作者
Aditya V Karhade, Akash A Shah, Christopher M Bono, Marco L Ferrone, Sandra B Nelson, Andrew J Schoenfeld, Mitchel B Harris, Joseph H Schwab
发表日期
2019/12/1
期刊
The Spine Journal
卷号
19
期号
12
页码范围
1950-1959
出版商
Elsevier
简介
Background Context
In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning.
Purpose
The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA.
Study Design/Setting
Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016.
Patients Sample
Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA.
Outcome Measures
In-hospital and 90-day postdischarge mortality.
Methods
Five machine learning algorithms (elastic …
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AV Karhade, AA Shah, CM Bono, ML Ferrone… - The Spine Journal, 2019