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Victor et al [23] and Perron et al [24,25] applied machine learning algorithms such as random forest and k-nearest neighbors on caseworker summaries to identify domestic violence events and opioid-related maltreatment risks, respectively. In addition, Saxena et al [26] used topic modeling to explore the concept and definitions of “risk” on child maltreatment records with promising performance.
JMIR Pediatr Parent 2025;8:e73579
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Their method outperformed 3 baseline approaches, namely frequency-based, graph-based, and K-means centroid-based extractive summarization, demonstrating better content preservation and semantic alignment with the original notes.
In the study by Alsentzer and Kim [38], they explored extractive summarization of discharge notes from the MIMIC-III database.
JMIR Med Inform 2025;13:e66476
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Terminology and Definitions of Racial Health Equity in Prominent Health Websites: Systematic Review
J Med Internet Res 2025;27:e64868
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Breath-Focused Mindfulness and Compassion Training in Parent-Child Dyads: Pilot Intervention Study
JMIR Form Res 2025;9:e69607
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Bringing Executive Function Testing Online: Assessment Validation Study
JMIR Form Res 2025;9:e75687
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