Publications
Machine and deep learning methods for radiomics
Avanzo, M,
Wei, L,
Stancanello, J,
Vallières, M,
Rao, A,
Morin, O,
Mattonen, SA,
El Naqa, I,
Med Phys, 2020 Jun; DOI:10.1002/mp.13678 Patient factors associated with discrepancies between patient-reported and clinician-documented peripheral neuropathy in women with breast cancer receiving paclitaxel: A pilot study
Salgado, TM,
Liu, J,
Reed, HL,
Quinn, CS,
Syverson, JG,
Le-Rademacher, J,
Lopez, CL,
Beutler, AS,
Loprinzi, CL,
Vangipuram, K,
Smith, EML,
Henry, NL,
Farris, KB,
Hertz, DL,
Breast, 2020 Jun; DOI:10.1016/j.breast.2020.02.011 Regional Variance in Disability and Quality-of-Life Outcomes After Surgery for Grade I Degenerative Lumbar Spondylolisthesis: A Quality Outcomes Database Analysis
Sherrod, BA,
Mummaneni, PV,
Alvi, MA,
Chan, AK,
Bydon, M,
Glassman, SD,
Foley, KT,
Potts, EA,
Shaffrey, ME,
Coric, D,
Knightly, JJ,
Park, P,
Wang, MY,
Fu, K-M,
Slotkin, JR,
Asher, AL,
Virk, MS,
Bisson, EF,
World Neurosurg, 2020 Jun; DOI:10.1016/j.wneu.2020.02.117 The future of upper extremity rehabilitation robotics: research and practice
Vu, PP,
Chestek, CA,
Nason, SR,
Kung, TA,
Kemp, SWP,
Cederna, PS,
Muscle Nerve, 2020 Jun; DOI:10.1002/mus.26860 An introduction to Machine/Deep Learning in Medical Physics: Tips & Pitfalls
Cui, S,
Tseng, H-H,
Pakela, J,
Ten Haken, RK,
El Naqa, I,
2020 Jun; DOI:10.1002/mp.14140 The role of machine and deep learning in modern medical physics
El Naqa, I,
Das, S,
Med Phys, 2020 Jun; DOI:10.1002/mp.14088 ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets
Li, H-D,
Xu, Y,
Zhu, X,
Liu, Q,
Omenn, GS,
Wang, J,
J Bioinform Comput Biol, 2020 Jun; DOI:10.1142/S0219720020400090 Thirty-Day Hospital Readmission and Surgical Complication Rates for Shunting in Normal Pressure Hydrocephalus: A Large National Database Analysis
Nadel, JL,
Wilkinson, DA,
Linzey, JR,
Maher, CO,
Kotagal, V,
Heth, JA,
Neurosurgery, 2020 Jun; DOI:10.1093/neuros/nyz299 Deep learning computer vision algorithm for detecting kidney stone composition
Black, KM,
Law, H,
Aldoukhi, A,
Deng, J,
Ghani, KR,
BJU Int, 2020 Jun; DOI:10.1111/bju.15035 Association analysis and meta-analysis of multiallelic variants for large-scale sequence data
Jiang, Y,
Chen, S,
Wang, X,
Liu, M,
Iacono, WG,
Hewitt, JK,
Hokanson, JE,
Krauter, K,
Laakso, M,
Li, KW,
Lutz, SM,
McGue, M,
Pandit, A,
Zajac, GJM,
Boehnke, M,
Abecasis, GR,
Vrieze, SI,
Jiang, B,
Zhan, X,
Liu, DJ,
Genes (Basel), 2020 May; DOI:10.3390/genes11050586 Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection
Takahashi, Y,
Ueki, M,
Yamada, M,
Tamiya, G,
Motoike, IN,
Saigusa, D,
Sakurai, M,
Nagami, F,
Ogishima, S,
Koshiba, S,
Kinoshita, K,
Yamamoto, M,
Tomita, H,
Transl Psychiatry, 2020 May; DOI:10.1038/s41398-020-0831-9 Microfluidic device for high-throughput affinity-based isolation of extracellular vesicles
