ATC and ICD-10 codes give healthcare AI systems a structured vocabulary for medicines, diagnoses, encounters, and outcomes. Used carefully, they can help an LLM connect clinical language with standardized concepts. Used carelessly, they can encode billing artifacts, leakage, outdated classifications, and local workflow bias.
This guide explains how to design an ATC ICD10 codes LLM training pipeline that is useful for Indian healthcare builders. It covers data modeling, preprocessing, annotation, evaluation, governance, and deployment decisions for clinical NLP systems.
ATC and ICD-10 solve different problems
ATC (Anatomical Therapeutic Chemical) codes classify medicines by anatomical target, therapeutic use, pharmacological properties, and chemical identity. Their hierarchy helps a model recognize that two medicines may belong to the same therapeutic class even when their names, brands, or spellings differ.
ICD-10 codes classify diseases, symptoms, abnormal findings, and health-related conditions. They support reporting, clinical documentation, claims workflows, epidemiology, and cohort construction. ICD-10 is not a complete representation of a patient: it may omit severity, temporality, test results, treatment response, and social context.
The two systems are complementary, not interchangeable. ATC can represent medication exposure; ICD-10 can represent documented clinical conditions. A training example might therefore connect an ICD-10 diagnosis with an ATC-coded medicine, but that association must not be treated as proof that the medicine caused, cured, or is always appropriate for the condition.
For a focused overview of diagnosis-code modeling, see ICD-10 codes for LLM training. Remove the space after the opening parenthesis when implementing this link.
Define the training task before collecting codes
“Train an LLM on ATC and ICD-10” is too broad to guide dataset design. Specify the task first:
- Code extraction: identify diagnoses or medicines mentioned in free text.
- Code normalization: map brand names, generic names, abbreviations, and misspellings to standard concepts.
- Code suggestion: propose candidate codes for review by a qualified professional.
- Clinical summarization: use coded events as supporting context for a longitudinal summary.
- Cohort discovery: find records matching a research definition.
- Quality assurance: detect inconsistent, missing, or implausible coding.
Each task requires different labels and safeguards. A code extraction model can be evaluated against expert annotations. A code suggestion model needs top-k recall and calibration. A summarization system must be tested for unsupported claims, not just linguistic fluency.
Do not combine all tasks into a single benchmark. A model may recognize a code accurately yet produce an unsafe recommendation when asked to interpret it.
Build a trustworthy dataset
Start with a data dictionary that defines every field, source, timestamp, unit, code version, and permitted use. Preserve the original text and the normalized representation separately. This makes errors auditable and prevents irreversible preprocessing.
A useful record may include:
- De-identified clinical text and document type.
- ATC code, drug name, strength, route, frequency, and status.
- ICD-10 code, description, encounter date, and diagnosis status.
- Evidence span showing where the code was supported in the text.
- Negation, uncertainty, temporality, and attribution.
- Source system, coding version, and annotation provenance.
Distinguish active, historical, planned, stopped, and rejected medicines. Likewise, distinguish confirmed diagnoses from suspected, ruled-out, family-history, and administrative entries. Without these distinctions, a model may infer that every code in a record describes an active patient condition.
Audit the dataset for duplicate encounters, copied-forward notes, contradictory medication lists, missing discharge records, and impossible dates. The guidance in how to audit AI training data integrity is particularly relevant when records originate from several hospitals or vendors. Again, remove the space in the Markdown link before publishing.
Handle India-specific complexity
Indian healthcare data is multilingual, fragmented, and often shaped by facility-specific workflows. Clinical notes may mix English with Hindi, regional languages, transliterated terms, abbreviations, brand names, and phonetic spellings. A model trained only on clean English descriptions will underperform in many real deployments.
Include representative samples from public and private hospitals, primary care, diagnostics, pharmacies, and telemedicine only when the consent and governance basis is clear. Record language and care setting as metadata, then report performance by subgroup rather than only publishing one aggregate score. If the product serves regional-language workflows, principles from low-resource language datasets for AI training in India can help with sampling, annotation, and quality control.
