Generalization of Machine Learning Approaches to Identify Notifiable Conditions from a Statewide Health Information Exchange.

Autor: Dexter GP; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA., Grannis SJ; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA.; Indiana University School of Medicine, Indianapolis, IN, USA., Dixon BE; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA.; Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA., Kasthurirathne SN; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA.; Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA.

Publikováno v: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2020 May 30; Vol. 2020, pp. 152-161. Date of Electronic Publication: 2020 May 30 (Print Publication: 2020).

Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs).

Autor: Belfield, Samuel J.¹ (AUTHOR), Cronin, Mark T.D.¹ (AUTHOR), Enoch, Steven J.¹ (AUTHOR), Firman, James W.¹ (AUTHOR) j.w.firman@ljmu.ac.uk

Publikováno v: PLoS ONE. 5/10/2023, Vol. 18 Issue 5, p1-29. 29p.

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

Autor: Nikparvar, Behnam¹ (AUTHOR), Rahman, Md. Mokhlesur^1,2 (AUTHOR), Hatami, Faizeh³ (AUTHOR), Thill, Jean-Claude^3,4 (AUTHOR) jfthill@uncc.edu

Publikováno v: Scientific Reports. 11/5/2021, Vol. 11 Issue 1, p1-12. 12p.

A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.

Autor: Megjhani, Murad, Terilli, Kalijah, Weinerman, Bennett, Nametz, Daniel, Kwon, Soon Bin, Velazquez, Angela, Ghoshal, Shivani, Roh, David J., Agarwal, Sachin, Connolly, E. Sander, Claassen, Jan, Park, Soojin

Publikováno v: Annals of Neurology; Jul2023, Vol. 94 Issue 1, p196-202, 7p

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort.

Autor: Feng R; Departments of Neurosurgery., Valliani AA; Departments of Neurosurgery., Martini ML; Departments of Neurosurgery., Gal JS; Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai., Neifert SN; Department of Neurosurgery, New York University Langone Medical Center., Kim NC; Department of Neurosurgery, New York University Langone Medical Center., Geng EA; Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai., Kim JS; Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai., Cho SK; Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai., Oermann EK; Department of Neurosurgery, New York University Langone Medical Center.; Department of Radiology, New York University Langone Medical Center.; Center for Data Science, New York University Langone Medical Center, New York, NY., Caridi JM; Department of Neurosurgery, University of Texas Health Science Center, Houston, TX.

Publikováno v: Clinical spine surgery [Clin Spine Surg] 2024 Feb 01; Vol. 37 (1), pp. E30-E36. Date of Electronic Publication: 2024 Jan 29.