MultiAgentSystems/MapAI-ClinicsAndMedCenters

1import streamlit as st2import folium3from folium.plugins import MarkerCluster4from streamlit_folium import folium_static5import googlemaps6from datetime import datetime7import os8 9# Initialize Google Maps10gmaps = googlemaps.Client(key=os.getenv('GOOGLE_KEY'))11 12# Function to fetch directions13def get_directions_and_coords(source, destination):14    now = datetime.now()15    directions_info = gmaps.directions(source, destination, mode='driving', departure_time=now)16    if directions_info:17        steps = directions_info[0]['legs'][0]['steps']18        coords = [(step['start_location']['lat'], step['start_location']['lng']) for step in steps]19        return steps, coords20    else:21        return None, None22 23# Function to render map with directions24def render_folium_map(coords):25    m = folium.Map(location=[coords[0][0], coords[0][1]], zoom_start=13)26    folium.PolyLine(coords, color="blue", weight=2.5, opacity=1).add_to(m)27    return m28 29# Function to add medical center paths and annotate distance30def add_medical_center_paths(m, source, med_centers):31    for name, lat, lon, specialty, city in med_centers:32        _, coords = get_directions_and_coords(source, (lat, lon))33        if coords:34            folium.PolyLine(coords, color="red", weight=2.5, opacity=1).add_to(m)35            folium.Marker([lat, lon], popup=name).add_to(m)36            distance_info = gmaps.distance_matrix(source, (lat, lon), mode='driving')37            distance = distance_info['rows'][0]['elements'][0]['distance']['text']38            folium.PolyLine(coords, color='red').add_to(m)39            folium.map.Marker(40                [coords[-1][0], coords[-1][1]],41                icon=folium.DivIcon(42                    icon_size=(150, 36),43                    icon_anchor=(0, 0),44                    html=f'<div style="font-size: 10pt; color : red;">{distance}</div>',45                    )46                ).add_to(m)47 48# Driving Directions Sidebar49st.sidebar.header('Directions 🚗')50source_location = st.sidebar.text_input("Source Location", "4 Brotherton Way, Auburn, MA 01501")51destination_location = st.sidebar.text_input("Destination Location", "366 Shrewsbury Street, Worcester, MA, 01604")52 53# Fetch and Display Directions54if st.sidebar.button('Get Directions'):55    steps, coords = get_directions_and_coords(source_location, destination_location)56    if steps and coords:57        st.subheader('Driving Directions:')58        for i, step in enumerate(steps):59            st.write(f"{i+1}. {step['html_instructions']}")60        st.subheader('Route on Map:')61        m1 = render_folium_map(coords)62        folium_static(m1)63    else:64        st.write("No available routes.")65 66# Massachusetts Medical Centers67st.markdown("### 🗺️ Maps - 🏥 Massachusetts Medical Centers 🌳")68m2 = folium.Map(location=[42.3601, -71.0589], zoom_start=8)69marker_cluster = MarkerCluster().add_to(m2)70 71massachusetts_med_centers = [72    ('The Endoscopy Center', 42.2098, -71.8356, '4 Brotherton Way, (508) 425-5446', 'Auburn'),73    ('ReadyMED – Auburn', 42.2090, -71.8358, '460 Southbridge Street, (508) 595-2700', 'Auburn'),74    ('Durable Medical Equipment', 42.2115, -71.8370, '42 Southbridge Street, (508) 407-7700', 'Auburn'),75    ('Auburn', 42.2098, -71.8356, '4 Brotherton Way, (508) 832-9621', 'Auburn'),76    ('Framingham', 42.2793, -71.4162, '761 Worcester Rd, (508) 872-1107', 'Framingham'),77    ('Holden', 42.3518, -71.8634, '64 Boyden Road, (508) 829-6765', 'Holden'),78    ('ReadyMED – Hudson', 42.3912, -71.5662, '234 Washington Street, (508) 