 Connection
Todd Hankinson to Machine Learning
This is a "connection" page, showing publications Todd Hankinson has written about Machine Learning.
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Connection Strength |
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0.867 |
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Prince EW, Apps JR, Jeang J, Chee K, Medlin S, Jackson EM, Dudley R, Limbrick D, Naftel R, Johnston J, Feldstein N, Prolo LM, Ginn K, Niazi T, Smith A, Kilburn L, Chern J, Leonard J, Lam S, Hersh DS, Gonzalez-Meljem JM, Amani V, Donson AM, Mitra SS, Bandopadhayay P, Martinez-Barbera JP, Hankinson TC. Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis. Neuro Oncol. 2024 06 03; 26(6):1109-1123.
Score: 0.631
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Malhotra AK, Kulkarni AV, Verhey LH, Reeder RW, Riva-Cambrin J, Jensen H, Pollack IF, McDowell M, Rocque BG, Tamber MS, McDonald PJ, Krieger MD, Pindrik JA, Isaacs AM, Hauptman JS, Browd SR, Whitehead WE, Jackson EM, Wellons JC, Hankinson TC, Chu J, Limbrick DD, Strahle JM, Kestle JRW. Does machine learning improve prediction accuracy of the Endoscopic Third Ventriculostomy Success Score? A contemporary Hydrocephalus Clinical Research Network cohort study. Childs Nerv Syst. 2024 Dec 10; 41(1):42.
Score: 0.164
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Prince EW, Hankinson TC, G?rg C. A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support. Pac Symp Biocomput. 2025; 30:40-53.
Score: 0.041
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Hale AT, Riva-Cambrin J, Wellons JC, Jackson EM, Kestle JRW, Naftel RP, Hankinson TC, Shannon CN. Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network. Childs Nerv Syst. 2021 05; 37(5):1485-1494.
Score: 0.031
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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