Public articles linked to the same research event.
Journal of Medical Internet Research Using 16,447 clinical notes from 6,382 patients with mental health diagnoses in the MIMIC-IV database, this study labeled sentiment from patient, physician, and general perspectives with two large language models (DeepSeek-7B and Mistral-7B) and three lexicon-based tools (ClinSent-lexicon, TextBlob, and VADER), finding substantial directional change in sentiment trajectories among patients with multiple admissions, greater fluctuation in Discharge Instructions than in Brief Hospital Course notes, more balanced patient-perspective sentiment versus predominantly neutral physician and general perspectives, better alignment of LLMs with patient-centered annotations than lexicon-based methods, and significantly more negative discharge-note sentiment trajectories among patients who died within 3
Using 16,447 clinical notes from 6,382 patients with mental health diagnoses in the MIMIC-IV database, this study labeled sentiment from patient, physician, and general perspectives with two large language models (DeepSeek-7B and Mistral-7B) and three lexicon-based tools (ClinSent-lexicon, TextBlob, and VADER), finding substantial directional change in sentiment trajectories among patients with multiple admissions, greater fluctuation in Discharge Instructions than in Brief Hospital Course notes, more balanced patient-perspective sentiment versus predominantly neutral physician and general perspectives, better alignment of LLMs with patient-centered annotations than lexicon-based methods, and significantly more negative discharge-note sentiment trajectories among patients who died within 3
Using 16,447 clinical notes from 6,382 patients with mental health diagnoses in the MIMIC-IV database, this study labeled sentiment from patient, physician, and general perspectives with two large language models (DeepSeek-7B and Mistral-7B) and three lexicon-based tools (ClinSent-lexicon, TextBlob, and VADER), finding substantial directional change in sentiment trajectories among patients with multiple admissions, greater fluctuation in Discharge Instructions than in Brief Hospital Course notes, more balanced patient-perspective sentiment versus predominantly neutral physician and general perspectives, better alignment of LLMs with patient-centered annotations than lexicon-based methods, and significantly more negative discharge-note sentiment trajectories among patients who died within 3
Using 16,447 clinical notes from 6,382 patients with mental health diagnoses in the MIMIC-IV database, this study labeled sentiment from patient, physician, and general perspectives with two large language models (DeepSeek-7B and Mistral-7B) and three lexicon-based tools (ClinSent-lexicon, TextBlob, and VADER), finding substantial directional change in sentiment trajectories among patients with multiple admissions, greater fluctuation in Discharge Instructions than in Brief Hospital Course notes, more balanced patient-perspective sentiment versus predominantly neutral physician and general perspectives, better alignment of LLMs with patient-centered annotations than lexicon-based methods, and significantly more negative discharge-note sentiment trajectories among patients who died within 3