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Search Items: SUPPORT VECTOR MACHINE, . hits: 20


Kern, WJ; Orlob, S; Bohn, A; Toller, W; Wnent, J; Grasner, JT; Holler, M Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest.
IEEE Trans Biomed Eng. 2023; PP: Doi: 10.1109/TBME.2023.3242717
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Jurmeister, P; Leitheiser, M; Wolkenstein, P; Klauschen, F; Capper, D; Brcic, L DNA methylation-based machine learning classification distinguishes pleural mesothelioma from chronic pleuritis, pleural carcinosis, and pleomorphic lung carcinomas.
Lung Cancer. 2022; 170:105-113 Doi: 10.1016/j.lungcan.2022.06.008
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Pareto, D; Garcia-Vidal, A; Groppa, S; Gonzalez-Escamilla, G; Rocca, M; Filippi, M; Enzinger, C; Khalil, M; Llufriu, S; Tintoré, M; Sastre-Garriga, J; Rovira, À Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.
Neuroradiology. 2022; 64(7):1383-1390 Doi: 10.1007/s00234-021-02885-7
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Pokorny, FB; Schmitt, M; Egger, M; Bartl-Pokorny, KD; Zhang, D; Schuller, BW; Marschik, PB Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome.
Sci Rep. 2022; 12(1): 13345 Doi: 10.1038/s41598-022-17203-1 [OPEN ACCESS]
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Corrado, C; Williams, S; Roney, C; Plank, G; O'Neill, M; Niederer, S Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation.
Europace. 2021; 23(Supplement_1):i12-i20-i12-i20 Doi: 10.1093/europace/euaa386 [OPEN ACCESS]
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Hussain, S; Raza, Z; Giacomini, G; Goswami, N Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test.
BIOLOGY-BASEL. 2021; 10(10): Doi: 10.3390/biology10101029 [OPEN ACCESS]
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Maktabi, M; Köhler, H; Ivanova, M; Neumuth, T; Rayes, N; Seidemann, L; Sucher, R; Jansen-Winkeln, B; Gockel, I; Barberio, M; Chalopin, C Classification of hyperspectral endocrine tissue images using support vector machines.
Int J Med Robot. 2020; 16(5):1-10 Doi: 10.1002/rcs.2121
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Hassler, AP; Menasalvas, E; Garcia-Garcia, FJ; Rodriguez-Manas, L; Holzinger, A Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome
BMC MED INFORM DECIS. 2019; 19: 33 Doi: 10.1186/s12911-019-0747-6 [OPEN ACCESS]
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Oleynik, M; Kugic, A; Kasáč, Z; Kreuzthaler, M Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.
J Am Med Inform Assoc. 2019; 26(11):1247-1254 Doi: 10.1093/jamia/ocz149 [OPEN ACCESS]
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Wottschel, V; Chard, DT; Enzinger, C; Filippi, M; Frederiksen, JL; Gasperini, C; Giorgio, A; Rocca, MA; Rovira, A; De Stefano, N; Tintoré, M; Alexander, DC; Barkhof, F; Ciccarelli, O; MAGNIMS study group and the EuroPOND consortium SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis.
Neuroimage Clin. 2019; 24: 102011-102011. Doi: 10.1016/j.nicl.2019.102011 [OPEN ACCESS]
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Oleynik, M; Patrão, DFC; Finger, M Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.
Stud Health Technol Inform. 2017; 235(11): 256-260.



Christian, J; Kröll, J; Strutzenberger, G; Alexander, N; Ofner, M; Schwameder, H Computer aided analysis of gait patterns in patients with acute anterior cruciate ligament injury.
Clin Biomech (Bristol, Avon). 2016; 33(5):55-60 Doi: 10.1016/j.clinbiomech.2016.02.008
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Reyes-Coronel, C; Waser, M; Garn, H; Deistler, M; Dal-Bianco, P; Benke, T; Ransmayr, G; Grossegger, D; Schmidt, R Predicting rapid cognitive decline in Alzheimer's disease patients using quantitative EEG markers and neuropsychological test scores.
Conf Proc IEEE Eng Med Biol Soc. 2016; 2016(2):6078-6081 Doi: 10.1109/EMBC.2016.7592115
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von Zur Mühlen, C; Koeck, T; Schiffer, E; Sackmann, C; Zürbig, P; Hilgendorf, I; Reinöhl, J; Rivera, J; Zirlik, A; Hehrlein, C; Mischak, H; Bode, C; Peter, K Urine proteome analysis as a discovery tool in patients with deep vein thrombosis and pulmonary embolism.
PROTEOM CLIN APPL. 2016; 10(5): 574-584. Doi: 10.1002/prca.201500105
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Engelhardt, KR; Gertz, ME; Keles, S; Schäffer, AA; Sigmund, EC; Glocker, C; Saghafi, S; Pourpak, Z; Ceja, R; Sassi, A; Graham, LE; Massaad, MJ; Mellouli, F; Ben-Mustapha, I; Khemiri, M; Kilic, SS; Etzioni, A; Freeman, AF; Thiel, J; Schulze, I; Al-Herz, W; Metin, A; Sanal, Ö; Tezcan, I; Yeganeh, M; Niehues, T; Dueckers, G; Weinspach, S; Patiroglu, T; Unal, E; Dasouki, M; Yilmaz, M; Genel, F; Aytekin, C; Kutukculer, N; Somer, A; Kilic, M; Reisli, I; Camcioglu, Y; Gennery, AR; Cant, AJ; Jones, A; Gaspar, BH; Arkwright, PD; Pietrogrande, MC; Baz, Z; Al-Tamemi, S; Lougaris, V; Lefranc, G; Megarbane, A; Boutros, J; Galal, N; Bejaoui, M; Barbouche, MR; Geha, RS; Chatila, TA; Grimbacher, B The extended clinical phenotype of 64 patients with dedicator of cytokinesis 8 deficiency.
J ALLERGY CLIN IMMUN. 2015; 136(2): 402-12. Doi: 10.1016/j.jaci.2014.12.1945 [OPEN ACCESS]
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Kreuzthaler, M; Schulz, S Detection of sentence boundaries and abbreviations in clinical narratives.
BMC Med Inform Decis Mak. 2015; 15 Suppl 2(1):S4-S4 Doi: 10.1186/1472-6947-15-S2-S4 [OPEN ACCESS]
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Wiltgen, M; Tilz, GP Molecular diagnosis and prognosis with DNA microarrays.
Hematology. 2011; 16(3):166-176 Doi: 10.1179/102453311X12953015767257
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Gilmore, S; Hofmann-Wellenhof, R; Soyer, HP A support vector machine for decision support in melanoma recognition.
Exp Dermatol. 2010; 19(9):830-835 Doi: 10.1111/j.1600-0625.2010.01112.x
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Wiltgen, M; Tilz, GP DNA microarray analysis: principles and clinical impact.
HEMATOLOGY. 2007; 12(4): 271-287. Doi: 10.1080/10245330701283967
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Dal Moro, F; Abate, A; Lanckriet, GR; Arandjelovic, G; Gasparella, P; Bassi, P; Mancini, M; Pagano, F A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.
Kidney Int. 2006; 69(1): 157-160. Doi: 10.1038/
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