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A co-learning framework for maternal and pediatric pharmacotherapy knowledge gap discovery
Xiaofu Liu1, Aislinn M O'Kane2, Lei Wang1
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States.
Objective:
Pregnant and breastfeeding women, along with children, represent populations persistently underrepresented in clinical research, leading to uncertainty in clinical decision-making. This study investigated the landscape of pharmacology research concerning pharmacokinetics (PK), pharmaco-epidemiology (PE), and clinical trials (CT) within maternal and pediatric patient populations.
Materials And Methods:
A pharmacology research landscape in maternal and pediatric patient populations was analyzed using a novel co-learning framework applied to 38 million PubMed abstracts. It incorporated an evolving annotation guideline for abstract labeling, uncertain sampling, annotation error calibration, and large language model training. The co-learning framework enabled human annotators and language models to iteratively learn from each other, optimizing the classification of PK/PE/CT papers. Analysis was restricted to medications prescribed for maternal and pediatric patients, focusing on publication evidence.
Results:
Co-learning significantly increased the F1-scores for classifying the CT, PE, and PK abstracts from (0.81, 0.84, 0.73) to (0.96, 0.94, 0.93), respectively. On the external validation set, the F1-score increased from (0.81, 0.60, 0.42) to (0.95, 0.88, 0.91) for CT, PE, and PK, respectively. The models identified 376 231 and 879 936 abstracts relevant to maternal and pediatric patients, respectively. In pregnant or postpartum women, 22%-66% prescribed drugs lacked either PK or PE/CT evidence. Among pediatric patients, 8%-40% prescribed drugs had no or weak PK or PE/CT evidence.
Discussion:
Co-learning is a more effective approach than active learning in obtaining feedback from human annotators for optimizing the language model. With sufficiently labeled samples, a language model such as BioBERT outperformed large language models.
Conclusion:
In both maternal and pediatric pharmacology research, the absence of PK evidence was notably more frequent compared to PE and CT. This significant finding calls for more pharmacokinetics studies within pediatric and maternal patient populations.