N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.
BACKGROUND
Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times.


OBJECTIVE
To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment.


METHODS
We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx).


RESULTS
Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task.


CONCLUSIONS
Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures.


CONCLUSIONS
SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods.
Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco, California, USA.
Ben J
BJ
Marafino
Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco, California, USA Department of Neurosurgery, University of California, San Francisco, California, USA.
Jason M
JM
Davies
Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco, California, USA Department of Pediatrics, University of California, San Francisco, California, USA.
Naomi S
NS
Bardach
Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco, California, USA.
Mitzi L
ML
Dean
Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco, California, USA Center for Healthcare Value, University of California, San Francisco, California, USA Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA Department of Medicine, University of California, San Francisco, California, USA.
R Adams
RA
Dudley
D000328:Adult; D002965:Classification / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification; D006801:Humans; D007231:Infant, Newborn; D016247:Information Storage and Retrieval; D007362:Intensive Care Units; D007567:Jaundice, Neonatal / Q000145:classification / Q000175:diagnosis; D010789:Phototherapy / Q000706:statistics & numerical data; D012121:Respiration, Artificial / Q000706:statistics & numerical data; D060388:Support Vector Machine
ICU; Risk adjustment; SVM; classification; n-gram