Lo, T-W,
Zhu, Z,
Purcell, E,
Watza, D,
Wang, J,
Kang, Y-T,
Jolly, S,
Nagrath, D,
Nagrath, S,
Lab Chip, 2020 May; DOI:10.1039/c9lc01190k Detecting qualitative changes in biological systems
Mitrea, C,
Bollig-Fischer, A,
Voichiţa, C,
Donato, M,
Romero, R,
Drăghici, S,
Sci Rep, 2020 May; DOI:10.1038/s41598-020-62578-8 Network segregation varies with neural distinctiveness in sensorimotor cortex
Cassady, K,
Gagnon, H,
Freiburger, E,
Lalwani, P,
Simmonite, M,
Park, DC,
Peltier, SJ,
Taylor, SF,
Weissman, DH,
Seidler, RD,
Polk, TA,
Neuroimage, 2020 May; DOI:10.1016/j.neuroimage.2020.116663 Telemedicine and the COVID-19 pandemic, lessons for the future
Bashshur, R,
Doarn, CR,
Frenk, JM,
Kvedar, JC,
Woolliscroft, JO,
Telemed J E Health, 2020 May; DOI:10.1089/tmj.2020.29040.rb Self-organization in brain tumors: How cell morphology and cell density influence glioma pattern formation
Jamous, S,
Comba, A,
Lowenstein, PR,
Motsch, S,
PLoS Comput Biol, 2020 May; DOI:10.1371/journal.pcbi.1007611 Deriving Evidence from Secondary Data in Hand Surgery: Strengths, Limitations, and Future Directions
Squitieri, L,
Chung, KC,
Hand Clin, 2020 May; DOI:10.1016/j.hcl.2020.01.011 Machine Learning Comes of Age Local Impact versus National Generalizability
Burns, ML,
Kheterpal, S,
Anesthesiology, 2020 May; DOI:10.1097/ALN.0000000000003223 Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach
Mathis, MR,
Engoren, MC,
Joo, H,
Maile, MD,
Aaronson, KD,
Burns, ML,
Sjoding, MW,
Douville, NJ,
Janda, AM,
Hu, Y,
Najarian, K,
Kheterpal, S,
Anesth Analg, 2020 May; DOI:10.1213/ANE.0000000000004630 Considerations for Integration of Perioperative Electronic Health Records Across Institutions for Research and Quality Improvement: The Approach Taken by the Multicenter Perioperative Outcomes Group
Colquhoun, DA,
Shanks, AM,
Kapeles, SR,
Shah, N,
Saager, L,
Vaughn, MT,
Buehler, K,
Burns, ML,
Tremper, KK,
Freundlich, RE,
Aziz, M,
Kheterpal, S,
Mathis, MR,
Anesth Analg, 2020 May; DOI:10.1213/ANE.0000000000004489 Access to Hand Therapy Following Surgery in the United States: Barriers and Facilitators
Krishnan, J,
Chung, KC,
Hand Clin, 2020 May; DOI:10.1016/j.hcl.2020.01.006 Deep learning for comprehensive ECG annotation
Teplitzky, BA,
McRoberts, M,
Ghanbari, H,
Heart Rhythm, 2020 May; DOI:10.1016/j.hrthm.2020.02.015 Digital health: A revolution in care
Ghanbari, H,
Marrouche, NF,
Heart Rhythm, 2020 May; DOI:10.1016/j.hrthm.2020.03.012 Emergent design principles for prediction algorithms in health care
Wheelock, K,
Lee, JM,
Ghanbari, H,
Heart Rhythm, 2020 May; DOI:10.1016/j.hrthm.2020.02.014 Prewas: Data pre-processing for more informative bacterial gwas
Saund, K,
Lapp, Z,
Thiede, SN,
Pirani, A,
Snitkin, ES,
Microb Genom, 2020 May; DOI:10.1099/mgen.0.000368 