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Saisanjana Kalagara, Adam E. M. Eltorai, Wesley M. Durand, J. Mason DePasse and Alan H. Daniels

the present study was to analyze factors associated with 30-day hospital readmission following lumbar laminectomy and to create machine learning–based models to predict these readmissions. 38 We hypothesized that the factors most influential in forecasting readmission would be postoperative complications and preoperative patient health status, such as preexisting health conditions and dependency levels. Methods Patient Selection Data were obtained from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database. This annually

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Alessandro Siccoli, Marlies P. de Wispelaere, Marc L. Schröder and Victor E. Staartjes

is interpretable and clinically valuable for both patients and surgeons. 38 Recently, interest has shifted toward machine learning (ML) algorithms in predictive modeling. 10 , 20 , 29 While some ML algorithms have been around for decades, they have only recently gained major interest for predictive analytics in medicine. 31 , 32 This increase in the use of ML is attributable to the advent of the “big data” era, but also to the development of new algorithms and steadily improving computational power. 25 Various ML techniques such as gradient-boosting machines

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Todd C. Hollon, Adish Parikh, Balaji Pandian, Jamaal Tarpeh, Daniel A. Orringer, Ariel L. Barkan, Erin L. McKean and Stephen E. Sullivan

that specific patient characteristics (e.g., tumor type, age, and body mass index [BMI]) are likely to vary in predictive importance across the entire patient population. Advances in applied predictive modeling using machine learning have provided a novel method for predicting outcomes in healthcare. 5 Machine learning models have an advantage over other predictive methods because machine learning enables a predictive computer model to automatically learn the best predictive features present in training data. As opposed to the use of a human operator to manually

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Thara Tunthanathip, Sakchai Sae-heng, Thakul Oearsakul, Ittichai Sakarunchai, Anukoon Kaewborisutsakul and Chin Taweesomboonyat

S urgical site infection (SSI) following neurosurgical operations is a burdensome complication in the field. Such complications can impact morbidity, mortality, and economics. 4 , 5 , 25 O’Keeffe et al. conducted a cost analysis of craniotomy infections, identifying an estimated cost per case of infection at £9283. The financial burden caused by craniotomy infections is often compounded by the direct costs incurred by prolonged hospitalization of the patient, diagnostic tests, treatment, and reoperation. 4 , 18 Machine learning (ML) is used for outcome

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Andrew T. Hale, David P. Stonko, Li Wang, Megan K. Strother and Lola B. Chambless

T hroughout the medical and surgical literature, it has become increasingly important to develop methods to predict outcomes, complications, and prognoses for given disease states. Specifically, it is often critical to be able to discriminate between binary characteristics or outcomes in the clinical setting (e.g., death or survival, benign or malignant, etc.). 17 , 18 , 32–34 Over the past several decades, as machine learning (ML) algorithms and artificial intelligence (AI) have improved, the modeling choices available to overcome these diagnostic and

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Nikhil Paliwal, Prakhar Jaiswal, Vincent M. Tutino, Hussain Shallwani, Jason M. Davies, Adnan H. Siddiqui, Rahul Rai and Hui Meng

, and the post-treatment shear rate were significantly different between occluded and nonoccluded IAs after 6 months of FD treatment. However, it remains unclear if these parameters can predict the FD treatment outcome. In order to develop models for predicting clinical outcome of FD-treated IAs, we surveyed potential candidate algorithms. In IA research, multivariate logistic regression analysis of untreated morphological and hemodynamic parameters has been used to classify aneurysm rupture status. 39 In other areas of medical research, novel machine learning (ML

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Predicting nonroutine discharge after elective spine surgery: external validation of machine learning algorithms

Presented at the 2019 AANS/CNS Joint Section on Disorders of the Spine and Peripheral Nerves

Brittany M. Stopa, Faith C. Robertson, Aditya V. Karhade, Melissa Chua, Marike L. D. Broekman, Joseph H. Schwab, Timothy R. Smith and William B. Gormley

mobility during postoperative inpatient stay; lower sitting and standing balance score; longer length of stay; and surgical complications. 3 , 14 , 20 , 25 Our team recently developed a machine learning algorithm from national surgical data that predicts nonhome discharge of patients undergoing elective spine surgery. 16 The algorithm was derived from more than 26,000 patients in the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) who underwent surgery for lumbar degenerative disc disorders, and was able to predict nonroutine

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Aditya V. Karhade, Paul Ogink, Quirina Thio, Marike Broekman, Thomas Cha, William B. Gormley, Stuart Hershman, Wilco C. Peul, Christopher M. Bono and Joseph H. Schwab

as calibration and decision analysis. Furthermore, none have applied modern techniques such as machine learning. The purposes of this study were as follows: 1) to develop machine learning models for preoperative prediction of nonroutine discharge in patients undergoing elective spine surgery for lumbar disc displacement or disc degeneration in a large multiinstitutional surgical registry and 2) to deploy these models as open-access web applications for healthcare professionals. Methods Guidelines The Transparent Reporting of multivariable Prediction Models for

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Whitney E. Muhlestein, Dallin S. Akagi, Amy R. McManus and Lola B. Chambless

this type of surgery, hampering the ability of providers, insurance companies, and patients to allocate resources appropriately or to develop cost-saving measures. Interest in using machine learning (ML) models to predict hospital charges has grown considerably in light of skyrocketing costs of healthcare. For example, ML models for colorectal and gastric cancer have been used to predict hospital charges and identify targets for cost containment. 21 , 30 ML models have also been shown to increase physician awareness of total charge predictors and have been used to

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Thiago Augusto Hernandes Rocha, Cyrus Elahi, Núbia Cristina da Silva, Francis M. Sakita, Anthony Fuller, Blandina T. Mmbaga, Eric P. Green, Michael M. Haglund, Catherine A. Staton and Joao Ricardo Nickenig Vissoci

LMICs because models for HICs may perform badly when extrapolated to poorer settings. 36 Another limitation of previous prognostic models is the use of clinical trial data for model development, which could limit external validity. 23 An increased prevalence of prospectively collected registry data and greater electronic health record use in LMICs provide an opportunity to develop culturally and contextually relevant prognostic models. Machine learning (ML), a branch of artificial intelligence, is a powerful approach to model development able to automatically