595-2700', 'Hudson'),79    ('ReadyMED – Leominster', 42.5251, -71.7598, '241 North Main Street, (508) 595-2700', 'Leominster'),80    ('Leominster', 42.5204, -71.7717, '225 New Lancaster Road, (978) 534-6500', 'Leominster'),81    ('ReadyMED – Milford', 42.1487, -71.5152, '340 East Main Street, (508) 595-2700', 'Milford'),82    ('Milford', 42.1398, -71.5163, '101 Cedar Street, (508) 634-3100', 'Milford'),83    ('The Surgery Center', 42.2924, -71.7131, '151 Main St, (844) 258-4272', 'Shrewsbury'),84    ('Shrewsbury Occupational Medicine', 42.2930, -71.7240, '222 Boston Turnpike, (508) 853-2854', 'Shrewsbury'),85    ('Shrewsbury', 42.2865, -71.7147, '378 Maple Ave, (508) 368-7820', 'Shrewsbury'),86    ('Southborough', 42.3057, -71.5256, '24-28 Newton Street, (508) 481-5500', 'Southborough'),87    ('Webster', 42.0474, -71.8801, '344 Thompson Road, (508) 671-4050', 'Webster'),88    ('Westborough', 42.2695, -71.6161, '900 Union Street, (508) 366-8836', 'Westborough'),89    ('Worcester – Saint Vincent Cancer and Wellness Center', 42.2626, -71.8027, '1 Eaton Place, (508) 368-5430', 'Worcester'),90    ('Worcester – Neponset Street', 42.2614, -71.8007, '5 Neponset Street, (508) 368-7800', 'Worcester'),91    ('Worcester Medical Center', 42.2614, -71.8006, '123 Summer Street, (508) 852-0600', 'Worcester'),92    ('Worcester – Harding Street Rehabilitation & Sports Medicine', 42.2605, -71.8000, '112 Harding Street, (508) 964-5592', 'Worcester'),93    ('Worcester – Gold Star Boulevard Rehabilitation and Sports Medicine', 42.2910, -71.7999, '50 Gold Star Boulevard, (508) 856-9510', 'Worcester'),94    ('Worcester – Front Street', 42.2619, -71.8008, '100 Front Street, (508) 595-2000', 'Worcester'),95    ('Surgical Eye Experts', 42.2620, -71.8029, '385 Grove Street, (508) 453-8802', 'Worcester'),96    ('ReadyMED PLUS – Worcester', 42.2612, -71.8010, '366 Shrewsbury Street, (508) 595-2700', 'Worcester')97]98 99 100# Dropdown to select medical center to focus on101medical_center_names = [center[0] for center in massachusetts_med_centers]102selected_medical_center = st.selectbox("Select Medical Center to Focus On:", medical_center_names)103 104# Zoom into the selected medical center105for name, lat, lon, specialty, city in massachusetts_med_centers:106    if name == selected_medical_center:107        m2 = folium.Map(location=[lat, lon], zoom_start=15)108 109# Annotate distances and paths for each medical center110add_medical_center_paths(m2, source_location, massachusetts_med_centers)111 112folium_static(m2)113 114def Fairness():115    # List of 10 Types of Bias 😓116    st.markdown("### 10 Types of Bias in Geographical Healthcare Data 👩‍⚕️🌍")117    st.markdown("""118    1. **Sampling Bias**: When the clinics or medical centers chosen for analysis do not represent the entire population.119    2. **Confirmation Bias**: Picking clinics or centers that confirm pre-existing assumptions.120    3. **Location Bias**: Focusing only on urban or rural areas.121    4. **Temporal Bias**: Not considering the seasonality or time-sensitive factors.122    5. **Accessibility Bias**: Overlooking clinics that are hard to reach but may offer unique specialties.123    6. **Economic Bias**: Focusing only on wealthy areas.124    7. **Size Bias**: Ignoring smaller clinics or new centers.125    8. **Technology Bias**: Assuming higher tech facilities provide better care.126    9. **Specialization Bias**: Overemphasis on one type of specialty.127    10. **Reporting Bias**: Basing judgments on self-reported data without validation.128    """)129 130    # List of 10 Types of Fairness 😇131    st.markdown("### 10 Types of Fairness