Our Work

Peer-reviewed publications and preprints from our group.

2024

  1. JM Mir, DB Kurland, ATM Cheung, A Liu, NA Shlobin, D Alber, S Rai, .... The evolution of pediatric spine surgery: a bibliometric analysis of publications from 1902 to 2023. Neurosurgery Practice 5 (3), e00092, 2024
  2. R Feng, AA Valliani, ML Martini, JS Gal, SN Neifert, NC Kim, EA Geng, .... Reliable prediction of discharge disposition following cervical spine surgery with ensemble machine learning and validation on a national cohort. Clinical Spine Surgery 37 (1), E30-E36, 2024
  3. DA Alber, A Cheung, DB Kurland, KL Sangwon, L Jiang, C Liu, .... 227 Spine-tuned Natural Language Models and Bespoke Regular Expression Classifiers for Automated Spinal Surgery Registry Development. Neurosurgery 70 (Supplement_1), 61, 2024

2023

  1. DB Kurland, ATM Cheung, NC Kim, K Ashayeri, T Hidalgo, .... A century of evolution in spine surgery publications: a bibliometric analysis of the field from 1900 to 2023. Neurosurgery 93 (5), 1121-1143, 2023
  2. ATM Cheung, DB Kurland, S Neifert, N Mandelberg, M Nasir-Moin, .... Developing an automated registry (autoregistry) of spine surgery using natural language processing and health system scale databases. Neurosurgery 93 (6), 1228-1234, 2023
  3. CL Bi, DB Kurland, R Ber, D Kondziolka, D Lau, D Pacione, .... Digital biomarkers and the evolution of spine care outcomes measures: Smartphones and wearables. Neurosurgery 93 (4), 745-754, 2023
  4. A Cheung, I Laufer, DR Pacione, D Lau, A Frempong-Boadu, .... Automated Development of a Spine Registry (Autoregistry) using an Interpretable Surgeon-Written Regular Expression Classifier. NEUROSURGERY 69, 70-70, 2023
  5. A Cheung, I Laufer, DR Pacione, D Lau, A Frempong-Boadu, .... 321 Automated Development of a Spine Registry (Autoregistry) using an Interpretable Surgeon-Written Regular Expression Classifier. Neurosurgery 69 (Supplement_1), 46-47, 2023

2022

  1. AA Valliani, NC Kim, ML Martini, JS Gal, SN Neifert, R Feng, EA Geng, .... Robust prediction of non-home discharge after thoracolumbar spine surgery with ensemble machine learning and validation on a nationwide cohort. World Neurosurgery 165, e83-e91, 2022
  2. AA Valliani, R Feng, ML Martini, SN Neifert, NC Kim, JS Gal, EK Oermann, .... Pragmatic prediction of excessive length of stay after cervical spine surgery with machine learning and validation on a national scale. Neurosurgery 91 (2), 322-330, 2022

2021

  1. ML Martini, SN Neifert, JS Gal, EK Oermann, JT Gilligan, JM Caridi. Drivers of prolonged hospitalization following spine surgery: a game-theory-based approach to explaining machine learning models. JBJS 103 (1), 64-73, 2021
  2. ML Martini, SN Neifert, EK Oermann, JT Gilligan, RJ Rothrock, FJ Yuk, .... Application of cooperative game theory principles to interpret machine learning models of nonhome discharge following spine surgery. Spine 46 (12), 803-812, 2021
  3. AAA Valliani, M Martini, S Neifert, J Gal, R Feng, N Kim, J Caridi, .... Reliable Prediction of Extended Length of Stay Following Non-fusion Spine Procedures using Machine Learning Validated on Nearly One Million Cases in the United States. JOURNAL OF NEUROSURGERY 135 (2), 69-69, 2021

2020

  1. BH Cho, D Kaji, ZB Cheung, IB Ye, R Tang, A Ahn, O Carrillo, JT Schwartz, .... Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision. Global spine journal 10 (5), 611-618, 2020
  2. ML Martini, SN Neifert, EK Oermann, J Gal, K Rajan, DA Nistal, JM Caridi. Machine learning with feature domains elucidates candidate drivers of hospital readmission following spine surgery in a large single-center patient cohort. Neurosurgery 87 (4), E500-E510, 2020

2019

  1. RS Bronheim, EK Oermann, DS Bronheim, JM Caridi. Revised cardiac risk index as a predictor for myocardial infarction and cardiac arrest following posterior lumbar decompression. Spine 44 (3), E187-E193, 2019

2018

  1. JS Kim, RK Merrill, V Arvind, D Kaji, SD Pasik, CC Nwachukwu, L Vargas, .... Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine 43 (12), 853-860, 2018
  2. JS Kim, V Arvind, EK Oermann, D Kaji, W Ranson, C Ukogu, AK Hussain, .... Predicting surgical complications in patients undergoing elective adult spinal deformity procedures using machine learning. Spine deformity 6 (6), 762-770, 2018
  3. V Arvind, JS Kim, EK Oermann, D Kaji, SK Cho. Predicting surgical complications in adult patients undergoing anterior cervical discectomy and fusion using machine learning. Neurospine 15 (4), 329, 2018
  4. R Feng, M Finkelstein, K Bilal, EK Oermann, M Palese, J Caridi. Trends and disparities in cervical spine fusion procedures utilization in the New York State. Spine 43 (10), E601-E606, 2018
  5. RS Bronheim, EK Oermann, SK Cho, JM Caridi. Coagulation profile as a risk factor for 30-day morbidity following cervical laminectomy and fusion. Spine 43 (4), 239-247, 2018
  6. JS Kim, RK Merrill, V Arvind, D Kaji, SD Pasik, CC Nwachukwu, L Vargas, .... Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine (Phila Pa 1976 …. 2018

2017

  1. RS Bronheim, EK Oermann, SK Cho, JM Caridi. Coagulation profile as a risk factor for 30-day morbidity and mortality following posterior lumbar fusion. Spine 42 (12), 950-957, 2017
  2. R Feng, M Finkelstein, EK Oermann, M Palese, JM Caridi. Trends and Disparities in Cervical Spine Fusion Procedure Utilization in the New York State. NEUROSURGERY 64, 276-276, 2017

2010

  1. EK Oermann, ND Coppa, M Margolis, FA Sandhu. Extramedullary hematopoietic tumor mimicking a thoracic nerve root schwannoma: Case report. Journal of Neurosurgery: Spine 13 (1), 78-81, 2010

Unknown

  1. JS Kim, RK Merrill, V Arvind, D Kaji, SD Pasik, CC Nwachukwu, L Vargas, .... Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion., 2018, 43. DOI: https://doi. org/10.1097/BRS 2442, 853-860, 0,