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| import streamlit as st | |
| import folium | |
| from folium.plugins import MarkerCluster | |
| from streamlit_folium import folium_static | |
| import googlemaps | |
| from datetime import datetime | |
| import os | |
| # Initialize Google Maps | |
| gmaps = googlemaps.Client(key=os.getenv('GOOGLE_KEY')) | |
| # Function to fetch directions | |
| def get_directions_and_coords(source, destination): | |
| now = datetime.now() | |
| directions_info = gmaps.directions(source, destination, mode='driving', departure_time=now) | |
| if directions_info: | |
| steps = directions_info[0]['legs'][0]['steps'] | |
| coords = [(step['start_location']['lat'], step['start_location']['lng']) for step in steps] | |
| return steps, coords | |
| else: | |
| return None, None | |
| # Function to render map with directions | |
| def render_folium_map(coords): | |
| m = folium.Map(location=[coords[0][0], coords[0][1]], zoom_start=13) | |
| folium.PolyLine(coords, color="blue", weight=2.5, opacity=1).add_to(m) | |
| return m | |
| # Function to add medical center paths and annotate distance | |
| def add_medical_center_paths(m, source, med_centers): | |
| for name, lat, lon, specialty, city in med_centers: | |
| _, coords = get_directions_and_coords(source, (lat, lon)) | |
| if coords: | |
| folium.PolyLine(coords, color="red", weight=2.5, opacity=1).add_to(m) | |
| folium.Marker([lat, lon], popup=name).add_to(m) | |
| distance_info = gmaps.distance_matrix(source, (lat, lon), mode='driving') | |
| distance = distance_info['rows'][0]['elements'][0]['distance']['text'] | |
| folium.PolyLine(coords, color='red').add_to(m) | |
| folium.map.Marker( | |
| [coords[-1][0], coords[-1][1]], | |
| icon=folium.DivIcon( | |
| icon_size=(150, 36), | |
| icon_anchor=(0, 0), | |
| html=f'<div style="font-size: 10pt; color : red;">{distance}</div>', | |
| ) | |
| ).add_to(m) | |
| # Driving Directions Sidebar | |
| st.sidebar.header('Directions 🚗') | |
| source_location = st.sidebar.text_input("Source Location", "4 Brotherton Way, Auburn, MA 01501") | |
| destination_location = st.sidebar.text_input("Destination Location", "366 Shrewsbury Street, Worcester, MA, 01604") | |
| # Fetch and Display Directions | |
| if st.sidebar.button('Get Directions'): | |
| steps, coords = get_directions_and_coords(source_location, destination_location) | |
| if steps and coords: | |
| st.subheader('Driving Directions:') | |
| for i, step in enumerate(steps): | |
| st.write(f"{i+1}. {step['html_instructions']}") | |
| st.subheader('Route on Map:') | |
| m1 = render_folium_map(coords) | |
| folium_static(m1) | |
| else: | |
| st.write("No available routes.") | |
| # Massachusetts Medical Centers | |
| st.markdown("### 🗺️ Maps - 🏥 Massachusetts Medical Centers 🌳") | |
| m2 = folium.Map(location=[42.3601, -71.0589], zoom_start=8) | |
| marker_cluster = MarkerCluster().add_to(m2) | |
| massachusetts_med_centers = [ | |
| ('The Endoscopy Center', 42.2098, -71.8356, '4 Brotherton Way, (508) 425-5446', 'Auburn'), | |
| ('ReadyMED – Auburn', 42.2090, -71.8358, '460 Southbridge Street, (508) 595-2700', 'Auburn'), | |
| ('Durable Medical Equipment', 42.2115, -71.8370, '42 Southbridge Street, (508) 407-7700', 'Auburn'), | |
| ('Auburn', 42.2098, -71.8356, '4 Brotherton Way, (508) 832-9621', 'Auburn'), | |
| ('Framingham', 42.2793, -71.4162, '761 Worcester Rd, (508) 872-1107', 'Framingham'), | |
| ('Holden', 42.3518, -71.8634, '64 Boyden Road, (508) 829-6765', 'Holden'), | |
| ('ReadyMED – Hudson', 42.3912, -71.5662, '234 Washington Street, (508) 595-2700', 'Hudson'), | |
