# Multi-Task Biomarker Configuration — Published Experiments
#
# Pass this file to --biomarker_config in train.py or test.py.
#
# For a minimal single-task example with detailed field descriptions,
# see biomarker_config_single_task_example.yaml.

experiment_name: "comorbidities_detection_multitask"
description: "Multi-task prediction of comorbidities and demographics from AbdCTBench"

# -------------------------------------------------------------------------
# Binary classification tasks (9 targets)
# -------------------------------------------------------------------------
binary_biomarkers:
  - name: "MORTALITY"
    description: "Death in the followup period"
    positive_class: "PRESENT"

  - name: "HCC12"
    description: "HCC code 12 — Breast, Prostate, and other cancers"
    positive_class: "PRESENT"

  - name: "HCC18"
    description: "HCC code 18 — Diabetes with Chronic Complications"
    positive_class: "PRESENT"

  - name: "HCC96"
    description: "HCC code 96 — Cardiac arrhythmias"
    positive_class: "PRESENT"

  - name: "HCC108"
    description: "HCC code 108 — Vascular disease"
    positive_class: "PRESENT"

  - name: "HCC111"
    description: "HCC code 111 — Chronic obstructive pulmonary disease"
    positive_class: "PRESENT"

  - name: "T2D"
    description: "Type 2 Diabetes"
    positive_class: "PRESENT"

  - name: "MI"
    description: "Myocardial Infarction"
    positive_class: "PRESENT"

  - name: "CALCIUMSCORING_ABDOMINALAGATSTON_BINARY"
    description: "High abdominal aortic calcium score (Agatston > 1000)"
    positive_class: "PRESENT"

# -------------------------------------------------------------------------
# Multiclass classification tasks
# -------------------------------------------------------------------------
multiclass_biomarkers: []

# -------------------------------------------------------------------------
# Continuous regression tasks (1 target)
# -------------------------------------------------------------------------
continuous_biomarkers:
  - name: "AGE"
    description: "Patient age in years (min-max normalized to [0, 1] during training)"
    min_value: 18
    max_value: 89
    normalization: "min_max"

# -------------------------------------------------------------------------
# Preprocessing settings
# -------------------------------------------------------------------------
preprocessing:
  image_size: 256
  normalize_images: true
  convert_to_rgb: true

# -------------------------------------------------------------------------
# Training settings
# -------------------------------------------------------------------------
training:
  class_weighting: true
  balanced_sampling: true

# -------------------------------------------------------------------------
# Validation / threshold settings
# -------------------------------------------------------------------------
validation:
  threshold_optimization: true
  optimization_metric: "f1_score"
  per_biomarker_thresholds: true
  threshold_search_range: [0.1, 0.9]
  threshold_search_steps: 9
  fallback_threshold: 0.5

  metrics:
    - "auroc"
    - "accuracy"
    - "sensitivity"
    - "specificity"
    - "f1_score"
    - "precision"
    - "recall"
  regression_metrics:
    - "mse"
    - "mae"
    - "r2_score"