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machine_learning [2018/07/21 13:06]
administrador
machine_learning [2018/10/21 10:03] (current)
administrador
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 ((Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med . 2016;​375(13):​1216-1219.)). ((Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med . 2016;​375(13):​1216-1219.)).
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 +Current [[outcome]]s [[prediction]] [[tool]]s are largely based on and limited by [[regression]] methods. Utilization of [[machine learning]] (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outcomes. [[Inpatient]] [[length of stay]] (LOS) is one such outcome that serves as a surrogate for patient [[disease]] severity and resource utilization.
 +
 +To develop a novel method to systematically rank, select, and combine ML algorithms to build a model that predicts LOS following [[craniotomy]] for [[brain tumor]].
 +
 +A training [[dataset]] of 41 222 patients who underwent craniotomy for brain tumor was created from the [[National Inpatient Sample]]. Twenty-nine ML algorithms were trained on 26 preoperative variables to predict LOS. Trained algorithms were ranked by calculating the root mean square logarithmic error (RMSLE) and top performing algorithms combined to form an ensemble. The ensemble was externally validated using a dataset of 4592 patients from the [[National Surgical Quality Improvement Program]]. Additional analyses identified variables that most strongly influence the ensemble model predictions.
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 +The ensemble model predicted LOS with RMSLE of .555 (95% confidence interval, .553-.557) on internal validation and .631 on external validation. Nonelective surgery, preoperative pneumonia, sodium abnormality,​ or weight loss, and non-White race were the strongest predictors of increased LOS.
 +
 +An ML ensemble model predicts LOS with good performance on internal and external validation, and yields clinical insights that may potentially improve patient outcomes. This systematic ML method can be applied to a broad range of clinical problems to improve patient care
 +((Muhlestein WE, Akagi DS, Davies JM, Chambless LB. Predicting Inpatient Length ​
 +of Stay After Brain Tumor Surgery: Developing [[Machine Learning]] Ensembles to
 +Improve Predictive Performance. Neurosurgery. 2018 Aug 3. doi:
 +10.1093/​neuros/​nyy343. [Epub ahead of print] PubMed PMID: 30113665.
 +)).
 +----
 +
 A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature.
  
machine_learning.txt · Last modified: 2018/10/21 10:03 by administrador