| ('ReadyMED – Leominster', 42.5251, -71.7598, '241 North Main Street, (508) 595-2700', 'Leominster'), | |
| ('Leominster', 42.5204, -71.7717, '225 New Lancaster Road, (978) 534-6500', 'Leominster'), | |
| ('ReadyMED – Milford', 42.1487, -71.5152, '340 East Main Street, (508) 595-2700', 'Milford'), | |
| ('Milford', 42.1398, -71.5163, '101 Cedar Street, (508) 634-3100', 'Milford'), | |
| ('The Surgery Center', 42.2924, -71.7131, '151 Main St, (844) 258-4272', 'Shrewsbury'), | |
| ('Shrewsbury Occupational Medicine', 42.2930, -71.7240, '222 Boston Turnpike, (508) 853-2854', 'Shrewsbury'), | |
| ('Shrewsbury', 42.2865, -71.7147, '378 Maple Ave, (508) 368-7820', 'Shrewsbury'), | |
| ('Southborough', 42.3057, -71.5256, '24-28 Newton Street, (508) 481-5500', 'Southborough'), | |
| ('Webster', 42.0474, -71.8801, '344 Thompson Road, (508) 671-4050', 'Webster'), | |
| ('Westborough', 42.2695, -71.6161, '900 Union Street, (508) 366-8836', 'Westborough'), | |
| ('Worcester – Saint Vincent Cancer and Wellness Center', 42.2626, -71.8027, '1 Eaton Place, (508) 368-5430', 'Worcester'), | |
| ('Worcester – Neponset Street', 42.2614, -71.8007, '5 Neponset Street, (508) 368-7800', 'Worcester'), | |
| ('Worcester Medical Center', 42.2614, -71.8006, '123 Summer Street, (508) 852-0600', 'Worcester'), | |
| ('Worcester – Harding Street Rehabilitation & Sports Medicine', 42.2605, -71.8000, '112 Harding Street, (508) 964-5592', 'Worcester'), | |
| ('Worcester – Gold Star Boulevard Rehabilitation and Sports Medicine', 42.2910, -71.7999, '50 Gold Star Boulevard, (508) 856-9510', 'Worcester'), | |
| ('Worcester – Front Street', 42.2619, -71.8008, '100 Front Street, (508) 595-2000', 'Worcester'), | |
| ('Surgical Eye Experts', 42.2620, -71.8029, '385 Grove Street, (508) 453-8802', 'Worcester'), | |
| ('ReadyMED PLUS – Worcester', 42.2612, -71.8010, '366 Shrewsbury Street, (508) 595-2700', 'Worcester') | |
| ] | |
| # Dropdown to select medical center to focus on | |
| medical_center_names = [center[0] for center in massachusetts_med_centers] | |
| selected_medical_center = st.selectbox("Select Medical Center to Focus On:", medical_center_names) | |
| # Zoom into the selected medical center | |
| for name, lat, lon, specialty, city in massachusetts_med_centers: | |
| if name == selected_medical_center: | |
| m2 = folium.Map(location=[lat, lon], zoom_start=15) | |
| # Annotate distances and paths for each medical center | |
| add_medical_center_paths(m2, source_location, massachusetts_med_centers) | |
| folium_static(m2) | |
| def Fairness(): | |
| # List of 10 Types of Bias 😓 | |
| st.markdown("### 10 Types of Bias in Geographical Healthcare Data 👩⚕️🌍") | |
| st.markdown(""" | |
| 1. **Sampling Bias**: When the clinics or medical centers chosen for analysis do not represent the entire population. | |
| 2. **Confirmation Bias**: Picking clinics or centers that confirm pre-existing assumptions. | |
| 3. **Location Bias**: Focusing only on urban or rural areas. | |
| 4. **Temporal Bias**: Not considering the seasonality or time-sensitive factors. | |
| 5. **Accessibility Bias**: Overlooking clinics that are hard to reach but may offer unique specialties. | |
| 6. **Economic Bias**: Focusing only on wealthy areas. | |
| 7. **Size Bias**: Ignoring smaller clinics or new centers. | |
| 8. **Technology Bias**: Assuming higher tech facilities provide better care. | |
| 9. **Specialization Bias**: Overemphasis on one type of specialty. | |
| 10. **Reporting Bias**: Basing judgments on self-reported data without validation. | |
