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#file_path = "cleaned_bmd_medication_data.xlsx"
import streamlit as st
import pandas as pd
import plotly.graph_objs as go
# Constants from linear regression
REGRESSION_CONSTANTS = {
'Femoral Neck': {'mu': 0.916852, 'sigma': 0.120754},
'Total Hip': {'mu': 0.955439, 'sigma': 0.125406},
'Lumbar spine (L1-L4)': {'mu': 1.131649, 'sigma': 0.139618},
}
# Load medication data
@st.cache_data
def load_medication_data():
file_path = "cleaned_bmd_medication_data.xlsx"
return pd.read_excel(file_path)
# Calculate predicted BMD after medication
def calculate_bmd(bmd, percentage_increase):
return bmd * (1 + percentage_increase)
# Convert BMD to T-score
def calculate_tscore(bmd, mu, sigma):
return (bmd - mu) / sigma
# Generate prediction table for all drugs
def generate_predictions(medication_data, site, bmd, mu, sigma):
site_data = medication_data[medication_data['Site'] == site]
all_results = []
for _, row in site_data.iterrows():
drug = row['Medication']
predictions = {'Year': ['0'], 'Predicted BMD': [round(bmd, 3)], 'Predicted T-score': [round(calculate_tscore(bmd, mu, sigma), 1)]}
for year in row.index[1:-1]: # Skip 'Medication' and 'Site' columns
if not pd.isna(row[year]):
percentage_increase = row[year]
predicted_bmd = bmd * (1 + percentage_increase)
predicted_tscore = calculate_tscore(predicted_bmd, mu, sigma)
predictions['Year'].append(year.replace(" Year", "")) # Simplify year label
predictions['Predicted BMD'].append(round(predicted_bmd, 3))
predictions['Predicted T-score'].append(round(predicted_tscore, 1))
all_results.append({'Drug': drug, 'Predictions': predictions})
return all_results
# Display results as table and plots
def display_results(predictions, site):
st.subheader(f"Predictions for {site}")
for result in predictions:
drug = result['Drug']
predictions = result['Predictions']
# Display table
st.write(f"### {drug}")
st.dataframe(pd.DataFrame(predictions))
# Plot BMD and T-score
bmd_plot = go.Scatter(
x=predictions['Year'], y=predictions['Predicted BMD'], mode='lines+markers',
name='Predicted BMD', line=dict(color='blue')
)
tscore_plot = go.Scatter(
x=predictions['Year'], y=predictions['Predicted T-score'], mode='lines+markers',
name='Predicted T-score', line=dict(color='green')
)
# Combine plots in a single row
col1, col2 = st.columns(2)
with col1:
st.plotly_chart(go.Figure(data=[bmd_plot], layout=go.Layout(
title=f"{drug} - Predicted BMD", xaxis_title="Year", yaxis_title="BMD (g/cm²)"
)))
with col2:
st.plotly_chart(go.Figure(data=[tscore_plot], layout=go.Layout(
title=f"{drug} - Predicted T-score", xaxis_title="Year", yaxis_title="T-score"
)))
# Streamlit UI
def main():
st.title("BMD and T-score Prediction Tool")
# DEXA Machine Selection
dexa_machine = st.selectbox("DEXA Machine", ["LUNAR"])
# Gender Selection
gender = st.selectbox("Gender", ["Female"])
# Location (Site) Selection with Mapping
site_mapping = {
'Lumbar spine (L1-L4)': 'LS',
'Femoral Neck': 'FN',
'Total Hip': 'TH'
}
site_options = list(site_mapping.keys())
selected_site = st.selectbox("Select Region (Site)", site_options)
site = site_mapping[selected_site] # Map to the actual value in the dataset
# Input patient data
bmd_patient = st.number_input(
"Initial BMD",
min_value=0.000, max_value=2.000,
value=0.800, step=0.001,
format="%.3f"
)
# Load constants and medication data
medication_data = load_medication_data()
constants = REGRESSION_CONSTANTS.get(selected_site, {})
# Generate and display predictions
if st.button("Predict"):
predictions = generate_predictions(medication_data, site, bmd_patient, constants['mu'], constants['sigma'])
display_results(predictions, selected_site)
if __name__ == "__main__":
main()
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