Artificial intelligence for detecting anaphylaxis in electronic medical records.

BACKGROUND

Despite established criteria, diagnosing anaphylaxis remains challenging but critical for preventing future reactions. Fast-paced clinical settings, compounded by underrecording in electronic medical records (EMRs), increase the risk of dangerous re-exposures. Leveraging artificial intelligence through automated systems such as large language models (LLMs) offers a solution.

OBJECTIVE

This study aims to assess the efficacy of artificial intelligence, specifically LLMs, in autonomously identifying anaphylaxis diagnoses from EMR text to enhance patient safety and optimize care delivery.

METHODS

LLMs (GPT 3.5, 4, and 4 Turbo) analyzed 969 medical texts in Brazilian Portuguese, annotated as anaphylaxis-positive (48) or negative (921) by 3 expert physicians. A primary prompt simulated a general practitioner's role in reviewing medical narratives for anaphylaxis detection, with a secondary prompt incorporating World Allergy Organization (WAO) criteria. The experiments were conducted using 3 GPT configurations. The diagnostic suggestions of the LLM were compared to the physicians' diagnoses. Precision, sensitivity (recall), specificity, and accuracy values were calculated.

RESULTS

Using the primary prompt, GPT 4 Turbo detected anaphylaxis cases with 90.6% precision, 100% sensitivity, 99.5% specificity, 99.5% accuracy, and a Cohen kappa coefficient of 0.95. The inclusion of WAO criteria slightly improved the performance of older models (GPT 3.5 + 4 configuration). However, for GPT 4 Turbo, additional information did not enhance precision.

CONCLUSION

The results highlight the potential of artificial intelligence, particularly LLMs, to automate anaphylaxis diagnosis, support healthcare professionals, and improve patient safety and care.