status = []
-    tumor_sizes = []
-    lymph_nodes = []
-    grades = []
-    stages = []
-    er_status = []
-    pr_status = []
-    her2_status = []
-    ki67_level = []
-    tnbc_status = []
-    brca_mutation = []
-    overall_health = []
-    genomic_score = []
-    treatment = []
-
-    for i in range(num_patients):
-        age = int(np.random.normal(60, 10))
-        age = max(30, min(age, 80))
-        ages.append(age)
-
-        menopausal = "Post-menopausal" if age >= 50 else "Pre-menopausal"
-        menopausal_status.append(menopausal)
-
-        tumor_size = round(np.random.lognormal(mean=0.7, sigma=0.5), 2)
-        tumor_sizes.append(tumor_size)
-
-        lymph_node = (
-            "Positive"
-            if (tumor_size > 2.0 and np.random.rand() < 0.6)
-            or (tumor_size <= 2.0 and np.random.rand() < 0.3)
-            else "Negative"
-        )
-        lymph_nodes.append(lymph_node)
-
-        grade = np.random.choice([1, 2, 3], p=[0.1, 0.4, 0.5] if tumor_size > 2.0 else [0.3, 0.5, 0.2])
-        grades.append(grade)
-
-        if tumor_size <= 2.0 and lymph_node == "Negative":
-            stage = "I"
-        elif (tumor_size > 2.0 and tumor_size <= 5.0) and lymph_node == "Negative":
-            stage = "II"
-        elif lymph_node == "Positive" or tumor_size > 5.0:
-            stage = "III"
-        else:
-            stage = "II"
-        if np.random.rand() < 0.05:
-            stage = "IV"
-        stages.append(stage)
-
-        er = np.random.choice(["Positive", "Negative"], p=[0.75, 0.25])
-        pr = "Positive" if er == "Positive" and np.random.rand() > 0.1 else "Negative"
-        er_status.append(er)
-        pr_status.append(pr)
-
-        her2 = np.random.choice(["Positive", "Negative"], p=[0.3, 0.7] if grade == 3 else [0.15, 0.85])
-        her2_status.append(her2)
-
-        ki67 = "High" if grade == 3 and np.random.rand() < 0.8 else "Low"
-        ki67_level.append(ki67)
-
-        tnbc = "Positive" if er == "Negative" and pr == "Negative" and her2 == "Negative" else "Negative"
-        tnbc_status.append(tnbc)
-
-        brca = "Positive" if (tnbc == "Positive" or age < 40) and np.random.rand() < 0.2 else "Negative"
-        brca_mutation.append(brca)
-
-        health = "Good" if age < 65 and np.random.rand() < 0.9 else "Poor"
-        overall_health.append(health)
-
-        recurrence_score = (
-            np.random.choice(["Low", "Intermediate", "High"], p=[0.6, 0.3, 0.1])
-            if er == "Positive" and her2 == "Negative"
-            else "N/A"
-        )
-        genomic_score.append(recurrence_score)
-
-        if stage in ["I", "II"]:
-            if tnbc == "Positive":
-                treat = "Surgery, Chemotherapy, and Radiation Therapy"
-            elif er == "Positive" and recurrence_score != "N/A":
-                if recurrence_score == "High":
-                    treat = "Surgery, Chemotherapy, Hormone Therapy, and Radiation Therapy"
-                elif recurrence_score == "Intermediate":
-                    treat = "Surgery, Consider Chemotherapy, Hormone Therapy, and Radiation Therapy"