| """) | |
| # List of 10 Types of Fairness 😇 | |
| st.markdown("### 10 Types of Fairness in Geographical Healthcare Data 🌍👩⚕️") | |
| st.markdown(""" | |
| 1. **Geographical Fairness**: Equal representation of urban and rural areas. | |
| 2. **Socioeconomic Fairness**: Diverse economic statuses in the sample. | |
| 3. **Healthcare Need Fairness**: Clinics catering to various healthcare needs. | |
| 4. **Accessibility Fairness**: Including centers reachable by public transportation. | |
| 5. **Specialization Fairness**: A balanced view across various medical specialties. | |
| 6. **Temporal Fairness**: Data that accounts for seasonal or time-sensitive changes. | |
| 7. **Cultural Fairness**: Inclusion of centers serving diverse cultural communities. | |
| 8. **Demographic Fairness**: Representation across different age groups and genders. | |
| 9. **Quality of Care Fairness**: Balanced data on patient satisfaction and quality of care. | |
| 10. **Resource Allocation Fairness**: Fair distribution of resources among different centers. | |
| """) | |
| Fairness() | |
| def Fairness2(): | |
| st.title("Bias and Fairness in Geographical Healthcare Data 🌍👩⚕️") | |
| st.markdown("### 10 Types of Bias in Geographical Healthcare Data 👩⚕️🌍") | |
| bias_types = { | |
| "Sampling Bias": r"\frac{\text{Unrepresented Population}}{\text{Total Population}}", | |
| "Confirmation Bias": r"\frac{\text{Data Confirming Assumptions}}{\text{Total Data Points}}", | |
| "Location Bias": r"\left| \frac{\text{Urban Centers}}{\text{Rural Centers}} - 1 \right|", | |
| "Temporal Bias": r"\frac{\text{Time-Sensitive Data Ignored}}{\text{Total Data Points}}", | |
| "Accessibility Bias": r"\frac{\text{Inaccessible Clinics}}{\text{Total Clinics}}", | |
| "Economic Bias": r"\frac{\text{Wealthy Area Clinics}}{\text{Total Clinics}}", | |
| "Size Bias": r"\frac{\text{Ignored Small Clinics}}{\text{Total Clinics}}", | |
| "Technology Bias": r"\frac{\text{High-Tech Clinics}}{\text{Total Clinics}}", | |
| "Specialization Bias": r"\frac{\text{Overemphasized Specialties}}{\text{Total Specialties}}", | |
| "Reporting Bias": r"\frac{\text{Unvalidated Reports}}{\text{Total Reports}}" | |
| } | |
| for bias, formula in bias_types.items(): | |
| st.markdown(f"**{bias}**") | |
| st.latex(f"{formula}") | |
| st.markdown("### 10 Types of Fairness in Geographical Healthcare Data 🌍👩⚕️") | |
| fairness_types = { | |
| "Geographical Fairness": r"1 - \left| \frac{\text{Urban Centers}}{\text{Rural Centers}} - 1 \right|", | |
| "Socioeconomic Fairness": r"\frac{\text{Diverse Economic Clinics}}{\text{Total Clinics}}", | |
| "Healthcare Need Fairness": r"\frac{\text{Various Healthcare Need Clinics}}{\text{Total Clinics}}", | |
| "Accessibility Fairness": r"\frac{\text{Accessible Clinics}}{\text{Total Clinics}}", | |
| "Specialization Fairness": r"1 - \left| \frac{\text{Specialized Clinics}}{\text{General Clinics}} - 1 \right|", | |
| "Temporal Fairness": r"1 - \frac{\text{Time-Sensitive Data Ignored}}{\text{Total Data Points}}", | |
| "Cultural Fairness": r"\frac{\text{Diverse Cultural Clinics}}{\text{Total Clinics}}", | |
| "Demographic Fairness": r"\frac{\text{Diverse Demographic Clinics}}{\text{Total Clinics}}", | |
| "Quality of Care Fairness": r"\frac{\text{High-Quality Clinics}}{\text{Total Clinics}}", | |
| "Resource Allocation Fairness": r"\frac{\text{Evenly Distributed Resources}}{\text{Total Resources}}" | |
| } | |
| for fairness, formula in fairness_types.items(): | |
| st.markdown(f"**{fairness}**") | |
| st.latex(f"{formula}") | |
| if __name__ == "__main__": | |
| Fairness2() | |