FHIR Extensions¶

A FHIR extension provides a way to add custom data elements to existing FHIR resources, enabling the representation of information that is not defined in the core FHIR specification. They allow for flexibility and extensibility within FHIR while maintaining interoperability.

The vocuabulary Observation includes a summary of these extensions

In [2]:

# extensions with ranges
combined_extension_range_counts = df[['research_study_identifiers', 'path', 'extension_url', 'low', 'high', 'url']]
combined_extension_range_counts = combined_extension_range_counts[combined_extension_range_counts['extension_url'] != '']
combined_extension_range_counts = combined_extension_range_counts[combined_extension_range_counts['low'] != '']
combined_extension_range_counts

# extensions with ranges combined_extension_range_counts = df[['research_study_identifiers', 'path', 'extension_url', 'low', 'high', 'url']] combined_extension_range_counts = combined_extension_range_counts[combined_extension_range_counts['extension_url'] != ''] combined_extension_range_counts = combined_extension_range_counts[combined_extension_range_counts['low'] != ''] combined_extension_range_counts

Out[2]:

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In [3]:

# extensions with codes
combined_extension_code_counts = df[['research_study_identifiers', 'path', 'extension_url', 'display', 'url', 'count']]
combined_extension_code_counts = combined_extension_code_counts[combined_extension_code_counts['extension_url'] != '']
combined_extension_code_counts = combined_extension_code_counts[combined_extension_code_counts['display'] != '']
combined_extension_code_counts

# extensions with codes combined_extension_code_counts = df[['research_study_identifiers', 'path', 'extension_url', 'display', 'url', 'count']] combined_extension_code_counts = combined_extension_code_counts[combined_extension_code_counts['extension_url'] != ''] combined_extension_code_counts = combined_extension_code_counts[combined_extension_code_counts['display'] != ''] combined_extension_code_counts

Out[3]:

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In [4]:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Create heatmap for each extension

# Get unique extensions
extension_urls = combined_extension_code_counts['extension_url'].unique()
combined_extension_code_counts = combined_extension_code_counts.infer_objects(copy=False)

for extension_url in extension_urls:
    # Filter data for the current extension
    filtered_data = combined_extension_code_counts[combined_extension_code_counts['extension_url'] == extension_url]

    # Pivot the filtered data for the heatmap
    heatmap_data = filtered_data.pivot_table(index='research_study_identifiers',
                                            columns=['display'],
                                            values='count',
                                            fill_value=0.0)

    # Create the heatmap
    plt.figure(figsize=(12, 10))  # Adjust figsize as needed

    # sns.heatmap(heatmap_data, annot=True, annot_kws={"size": 6}, fmt=".0f", cmap='viridis', cbar=False)

    # Create a mask to identify zero values
    mask = heatmap_data == 0

 # Custom function to format annotations
    def heatmap_annot(val, **kwargs):
        if val == 0:  # Hide annotations for zero values
            return ""
        else:
            return f"{val:.0f}"  # Format non-zero values as integers


    # Generate heatmap with the custom annotation function
    ax = sns.heatmap(heatmap_data, annot=True, fmt=".0f", cmap='viridis',
                cbar_kws={'extend': 'both', 'extendrect': True},
                vmin=0,  # Set minimum value for color scale
                vmax=heatmap_data.max().max() / 2,  # Adjust maximum value
                annot_kws={"size": 6})  # Reduce font size


    # Apply the custom annotation function to each cell
    for text in ax.texts:
        text.set_text(heatmap_annot(float(text.get_text())))

    plt.title(f'Heatmap for Path: {extension_url}')  # Set title with path name
    plt.xlabel('Display')
    plt.ylabel('Research Study Identifiers')
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()
    plt.show()

import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Create heatmap for each extension # Get unique extensions extension_urls = combined_extension_code_counts['extension_url'].unique() combined_extension_code_counts = combined_extension_code_counts.infer_objects(copy=False) for extension_url in extension_urls: # Filter data for the current extension filtered_data = combined_extension_code_counts[combined_extension_code_counts['extension_url'] == extension_url] # Pivot the filtered data for the heatmap heatmap_data = filtered_data.pivot_table(index='research_study_identifiers', columns=['display'], values='count', fill_value=0.0) # Create the heatmap plt.figure(figsize=(12, 10)) # Adjust figsize as needed # sns.heatmap(heatmap_data, annot=True, annot_kws={"size": 6}, fmt=".0f", cmap='viridis', cbar=False) # Create a mask to identify zero values mask = heatmap_data == 0 # Custom function to format annotations def heatmap_annot(val, **kwargs): if val == 0: # Hide annotations for zero values return "" else: return f"{val:.0f}" # Format non-zero values as integers # Generate heatmap with the custom annotation function ax = sns.heatmap(heatmap_data, annot=True, fmt=".0f", cmap='viridis', cbar_kws={'extend': 'both', 'extendrect': True}, vmin=0, # Set minimum value for color scale vmax=heatmap_data.max().max() / 2, # Adjust maximum value annot_kws={"size": 6}) # Reduce font size # Apply the custom annotation function to each cell for text in ax.texts: text.set_text(heatmap_annot(float(text.get_text()))) plt.title(f'Heatmap for Path: {extension_url}') # Set title with path name plt.xlabel('Display') plt.ylabel('Research Study Identifiers') plt.xticks(rotation=45, ha='right') plt.tight_layout() plt.show()