-                else:
-                    treat = "Surgery, Hormone Therapy, and Radiation Therapy"
-            elif her2 == "Positive":
-                treat = "Surgery, HER2-Targeted Therapy, Chemotherapy, and Radiation Therapy"
-            else:
-                treat = "Surgery, Chemotherapy, and Radiation Therapy"
-        elif stage == "III":
-            treat = (
-                "Neoadjuvant Chemotherapy, Surgery, Radiation Therapy"
-                + (", HER2-Targeted Therapy" if her2 == "Positive" else "")
-                + (", Hormone Therapy" if er == "Positive" else "")
-            )
-        else:
-            treat = "Systemic Therapy (Palliative Care)"
-        treatment.append(treat)
-
-    breast_cancer_data = {
+    ages = np.random.randint(30, 80, size=num_patients)
+    menopausal_status = ["Post-menopausal" if age >= 50 else "Pre-menopausal" for age in ages]
+    tumor_sizes = np.round(np.random.lognormal(mean=0.7, sigma=0.5, size=num_patients), 2)
+    lymph_nodes = np.random.choice(["Positive", "Negative"], size=num_patients, p=[0.4, 0.6])
+    tumor_grades = np.random.choice([1, 2, 3], size=num_patients, p=[0.3, 0.5, 0.2])
+    tumor_stages = np.random.choice(["I", "II", "III", "IV"], size=num_patients, p=[0.4, 0.3, 0.2, 0.1])
+    er_status = np.random.choice(["Positive", "Negative"], size=num_patients, p=[0.75, 0.25])
+    pr_status = np.random.choice(["Positive", "Negative"], size=num_patients, p=[0.7, 0.3])
+    her2_status = np.random.choice(["Positive", "Negative"], size=num_patients, p=[0.3, 0.7])
+    ki67_levels = np.random.choice(["High", "Low"], size=num_patients, p=[0.6, 0.4])
+    tnbc_status = ["Positive" if er == "Negative" and pr == "Negative" and her2 == "Negative" else "Negative" for er, pr, her2 in zip(er_status, pr_status, her2_status)]
+    brca_mutation = np.random.choice(["Positive", "Negative"], size=num_patients, p=[0.1, 0.9])
+    overall_health = np.random.choice(["Good", "Poor"], size=num_patients, p=[0.7, 0.3])
+    genomic_score = np.random.choice(["Low", "Intermediate", "High", "N/A"], size=num_patients, p=[0.3, 0.2, 0.1, 0.4])
+    treatments = np.random.choice(["Surgery", "Chemotherapy", "Radiation Therapy"], size=num_patients)
+
+    return pd.DataFrame({
         "PRIMARY_PERSON_KEY": primary_keys,
         "Age": ages,
         "Menopausal Status": menopausal_status,
         "Tumor Size (cm)": tumor_sizes,
         "Lymph Node Involvement": lymph_nodes,
-        "Tumor Grade": grades,
-        "Tumor Stage": stages,
+        "Tumor Grade": tumor_grades,
+        "Tumor Stage": tumor_stages,
         "ER Status": er_status,
         "PR Status": pr_status,
         "HER2 Status": her2_status,
-        "Ki-67 Level": ki67_level,
+        "Ki-67 Level": ki67_levels,
         "TNBC Status": tnbc_status,
         "BRCA Mutation": brca_mutation,
         "Overall Health": overall_health,
         "Genomic Recurrence Score": genomic_score,
-        "Treatment": treatment,
-    }
-
-    return pd.DataFrame(breast_cancer_data)
+        "Treatment": treatments
+    })
 