in Geographical Healthcare Data 🌍👩‍⚕️")132    st.markdown("""133    1. **Geographical Fairness**: Equal representation of urban and rural areas.134    2. **Socioeconomic Fairness**: Diverse economic statuses in the sample.135    3. **Healthcare Need Fairness**: Clinics catering to various healthcare needs.136    4. **Accessibility Fairness**: Including centers reachable by public transportation.137    5. **Specialization Fairness**: A balanced view across various medical specialties.138    6. **Temporal Fairness**: Data that accounts for seasonal or time-sensitive changes.139    7. **Cultural Fairness**: Inclusion of centers serving diverse cultural communities.140    8. **Demographic Fairness**: Representation across different age groups and genders.141    9. **Quality of Care Fairness**: Balanced data on patient satisfaction and quality of care.142    10. **Resource Allocation Fairness**: Fair distribution of resources among different centers.143    """)144 145Fairness()146 147def Fairness2():148    st.title("Bias and Fairness in Geographical Healthcare Data 🌍👩‍⚕️")149 150    st.markdown("### 10 Types of Bias in Geographical Healthcare Data 👩‍⚕️🌍")151    bias_types = {152        "Sampling Bias": r"\frac{\text{Unrepresented Population}}{\text{Total Population}}",153        "Confirmation Bias": r"\frac{\text{Data Confirming Assumptions}}{\text{Total Data Points}}",154        "Location Bias": r"\left| \frac{\text{Urban Centers}}{\text{Rural Centers}} - 1 \right|",155        "Temporal Bias": r"\frac{\text{Time-Sensitive Data Ignored}}{\text{Total Data Points}}",156        "Accessibility Bias": r"\frac{\text{Inaccessible Clinics}}{\text{Total Clinics}}",157        "Economic Bias": r"\frac{\text{Wealthy Area Clinics}}{\text{Total Clinics}}",158        "Size Bias": r"\frac{\text{Ignored Small Clinics}}{\text{Total Clinics}}",159        "Technology Bias": r"\frac{\text{High-Tech Clinics}}{\text{Total Clinics}}",160        "Specialization Bias": r"\frac{\text{Overemphasized Specialties}}{\text{Total Specialties}}",161        "Reporting Bias": r"\frac{\text{Unvalidated Reports}}{\text{Total Reports}}"162    }163    164    for bias, formula in bias_types.items():165        st.markdown(f"**{bias}**")166        st.latex(f"{formula}")167        168    st.markdown("### 10 Types of Fairness in Geographical Healthcare Data 🌍👩‍⚕️")169    fairness_types = {170        "Geographical Fairness": r"1 - \left| \frac{\text{Urban Centers}}{\text{Rural Centers}} - 1 \right|",171        "Socioeconomic Fairness": r"\frac{\text{Diverse Economic Clinics}}{\text{Total Clinics}}",172        "Healthcare Need Fairness": r"\frac{\text{Various Healthcare Need Clinics}}{\text{Total Clinics}}",173        "Accessibility Fairness": r"\frac{\text{Accessible Clinics}}{\text{Total Clinics}}",174        "Specialization Fairness": r"1 - \left| \frac{\text{Specialized Clinics}}{\text{General Clinics}} - 1 \right|",175        "Temporal Fairness": r"1 - \frac{\text{Time-Sensitive Data Ignored}}{\text{Total Data Points}}",176        "Cultural Fairness": r"\frac{\text{Diverse Cultural Clinics}}{\text{Total Clinics}}",177        "Demographic Fairness": r"\frac{\text{Diverse Demographic Clinics}}{\text{Total Clinics}}",178        "Quality of Care Fairness": r"\frac{\text{High-Quality Clinics}}{\text{Total Clinics}}",179        "Resource Allocation Fairness": r"\frac{\text{Evenly Distributed Resources}}{\text{Total Resources}}"180    }181    182    for fairness, formula in fairness_types.items():183        st.markdown(f"**{fairness}**")184        st.latex(f"{formula}")185 186if __name__ == "__main__":187    Fairness2()188