Do Clinical Notes Improve Early Prediction of Mortality or Prolonged ICU Stay?

Studies that add clinical notes to structured electronic health record data report gains in early prediction of in-hospital death or prolonged ICU stay ranging from none to 0.064 in the area under the receiver operating characteristic curve (AUROC). Each study measures that gain against a different structured baseline. In 25,692 MIMIC-III admissions, we held the cohort, outcome and notes fixed and varied only the structured baseline. Against 21 structured predictors, notes increased the AUROC by 0.065; against 223, by 0.010 under gradient boosting and 0.002 under logistic regression, declines of 85% and 97%. Varying the baseline alone reproduced the size of the published range within one cohort. Note-processing tools usually need adaptation to each hospital’s documentation, so ICU and hospital leaders weighing that investment need to know what a reported gain was measured against.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-27
DOI
https://doi.org/10.5281/zenodo.23682849
Primary Topic
Machine Learning in Healthcare
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Do Clinical Notes Improve Early Prediction of Mortality or Prolonged ICU Stay?

Harman Brah, Nisha Padhi
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
preprint

Do Clinical Notes Improve Early Prediction of Mortality or Prolonged ICU Stay?

Harman Brah, Nisha Padhi
preprint en

Abstract

Studies that add clinical notes to structured electronic health record data report gains in early prediction of in-hospital death or prolonged ICU stay ranging from none to 0.064 in the area under the receiver operating characteristic curve (AUROC). Each study measures that gain against a different structured baseline. In 25,692 MIMIC-III admissions, we held the cohort, outcome and notes fixed and varied only the structured baseline. Against 21 structured predictors, notes increased the AUROC by 0.065; against 223, by 0.010 under gradient boosting and 0.002 under logistic regression, declines of 85% and 97%. Varying the baseline alone reproduced the size of the published range within one cohort. Note-processing tools usually need adaptation to each hospital’s documentation, so ICU and hospital leaders weighing that investment need to know what a reported gain was measured against.

Zenodo (CERN European Organization for Nuclear Research)
Aspiring Scholars Directed Research Program (US)
Machine Learning in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon