Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Publikationen
Suchbegriffe: MACHINE LEARNING, . Treffer: 109
2023
Duering, M; Biessels, GJ; Brodtmann, A; Chen, C; Cordonnier, C; de, Leeuw, FE; Debette, S; Frayne, R; Jouvent, E; Rost, NS; Ter, Telgte, A; Al-Shahi, Salman, R; Backes, WH; Bae, HJ; Brown, R; Chabriat, H; De, Luca, A; deCarli, C; Dewenter, A; Doubal, FN; Ewers, M; Field, TS; Ganesh, A; Greenberg, S; Helmer, KG; Hilal, S; Jochems, ACC; Jokinen, H; Kuijf, H; Lam, BYK; Lebenberg, J; MacIntosh, BJ; Maillard, P; Mok, VCT; Pantoni, L; Rudilosso, S; Satizabal, CL; Schirmer, MD; Schmidt, R; Smith, C; Staals, J; Thrippleton, MJ; van, Veluw, SJ; Vemuri, P; Wang, Y; Werring, D; Zedde, M; Akinyemi, RO; Del, Brutto, OH; Markus, HS; Zhu, YC; Smith, EE; Dichgans, M; Wardlaw, JM
Neuroimaging standards for research into small vessel disease-advances since 2013.
Lancet Neurol. 2023;
Doi: 10.1016/S1474-4422(23)00131-X
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Frohlich, E; Loizou, GD
Editorial: 3Rs-Strategies for reduction and refinement of animal studies
FRONT PHARMACOL. 2023; 14: 1200965
Doi: 10.3389/fphar.2023.1200965
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Holzinger, A; Keiblinger, K; Holub, P; Zatloukal, K; Müller, H
AI for life: Trends in artificial intelligence for biotechnology.
N Biotechnol. 2023; 74:16-24
Doi: 10.1016/j.nbt.2023.02.001
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Jauk, S; Kramer, D; Veeranki, SPK; Siml-Fraissler, A; Lenz-Waldbauer, A; Tax, E; Leodolter, W; Gugatschka, M
Evaluation of a Machine Learning-Based Dysphagia Prediction Tool in Clinical Routine: A Prospective Observational Cohort Study
DYSPHAGIA. 2023;
Doi: 10.1007/s00455-022-10548-9
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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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L'Imperio, V; Wulczyn, E; Plass, M; Müller, H; Tamini, N; Gianotti, L; Zucchini, N; Reihs, R; Corrado, GS; Webster, DR; Peng, LH; Chen, PC; Lavitrano, M; