Medical Data Science and AI

Medi redefines healthcare analytics with powerful, accessible data science and AI tools designed specifically for medical applications.

Data Science Capabilities

Advanced Statistical Analysis

Medi includes built-in methods for common healthcare statistical operations:

// Survival analysis for clinical trials
survival_curve = kaplan_meier(
  data: trial_data,
  time: "follow_up_days",
  event: "disease_progression"
);

// Epidemiological modeling
outbreak_prediction = sir_model(
  population: 1000000,
  initial_infected: 100,
  r0: 2.5,
  recovery_days: 14
);

// Hospital resource optimization
bed_allocation = optimize_resources(
  hospital_data,
  objective: "minimize_wait_time",
  constraints: ["max_beds = 500", "min_staff_ratio = 0.25"]
);

Big Data Processing

Medi scales effortlessly to process massive healthcare datasets:

// Process and analyze 10TB genomic dataset
dataset genome_data = parallel {
  load_bulk_genomic(
    path: "/data/genomes/",
    format: "FASTQ",
    chunk_size: "1GB"
  );
};

// Distributed EHR processing
dataset patient_records = distributed {
  nodes: cluster.nodes,
  data: ehr_query("SELECT * FROM encounters"),
  operation: preprocess_encounters
};

Visualization

Create interactive, clinician-friendly visualizations with a few lines of code:

visualize {
  // Forest plot for meta-analysis
  plot_forest(
    meta_analysis_results,
    title: "Treatment Efficacy Across Studies",
    sort_by: "effect_size"
  );

  // Risk score visualization
  plot_risk_score(
    patient_cohort,
    risk_function: predict_cardiac_risk,
    stratify_by: "age_group",
    annotate: ["high_risk_patients"]
  );

  // Save interactive dashboard
  save("cardiac_risk_dashboard.html", interactive: true);
}

Artificial Intelligence

Pre-Trained Healthcare Models

Medi's medi.ai module provides ready-to-use models for common healthcare tasks:

// Detect lung nodules in CT scans
detection_results = medi.ai.imaging.detect_lung_nodules(
  images: patient_ct_scans,
  sensitivity: "high",
  return_confidence: true
);

// Predict heart failure risk
risk_scores = medi.ai.predict_risk(
  data: patient_data,
  condition: "heart_failure",
  timeframe: "5_years",
  features: ["age", "bp", "bmi", "medications", "comorbidities"]
);

// Analyze clinical notes
sentiment_analysis = medi.ai.nlp.analyze_notes(
  text: clinical_notes,
  extract: ["symptoms", "medications", "sentiment"]
);

Federated Learning

Train AI models across hospitals without sharing sensitive data:

// Set up federated learning
federated pneumonia_detection {
  sites: ["hospital_a", "hospital_b", "hospital_c"],
  model: "cnn",
  data_spec: {
    x: "chest_xray",
    y: "pneumonia_diagnosis"
  },
  privacy: {
    differential_privacy: true,
    epsilon: 0.5
  }
};

// Train the model
pneumonia_detection.train(
  epochs: 50,
  batch_size: 32,
  optimizer: "adam"
);

// Evaluate performance at each site
site_metrics = pneumonia_detection.evaluate_local();
global_metrics = pneumonia_detection.evaluate_global();

Real-Time AI

Implement low-latency inference on edge devices like wearables:

// Define ECG analysis for wearable
stream ecg_stream = connect("wearable_001", protocol: "MQTT");
model = medi.ai.load_model("arrhythmia_detection.medi");

// Optimize for edge deployment
edge_model = model.optimize(
  target: "wearable",
  format: "wasm",
  quantize: true
);

// Real-time monitoring
monitor ecg_stream {
  // Process each batch of ECG data
  window = collect(seconds: 5);

  // Run inference
  predictions = edge_model.predict(window);

  // Alert on detected arrhythmia
  if (predictions.contains("ventricular_tachycardia")) {
    alert("Critical arrhythmia detected", priority: "high");
  }
};

Explainable AI

Ensure transparency and trust in AI-driven healthcare decisions:

// Get explanations for AI predictions
explanations = model.explain(
  prediction: diagnosis_prediction,
  method: "shap",
  num_features: 10
);

// Visualize feature importance
visualize {
  plot_explanation(
    explanations,
    title: "Factors Influencing Diagnosis"
  );
};

// Generate clinical report with explanations
report {
  template: "ai_diagnosis",
  prediction: diagnosis_prediction,
  explanation: explanations,
  confidence: model.confidence,
  output: "explainable_ai_report.pdf"
};

Quantum Computing Readiness

Early support for quantum algorithms applicable to drug discovery and genomics:

// Import quantum computing module
import medi.quantum;

// Define quantum circuit for molecular simulation
circuit = medi.quantum.create_circuit(
  algorithm: "vqe",
  molecule: "aspirin",
  backend: "qiskit"
);

// Run simulation
results = circuit.run(
  shots: 1000,
  optimization: "cobyla"
);

// Analyze energy levels
binding_energy = results.get_binding_energy();