Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method

Kamber L Hart; Amelia M Pellegrini; Brent P Forester; Sabina Berretta; Shawn N Murphy; Roy H Perlis; Thomas H McCoy Jr

doi:10.1016/j.genhosppsych.2020.11.003

Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method

Gen Hosp Psychiatry. 2021 Jan-Feb:68:46-51. doi: 10.1016/j.genhosppsych.2020.11.003. Epub 2020 Nov 10.

Authors

Kamber L Hart¹, Amelia M Pellegrini², Brent P Forester³, Sabina Berretta⁴, Shawn N Murphy⁵, Roy H Perlis⁶, Thomas H McCoy Jr⁷

Affiliations

¹ Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: klhart@partners.org.
² Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: apellegrini@mail.missouri.edu.
³ Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; McLean Hospital, 115 Mill St, Belmont, MA 02478, USA. Electronic address: bforester@partners.org.
⁴ Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; McLean Hospital, 115 Mill St, Belmont, MA 02478, USA. Electronic address: sberretta@mclean.harvard.edu.
⁵ Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA. Electronic address: snmurphy@partners.org.
⁶ Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA. Electronic address: rperlis@partners.org.
⁷ Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA. Electronic address: thmccoy@partners.org.

Abstract

Background: Agitation is a common feature of many neuropsychiatric disorders.

Objective: Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases.

Methods: We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined.

Results: Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype.

Conclusion: Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Keywords: Data mining; Electronic health records; Machine learning; Natural language processing; Psychomotor agitation.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Anxiety
Depressive Disorder, Major*
Humans
Natural Language Processing
Psychomotor Agitation / epidemiology
Psychotic Disorders*

Abstract

Publication types

MeSH terms

Grants and funding