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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import random | |
| from datetime import datetime, timedelta | |
| # Seed for reproducibility | |
| 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 = { | |
| "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, | |
| "ER Status": er_status, | |
| "PR Status": pr_status, | |
| "HER2 Status": her2_status, | |
| "Ki-67 Level": ki67_level, | |
| "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) | |
| # Function to generate Members | |
| def generate_members_from_breast_cancer(breast_cancer_df): | |
| return pd.DataFrame({ | |
| "MEMBER_ID": breast_cancer_df["PRIMARY_PERSON_KEY"], | |
| "PRIMARY_PERSON_KEY": breast_cancer_df["PRIMARY_PERSON_KEY"], | |
| "MEM_GENDER": ["F"] * len(breast_cancer_df), | |
| "MEM_ETHNICITY": np.random.choice(["Hispanic", "Non-Hispanic", None], len(breast_cancer_df)), | |
| "MEM_RACE": np.random.choice(["White", "Black", "Asian", None], len(breast_cancer_df)), | |
| "MEM_STATE": np.random.choice(["MI", "HI", "CA"], len(breast_cancer_df)), | |
| "MEM_ZIP3": np.random.randint(100, 999, len(breast_cancer_df)), | |
| }) | |
| # Function to generate Enrollments | |
| def generate_enrollments_from_breast_cancer(breast_cancer_df): | |
| return pd.DataFrame({ | |
| "PRIMARY_PERSON_KEY": breast_cancer_df["PRIMARY_PERSON_KEY"], | |
| "MEM_STAT": np.random.choice(["ACTIVE", "INACTIVE"], len(breast_cancer_df)), | |
| "PAYER_LOB": np.random.choice(["MEDICAID", "COMMERCIAL", "MEDICARE"], len(breast_cancer_df)), | |
| "PAYER_TYPE": np.random.choice(["PPO", "HMO"], len(breast_cancer_df)), | |
| "RELATION": np.random.choice(["SUBSCRIBER", "DEPENDENT"], len(breast_cancer_df)), | |
| }) | |
| # Function to generate Services | |
| def generate_services(num_services, primary_keys): | |
| return pd.DataFrame({ | |
| "PRIMARY_PERSON_KEY": np.random.choice(primary_keys, num_services), | |
| "SERVICE_SETTING": np.random.choice(["OUTPATIENT", "INPATIENT"], num_services), | |
| "PROC_CODE": np.random.randint(1000, 9999, num_services), | |
| "SERVICE_DATE": pd.date_range(start="2023-01-01", periods=num_services).to_numpy(), | |
| "AMOUNT_BILLED": np.random.uniform(500, 15000, num_services), | |
| "AMOUNT_PAID": np.random.uniform(500, 15000, num_services), | |
| "CLAIM_STATUS": np.random.choice(["PAID", "DENIED", "PENDING"], num_services), | |
| "RELATION": np.random.choice(["SUBSCRIBER", "DEPENDENT"], num_services), | |
| }) | |
| # Function to generate Providers | |
| def generate_providers(num_providers): | |
| return pd.DataFrame({ | |
| "PROVIDER_ID": [f"PROV_{i+1:05d}" for i in range(num_providers)], | |
| "PROV_NAME": np.random.choice(["Clinic A", "Clinic B", "Clinic C"], num_providers), | |
| "PROV_STATE": np.random.choice(["MI", "HI", "CA"], num_providers), | |
| "PROV_ZIP": np.random.randint(10000, 99999, num_providers), | |
| "PROV_SPECIALTY": np.random.choice(["Oncology", "Radiology", "Surgery"], 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 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, 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 | |
| ) | |
| 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") | |
| st.subheader("Members Data") | |
| st.dataframe(members_df.head()) | |
| st.download_button("Download Members Data", members_df.to_csv(index=False), "members.csv") | |
| st.subheader("Enrollments Data") | |
| st.dataframe(enrollments_df.head()) | |
| st.download_button("Download Enrollments Data", enrollments_df.to_csv(index=False), "enrollments.csv") | |
| st.subheader("Services Data") | |
| st.dataframe(services_df.head()) | |
| st.download_button("Download Services Data", services_df.to_csv(index=False), "services.csv") | |
| st.subheader("Providers Data") | |
| st.dataframe(providers_df.head()) | |
| st.download_button("Download Providers Data", providers_df.to_csv(index=False | |