Machine learning approach to stratify patients for clinical trials based on genomic and phenotypic data to improve trial success rates.
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This analysis aims to identify patient subgroups that are most likely to respond to a novel treatment. By stratifying patients based on biomarkers, we can design more efficient clinical trials.
* **Data Integration:** Combine Electronic Health Records (EHR) with genomic variant data.
* **Dimensionality Reduction:** Use PCA/t-SNE to visualize high-dimensional patient data.
* **Clustering:** Apply K-Means clustering to identify distinct patient phenotypes.
* **Survival Analysis:** Compare treatment outcomes across identified clusters.
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
# Load patient data
patient_data = pd.read_csv('clinical_trial_cohort.csv')
# Select biomarkers for stratification
biomarkers = ['age', 'bmi', 'biomarker_A', 'biomarker_B', 'gene_expression_X']
X = patient_data[biomarkers]
# Normalize data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# PCA for visualization
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
print(f"Explained variance ratio: {pca.explained_variance_ratio_}")Explained variance ratio: [0.45 0.32]
We identify 3 distinct patient clusters which may represent different response profiles.
Analyzing the survival rates (Kaplan-Meier) for each cluster reveals that **Cluster 2** shows a significantly better response to the treatment.