File size: 4,153 Bytes
48c283c 0f4785f 4dd63ab 0e2051f 4dd63ab 4111c89 4dd63ab b27e6a4 4dd63ab 95b7274 4dd63ab b27e6a4 4dd63ab b27e6a4 0e2051f b27e6a4 db2b096 b27e6a4 0f4785f b27e6a4 4dd63ab b27e6a4 0f4785f b27e6a4 4dd63ab b27e6a4 0f4785f b27e6a4 48c283c b27e6a4 48c283c b27e6a4 0f4785f 4dd63ab 0e2051f 53461be 12f2cdc 4dd63ab 0f4785f 331fa49 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
#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) # Adjusted as per your update
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
site_options = ['Lumbar spine (L1-L4)', 'Femoral Neck', 'Total Hip']
site = st.selectbox("Select Region (Site)", site_options)
# 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
constants = REGRESSION_CONSTANTS[site]
medication_data = load_medication_data()
# Generate and display predictions
if st.button("Predict"):
predictions = generate_predictions(medication_data, site, bmd_patient, constants['mu'], constants['sigma'])
display_results(predictions, site)
if __name__ == "__main__":
main()
|