Nora C. Kim
9 Papers
Nora C. Kim is an academic researcher. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 2, co-authored 7 publications.
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Papers
Health system-scale language models are all-purpose prediction engines
Lavender Yao Jiang,Xujin Liu,Mustafa Nasir-Moin,Duo Wang,Anas A. Abidin,Howard A. Riina,Paawan V. Punjabi,Madeline Miceli,Nora C. Kim,Cordelia Orillac,Zane Schnurman,Hannah Weiss,D. Midian Kurland,Yosef M. Dastagirzada,Douglas Kondziolka,Alexander T. M. Cheung,Grace Yang,Mingzi Cao,Mona Flores,Anthony Costa,Yin Aphinyanaphongs,Kyunghyun Cho,Eric Oermann +22 more
TL;DR: In this article , the authors use unstructured clinical notes from the electronic health record (EHR) to enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment.
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Management and surgical outcomes of dystrophic scoliosis in neurofibromatosis type 1: a systematic review.
Sean N Neifert,Hammad A. Khan,David B. Kurland,Nora C. Kim,Kaleb Yohay,Devorah Segal,Amer F. Samdani,Steven W. Hwang,Darryl Lau +8 more
TL;DR: A systematic review on the natural history, management options, and surgical outcomes in patients who underwent NF1 dystrophic scoliosis surgery in a variety of approaches: posterior-only, combined anterior-posterior, and growth-friendly surgery.
Robust Prediction of Non-home Discharge After Thoracolumbar Spine Surgery with Ensemble Machine Learning and Validation on a Nationwide Cohort.
Aly A. Valliani,Nora C. Kim,Michael L Martini,Jonathan S. Gal,Sean N Neifert,Rui Feng,Eric E. Geng,Juno Kim,Samuel K. Cho,Eric K. Oermann,John M. Caridi +10 more
TL;DR: In this paper , the authors developed a robust machine learning algorithm to predict non-home discharge after thoracolumbar spine surgery that generalizes to unseen populations and identifies markers for prediction.
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Early Experience of Surgical Planning for STA-MCA Bypass Using Virtual Reality.
Nora C. Kim,Karl L. Sangwon,Eytan Raz,Maksim Shapiro,Caleb Rutledge,Peter Kim Nelson,Howard A. Riina,Erez Nossek +7 more
TL;DR: In this paper , the authors used 3D virtual reality (VR) models to optimize the planning of STA-MCA bypass in 30 patients from August 2020 to February 2022, and the results showed that VR can serve as a useful, interactive preoperative planning tool by enhancing visualization of the spatial relationship between the STA and MCA without compromising the surgical results.
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