Do not assume that an international codebook maps directly to every Indian workflow. Confirm which ICD-10 release, national adaptation, payer convention, and local terminology are in use. Version mismatches can create silent label errors, especially when historical data spans several years.
Preprocess codes without destroying meaning
Tokenization alone is not enough. Treat codes as structured identifiers and preserve their hierarchy. Useful representations include:
- The full code and each valid parent category.
- Text descriptions supplied by an authoritative terminology source.
- Drug ingredients, strength, route, and formulation where available.
- Relationships such as “is-a,” “broader-than,” or “co-occurs-with.”
- Time intervals between diagnosis, prescription, administration, and outcome.
Avoid replacing a code with a single opaque token if the task benefits from hierarchy. Conversely, do not invent semantic relationships from string similarity. Two codes with similar prefixes may still describe clinically different conditions.
Use temporal splits for evaluation. Randomly splitting notes can place near-duplicate or copied-forward records in both training and test sets, producing inflated results. For patient-level tasks, keep all records from the same patient in one partition. For deployment claims, validate on a later period and, ideally, a separate institution.
Train and evaluate for clinical usefulness
Choose a baseline before fine-tuning an LLM. A rules-based dictionary, a conventional classifier, or a retrieval system may be sufficient for narrow extraction tasks and will provide a meaningful comparison.
Track metrics that match the task:
- Precision, recall, and F1 for code extraction.
- Recall at k and precision at k for candidate suggestions.
- Calibration and abstention rate for uncertain predictions.
- Performance by language, specialty, facility, and code frequency.
- Error rates for negation, temporality, rare diseases, and brand-generic mapping.
- Unsupported-claim rate and clinician-rated usefulness for generated summaries.
Evaluate rare and clinically consequential codes separately. A high overall score can hide poor performance on low-frequency conditions. Have clinicians review representative false positives and false negatives, and record whether each error could alter care, billing, research eligibility, or patient communication.
A robust system should be able to say “insufficient evidence” rather than forcing a code. Retrieval from a versioned terminology service can also be safer than asking a generative model to recall code descriptions from memory.
Privacy, licensing, and governance
Healthcare data requires more than removing names. Review quasi-identifiers, free-text identifiers, dates, rare conditions, location signals, and linkage risks. Apply access controls, encryption, retention limits, audit logs, and documented data-processing agreements.
Confirm that code descriptions, terminology files, and clinical datasets may be used for the intended training and commercial purpose. Keep a dataset card describing provenance, consent basis, exclusions, known gaps, and release restrictions. Maintain model cards with intended use, contraindications, evaluation populations, and escalation procedures.
For production systems, separate clinical decision support from autonomous diagnosis or prescribing. Use human review for high-impact outputs, show evidence spans where possible, and log model version, terminology version, prompt or input, output, and reviewer action. Monitor drift as coding practices and patient populations change.
A practical implementation sequence
1. Define one clinical task and its risk level.
2. Select the relevant ATC and ICD-10 versions and document mappings.
3. Build a de-identified, patient-level dataset with evidence spans.
4. Annotate negation, uncertainty, temporality, and medication status.
5. Create temporal, institution-held-out, and subgroup test sets.
6. Establish a transparent baseline before fine-tuning an LLM.
7. Test calibration, abstention, rare-code performance, and harmful errors.
8. Pilot with trained reviewers in a controlled workflow.
9. Monitor drift, audit samples, and update terminology deliberately.
Post-training optimization can reduce inference cost, but it does not repair weak labels or unsafe task definitions. If deployment costs are a constraint, evaluate quantization only after quality and calibration are stable; post-training quantization provides a useful engineering starting point.
Conclusion
ATC ICD10 codes LLM training works best as a structured clinical NLP project, not as a simple exercise in adding codes to a text corpus. Preserve hierarchy and time, distinguish administrative records from clinical truth, evaluate across Indian care settings, and design the model to abstain when evidence is weak. The strongest systems combine coded data with clinical text, terminology retrieval, expert review, and continuous governance.