Journal ArticleDOI
Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review
Joeky T. Senders,Patrick Staples,Aditya V. Karhade,Mark M. Zaki,William B. Gormley,Marike L. D. Broekman,Timothy R. Smith,Omar Arnaout +7 more
TLDR
Based on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively.About:
This article is published in World Neurosurgery.The article was published on 2018-01-01. It has received 263 citations till now. The article focuses on the topics: Receiver operating characteristic.read more
Citations
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Journal ArticleDOI
Logistic regression was as good as machine learning for predicting major chronic diseases.
Simon Nusinovici,Yih Chung Tham,Marco Yu Chak Yan,Daniel Shu Wei Ting,Jialiang Li,Charumathi Sabanayagam,Tien Yin Wong,Ching-Yu Cheng +7 more
TL;DR: Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors in a prospective cohort study in Asian adults.
Journal ArticleDOI
Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.
Quinlan D. Buchlak,Nazanin Esmaili,Jean-Christophe Leveque,Farrokh Farrokhi,Christine Bennett,Massimo Piccardi,Rajiv K. Sethi,Rajiv K. Sethi +7 more
TL;DR: This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied, and identified gaps in the literature and opportunities for future neuros surgical ML research.
Journal ArticleDOI
An introduction and overview of machine learning in neurosurgical care.
Joeky T. Senders,Joeky T. Senders,Mark M. Zaki,Aditya V. Karhade,Bliss J. Chang,William B. Gormley,Marike L. D. Broekman,Marike L. D. Broekman,Timothy R. Smith,Omar Arnaout +9 more
TL;DR: Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.
Journal ArticleDOI
Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion.
Aditya V. Karhade,Paul T. Ogink,Quirina C. B. S. Thio,Marike L. D. Broekman,Thomas D. Cha,Stuart H. Hershman,Jianren Mao,Wilco C. Peul,Andrew J. Schoenfeld,Christopher M. Bono,Joseph H. Schwab +10 more
TL;DR: Machine learning algorithms could be used to preoperatively stratify risk these patients, possibly enabling early intervention to reduce the potential for long-term opioid use in this population of patients.
Journal ArticleDOI
Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.
Prem N. Ramkumar,Sergio M. Navarro,Heather S. Haeberle,Jaret M. Karnuta,Michael A. Mont,Joseph P. Iannotti,Brendan M. Patterson,Viktor E. Krebs +7 more
TL;DR: A preliminary machine learning algorithm demonstrated excellent construct validity, reliability, and responsiveness predicting LOS and payment prior to primary THA, which has the potential to allow for a risk-based PSPM prior to elective THA that offers tiered reimbursement commensurate with case complexity.
References
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Journal ArticleDOI
Machine learning: Trends, perspectives, and prospects
TL;DR: The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
TL;DR: Algorithmic models have been widely used in fields outside statistics as discussed by the authors, both in theory and practice, and can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets.
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Machine Learning in Medicine.
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Machine learning applications in cancer prognosis and prediction.
Konstantina Kourou,Themis P. Exarchos,Konstantinos P. Exarchos,Michalis V. Karamouzis,Dimitrios I. Fotiadis +4 more
TL;DR: Given the growing trend on the application of ML methods in cancer research, this work presents here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
Journal ArticleDOI
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