 # Function to generate Members from BreastCancer
 def generate_members_from_breast_cancer(breast_cancer_df):
@@ -177,22 +93,69 @@ def generate_providers(num_providers):
         "PROV_TAXONOMY": np.random.choice(["208100000X", "207RE0101X"], num_providers),
     })
 
+# Function to generate wearable data
+def generate_wearable_data(num_patients, num_measurements, start_datetime, time_interval, cancer_rate, chemo_brain_effect, primary_keys):
+    num_cancer_patients = int((cancer_rate / 100) * num_patients)
+    cancer_patients = set(random.sample(primary_keys, num_cancer_patients))
+    baseline_activity = 2000
+    baseline_heart_rate = 80
+    baseline_o2 = 98.2
+    activity_reduction_factor = (100 - chemo_brain_effect) / 100.0
+    chemo_heart_rate_increase = 5
+
+    data_rows = []
+    timestamps = [start_datetime + i * time_interval for i in range(num_measurements)]
+
+    for pkey in primary_keys:
+        is_cancer = pkey in cancer_patients
+        for ts in timestamps:
+            activity_var = random.randint(-300, 300)
+            hr_var = random.randint(-3, 3)
+            o2_var = random.uniform(-0.3, 0.3)
+
+            if is_cancer:
+                activity = int((baseline_activity + activity_var) * activity_reduction_factor)
+                heart_rate = baseline_heart_rate + hr_var + chemo_heart_rate_increase
+            else:
+                activity = baseline_activity + activity_var
+                heart_rate = baseline_heart_rate + hr_var
+
+            o2_sat = baseline_o2 + o2_var
+
+            activity = max(activity, 0)
+            heart_rate = max(heart_rate, 50)
+            o2_sat = max(o2_sat, 90.0)
+
+            data_rows.append([pkey, ts.strftime("%Y-%m-%d %H:%M:%S"), activity, heart_rate, round(o2_sat, 1)])
+    
+    return pd.DataFrame(data_rows, columns=["PRIMARY_PERSON_KEY", "Measurement_Timestamp", "Activity_Level", "Heart_Rate", "O2_Saturation"])
+
 # Main Streamlit App
-st.title("Synthetic Medical Data Generator")
+st.title("Synthetic Medical Data Generator with Wearable Data")
 
 # Sliders
 num_patients = st.slider("Number of Breast Cancer Patients to Generate", 10, 1000, 100)
+num_measurements = st.slider("Measurements per Patient (Wearable Data)", 1, 100, 10)
 num_services = st.slider("Number of Services to Generate", 10, 2000, 500)
 num_providers = st.slider("Number of Providers to Generate", 10, 500, 100)
 
+start_date = st.date_input("Wearable Data Start Date", value=datetime(2024, 12, 1))
+start_time = st.time_input("Wearable Data Start Time", value=datetime(2024, 12, 1, 8, 0).time())
+cancer_rate = st.slider("Percentage of Patients with Cancer (Wearable Data)", 0, 100, 30)
+chemo_brain_effect = st.slider("Chemo Brain Impact on Activity Level (in % reduction)", 0, 50, 20)
+
 if st.button("Generate Data"):
+    primary_keys = [f"PPK_{i+1:05d}" for i in range(num_patients)]
+    wearable_start_datetime = datetime.combine(start_date, start_time)
     breast_cancer_df = generate_breast_cancer_data(num_patients)
     members_df = generate_members_from_breast_cancer(breast_cancer_df)
     enrollments_df = generate_enrollments_from_breast_cancer(breast_cancer_df)
-    services_df = generate_services(num_services, breast_cancer_df["PRIMARY_PERSON_KEY"].tolist())
+    services_df = generate_services(num_services, primary_keys)
     providers_df = generate_providers(num_providers)
+    wearable_data = generate_wearable_data(
+        num_patients, num_measurements, wearable_start_datetime, timedelta(hours=1), cancer_rate, chemo_brain_effect, primary_keys
+    )
 
-    # Display and download data
     st.subheader("Breast Cancer Data")
     st.dataframe(breast_cancer_df.head())
     st.download_button("Download Breast Cancer Data", breast_cancer_df.to_csv(index=False), "breast_cancer.csv")
@@ -212,3 +175,7 @@ if st.button("Generate Data"):
     st.subheader("Providers Data")
     st.dataframe(providers_df.head())
     st.download_button("Download Providers Data", providers_df.to_csv(index=False), "providers.csv")
+
+    st.subheader("Wearable Data")
+    st.dataframe(wearable_data.head())
+    st.download_button("Download Wearable Data", wearable_data.to_csv(index=False), "wearable_data.csv")