OBJECTIVES: Past studies have shown that many clinical machine learning models have performance limitations due to imbalances in the training data. For voice data generation projects, the origin of the problem may lie in the recruiting methods used during data collection efforts. This study introduces a generative AI pipeline for "dataset decision support", recommending recruitment decisions based on high-dimensional insights.

METHODS: The publicly available GOSSIS-1-eICU dataset was filtered to create patient populations that were relevant to voice data generation projects. Lab results and vital signs from the electronic health record were also used to train a neural network for prediction of disease type. Prediction uncertainty estimates were included in the dataset as approximate indicators of health complexity. To select the best recruitment choice for addressing imbalances, an open-source large language model (LLM) was then instructed to assess dataset statistics and the characteristics of possible participants. Simulations were run in which the system constructed datasets of 250 patients.

RESULTS: In over 90% of cases, the proposed system reduced categorical imbalances and widened continuous distributions when compared to randomly sampled counterfactual datasets (q-value < 0.05). Variables included race, age, BMI, sex, disease type, oxygenation status, co-morbidities, post-operative status, Glasgow Coma Scale verbal response score, the Acute Physiology Score III, prediction uncertainty, vital signs, and lab results.

CONCLUSION: LLMs may provide useful, explainable recommendations when presented with dataset distribution statistics and candidate profiles. In the future, this simulated scenario may be extended to align with conditions in emergency departments or other high-volume settings.

LEVEL OF EVIDENCE: 3.