Dynamic Topic Language Model on Heterogeneous Children's Mental Health
  Clinical Notes
Hanwen Ye Tatiana Moreno Adrianne Alpern Louis Ehwerhemuepha Annie Qu
Mental health diseases affect children's lives and well-beings which have
received increased attention since the COVID-19 pandemic. Analyzing psychiatric
clinical notes with topic models is critical to evaluate children's mental
status over time. However, few topic models are built for longitudinal
settings, and they fail to keep consistent topics and capture temporal
trajectories for each document. To address these challenges, we develop a
longitudinal topic model with time-invariant topics and individualized temporal
dependencies on the evolving document metadata. Our model preserves the
semantic meaning of discovered topics over time and incorporates heterogeneity
among documents. In particular, when documents can be categorized, we propose
an unsupervised topics learning approach to maximize topic heterogeneity across
different document groups. We also present an efficient variational
optimization procedure adapted for the multistage longitudinal setting. In this
case study, we apply our method to the psychiatric clinical notes from a large
tertiary pediatric hospital in Southern California and achieve a 38% increase
in the overall coherence of extracted topics. Our real data analysis reveals
that children tend to express more negative emotions during state shutdowns and
more positive when schools reopen. Furthermore, it suggests that sexual and
gender minority (SGM) children display more pronounced reactions to major
COVID-19 events and a greater sensitivity to vaccine-related news than non-SGM
children. This study examines the progression of children's mental health
during the pandemic and offers clinicians valuable insights to recognize the
disparities in children's mental health related to their sexual and gender
identities.