Evidence map›Paper›PMID 37753238›Full record

Observational studyCritical care explorations2023

Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record.

Chris J Kennedy, Catherine Chiu, Allyson Cook Chapman, Oksana Gologorskaya, Hassan Farhan, Mary Han, MacGregor Hodgson, Daniel Lazzareschi, Deepshikha Ashana, Sei Lee and 5 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Critical care explorations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Chris J KennedyDepartment of Psychiatry, Harvard Medical School, Boston, MA.
Catherine ChiuDepartment of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA.
Allyson Cook ChapmanCritical Care and Palliative Medicine, Department of Internal Medicine, University of California San Francisco, San Francisco, CA.
Oksana GologorskayaBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA.
Hassan FarhanDepartment of Anesthesiology, Perioperative and Pain Management, Stanford University, Stanford, CA.
Mary HanCenter for Precision Psychiatry, Massachusetts General Hospital, Boston, MA.
MacGregor HodgsonCenter for Precision Psychiatry, Massachusetts General Hospital, Boston, MA.
Daniel LazzareschiCenter for Precision Psychiatry, Massachusetts General Hospital, Boston, MA.
Deepshikha AshanaDivision of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC.
Sei LeeDivision of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA.
Alexander K SmithDivision of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA.
Edie EspejoDivision of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA.
John BoscardinDivision of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA.
Romain PirracchioDepartment of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA.
Julien CobertDepartment of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA.

Funding

VARC CoreP30AG044281 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Louise C. Walter · 2013 to 2026
$19.9M
Prognostic calculators for patients with Alzheimers disease and related dementiasR01AG057751 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI LEE, SEI, SMITH, ALEXANDER KELIIMOEANU · 2018 to 2021
$1.5M
Mentoring Researchers in Prognosis Research in Alzheimer’s Disease and Related DementiasK24AG068312 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Alexander Keliimoeanu Smith · 2020 to 2026
$1.2M
Mentoring Patient-Oriented Research to Individualize Care for Hospitalized Older Adults with and without Alzheimer's Disease and Related DementiasK24AG066998 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Sei Lee · 2021 to 2026
$1.1M
FDA HHS U01 FD005978NIA NIH HHS K24 AG066998NIA NIH HHS K24 AG068312NIA NIH HHS P30 AG044281NIA NIH HHS R01 AG057751
6 · The paper itself

Abstract

objectivesTo develop proof-of-concept algorithms using alternative approaches to capture provider sentiment in ICU notes.

designRetrospective observational cohort study.

settingThe Multiparameter Intelligent Monitoring of Intensive Care III (MIMIC-III) and the University of California, San Francisco (UCSF) deidentified notes databases. PATIENTS: Adult (≥18 yr old) patients admitted to the ICU. MEASUREMENTS AND MAIN

resultsWe developed two sentiment models: 1) a keywords-based approach using a consensus-based clinical sentiment lexicon comprised of 72 positive and 103 negative phrases, including negations and 2) a Decoding-enhanced Bidirectional Encoder Representations from Transformers with disentangled attention-v3-based deep learning model (keywords-independent) trained on clinical sentiment labels. We applied the models to 198,944 notes across 52,997 ICU admissions in the MIMIC-III database. Analyses were replicated on an external sample of patients admitted to a UCSF ICU from 2018 to 2019. We also labeled sentiment in 1,493 note fragments and compared the predictive accuracy of our tools to three popular sentiment classifiers. Clinical sentiment terms were found in 99% of patient visits across 88% of notes. Our two sentiment tools were substantially more predictive (Spearman correlations of 0.62-0.84,

conclusionOur exploratory healthcare-specific sentiment models can more accurately detect positivity and negativity in clinical notes compared with general sentiment tools not designed for clinical usage.

Indexed as

Electronic Health RecordsIntensive Care UnitsAdultAgedAlgorithmsCohort StudiesDeep LearningFemaleHumansMaleMiddle AgedNatural Language ProcessingRetrospective StudiesSan Franciscocomputer-assisted decision-makingcritical carecritical care outcomesnatural language processingsentiment analysis

Identifiers

PMID37753238
PMCPMC10519480

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.