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Design an ICD-10 Prediction System for Clinical Records

AmbienceMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenML System Design

Last updated

Design an end-to-end clinical NLP system to predict ICD-10 codes from long medical records, addressing extreme multi-label classification, long-tail code distributions, and human-in-the-loop coder feedback.

Scenario

Ambience develops AI documentation tools that generate structured clinical encounter summaries from doctor-patient conversations. To automate medical billing and risk-adjustment workflows, your team must build an automated ICD-10-CM coding engine that processes clinician notes and proposes relevant diagnostic codes along with supporting textual evidence.

Requirements

  • Functional Requirements:

    • Ingest unstructured clinical encounter notes and predict a ranked list of ICD-10 codes with calibrated confidence scores.
    • Extract supporting spans of text (evidence attribution) for each suggested code to aid medical coder review.
    • Provide an interface/API for human medical coders to accept, reject, or manually add codes.
    • Continually incorporate coder feedback into model updates without catastrophic forgetting.

  • Non-Functional Requirements:

    • Latency: Return code suggestions within 2 seconds for a standard note ( 80% of codes appear fewer than 10 times in historical training data (extreme long tail).
    • Compliance & Safety: Comply with HIPAA regulations; minimize hallucinations and erroneous upcoding.

Key Design Topics to Cover

  1. Long-Document Processing: Standard transformer context windows (e.g., 512 tokens) are insufficient for multi-page clinical notes. Discuss tokenization, chunking/pooling strategies, and long-context architectures (e.g., sparse attention, hierarchical transformers).
  2. Extreme Multi-Label Classification (XML): Compare direct classification (flat sigmoid over 70k classes) against bi-encoder retrieval architectures (matching note representations to code description embeddings) and hierarchical tree-based routing.
  3. Handling Rare & Zero-Shot Codes: How to handle codes with zero or minimal training instances using code descriptions, synonyms, and clinical knowledge graphs.
  4. Evaluation Metrics: Choose appropriate metrics across head, torso, and tail codes (e.g., Macro-F1, Micro-F1, Precision@K, NDCG@K) and audit risk measures.
  5. Feedback Loop & Active Learning: Pipeline for capturing coder corrections, managing label noise, and updating models.

Hints

Hint 1

Consider framing the problem as dense semantic retrieval between note segments and official ICD-10 code descriptions to naturally support zero-shot tail codes.

Hint 2

The hierarchical syntax of ICD-10 (chapter, category, subcategory) can be leveraged for coarse-to-fine candidate generation or hierarchical multi-task loss.

Hint 3

For evidence attribution, evaluate attention roll-out, gradient-based saliency, or snippet-level retrieval rather than relying solely on global document embeddings.

Follow-up questions

  • How would you handle annual CMS code set updates (newly introduced codes, revisions, and deprecations) without full retraining?
  • How would you tune prediction thresholds to optimize the economic trade-off between medical coder review time and missed billing revenue?
  • If coders report frequent false positives caused by family history or negated conditions, what architectural or post-processing safeguards would you introduce?