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Stable#

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JAMA#

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CG-BEST represents our Dionysian bequethal.

You, from crimes
Art, to enchant
Relieved, by prayer
Spirits, to enforce
Ending, in despair
— Prospero
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Dionysus as chaotic energy (static), Athena as the filtering force (temperament), and Apollo shaping the resulting harmony (jazz). Source: DALL-E

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network fractal
def define_layers():
    return {
        'Suis': ['Patient Access/Care', 'Hospital Records', 'Disclosure Risk', 'Collaborators', 'Databases', 'Analytic Scripts', ], # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adverse Event Markers', 'Comorbidity/ICD Codes', 'Temporal Changes'],  
        "M'èléve": ['Mortality Rate', 'Organ Failure',  'Hospitalization', 'Dependency', 'Physical Frailty']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = { # Dynamic
        'yellow': ['Information'],  
        'paleturquoise': ['Analytic Scripts', 'Decision', 'Temporal Changes', 'Physical Frailty'],  
        'lightgreen': ['Databases', 'Comorbidity/ICD Codes', 'Organ Failure', 'Dependency', 'Hospitalization'],  
        'lightsalmon': [
            'Disclosure Risk', 'Collaborators', 'Baseline',  
            'Adverse Event Markers', 'Mortality Rate'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))   

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=15, connectionstyle="arc3,rad=0.2"
    )
    plt.title(" ", fontsize=23)    
    # ✅ Save the actual image *after* drawing it
    # plt.savefig("../figures/ukubona.jpeg", dpi=300, bbox_inches='tight')
    # plt.show()

# Run the visualization
visualize_nn()
../../_images/24e72a12a8c844022658cb18e8d48dca9e12311b5b1fb7a3d2841401d5d157aa.png
figures/blanche.*

Fig. 23 What are the downstream effects of this proposal?#

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