  Objective: To investigate GPT-3.5 in generating and coding medical documents
with ICD-10 codes for data augmentation on low-resources labels.
  Materials and Methods: Employing GPT-3.5 we generated and coded 9,606
discharge summaries based on lists of ICD-10 code descriptions of patients with
infrequent (generation) codes within the MIMIC-IV dataset. Combined with the
baseline training set, this formed an augmented training set. Neural coding
models were trained on baseline and augmented data and evaluated on a MIMIC-IV
test set. We report micro- and macro-F1 scores on the full codeset, generation
codes, and their families. Weak Hierarchical Confusion Matrices were employed
to determine within-family and outside-of-family coding errors in the latter
codesets. The coding performance of GPT-3.5 was evaluated both on prompt-guided
self-generated data and real MIMIC-IV data. Clinical professionals evaluated
the clinical acceptability of the generated documents.
  Results: Augmentation slightly hinders the overall performance of the models
but improves performance for the generation candidate codes and their families,
including one unseen in the baseline training data. Augmented models display
lower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by the
prompted descriptions, but performs poorly on real data. Evaluators note the
correctness of generated concepts while suffering in variety, supporting
information, and narrative.
  Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding.
Augmentation positively affects generation code families but mainly benefits
codes with existing examples. Augmentation reduces out-of-family errors.
Discharge summaries generated by GPT-3.5 state prompted concepts correctly but
lack variety, and authenticity in narratives. They are unsuitable for clinical
practice.
