Model Card: SC/IC Pre-triage Risk Model

Version: v1.2.0
Release date: 3 Jun 2026

 

1 Model overview

The Symptom Check / Illness Check (SC/IC) Pre-Triage Model is designed to classify patient-reported check data into clear, actionable urgency levels. To maximize safety, the system operates using two distinct wings: a traditional rule-based expert system and a Machine Learning (ML) component. The final output always prioritizes safety by defaulting to the higher of the two predicted urgency levels.

  • Model name: SC/IC Pre-triage Model
  • Model type: One-vs-Rest Logistic Regression classifier
  • Developer: XUND
  • Intended users: Laypersons (general public)

 

2 Intended use

2.1 Purpose and benefits

The model enhances the accuracy of our standard rule-based triage system by adding multivariable machine learning support. It processes user inputs to estimate the overall clinical urgency of a situation, allowing the digital health tool to escalate a case if complex combinations of symptoms indicate a higher risk than basic rules would catch. Importantly, the ML model can only escalate care - it is architecturally restricted from ever de-escalating a rule-based expert assessment.

2.2 Out of scope cases

  • Not a diagnosis: The model classifies urgency levels to guide next steps; it does not diagnose specific medical conditions.
  • Emergency use: Not intended for assessing acute or life-threatening symptoms.
  • Age restriction: This model is not validated for pediatric use and strictly excludes patients below the age of 10.

 

3 Model inputs and outputs

3.1 Model inputs

The model processes comprehensive, patient-provided responses gathered during an SC/IC check, including:

  • Demographics: Basic user information (age and biological sex).
  • Symptom profile: Primary complaints and associated symptoms.
  • Clinical factors: Other contextual, illness-related variables provided by the user.

3.2 Model outputs

The primary public output is a clear, color-coded urgency label directing the user to the safest level of care:

  • RED (Seek urgent care): Immediate medical attention is required.
  • ORANGE (Visit a doctor): Professional evaluation is needed in the near future.
  • GREEN (Self-care): Symptoms can be safely managed at home.

 

4 Training data & diversity

The model is trained on a curated dataset combining published medical literature with validated real-patient histories.

  • Data sources: The model is trained on a broad spectrum of medical case studies covering diverse specialties. This includes published medical literature alongside real-patient histories annotated by XUND’s internal medical team.
  • Dataset balancing: Because published medical literature naturally focuses more heavily on severe cases, the training data was intentionally enriched with a dedicated subset of verified, low-urgency (GREEN) clinical scenarios. This ensures the model maintains a balanced baseline distribution and performs reliably across all three triage tiers.
  • Validation & optimization: To maximize the utility of the available clinical data, the model was validated using rigorous cross-validation techniques. The final architecture utilizes an L2-regularized linear classifier optimized with balanced class weights to counteract inherent dataset distributions.

5 Evaluation & performance

5.1 Performance metrics

The model is evaluated using two primary metrics: Overall accuracy (maintaining strong performance across all three triage tiers) and Sensitivity (ensuring critical RED cases are never missed).

Evaluation Metric Model Performance (v1.2.0) Target Baseline (Laypeople Avg.) Clinical Interpretation
Overall Accuracy 65.7% 63.19% Outperforms average layperson
Sensitivity (Urgent/RED cases) 78.8% 75.20% Strong safety margin for critical cases

5.2 Benchmarking

To satisfy our clinical release constraints, the model must consistently exceed the safety metrics of an average layperson as established in our comprehensive Clinical Evaluation Report.

6 Limitations & safety considerations

As part of our commitment to transparent AI, we track several data constraints inherited from the small, case-study-based nature of available medical evaluation sets:

  • Dataset imbalances: Training literature naturally leans heavier toward severe cases (Orange/ Red) than everyday self-care cases (Green). Additionally, age displays a bi-modal distribution, and sex imbalances exist within the nephrology and infectious illness categories.
  • Generalization limits: Many illnesses appear only once within the data, meaning they may exist in either the training or testing splits, but not both.
  • Contextual differences: Training inputs rely on expert-annotated clinical summaries rather than direct, user-filled digital questionnaires. Performance may vary in low-information scenarios where users provide minimal symptoms.
  • Data noise filtering (feature selection): To ensure stability and safety, the model strictly ignores rare symptoms or data points during both training and real-world clinical evaluation. A clinical feature is only considered if it appears across multiple (>=3) independent case studies, ensuring the AI never bases urgency decisions on isolated anomalies.

 

7 Ethical & responsible use

XUND is committed to developing AI that is transparent, safe, and beneficial. The SC/IC Pre-triage Model is governed by the following ethical principles:

  • Safety-first architecture: The model's conditional logic acts as an ethical guardrail - machine learning is used purely as a protective layer to catch overlooked risks and escalate care, never to downgrade clinical caution.
  • Medical oversight: Feature inclusion parameters and the underlying rule-based systems are entirely designed and overseen by qualified medical professionals.
  • Continuous surveillance: Because final training utilizes the full dataset post-cross-validation, we perform active subgroup monitoring, calibration surveillance, and ongoing medical expert validation to maintain reliability across demographic populations and prevent algorithmic drift.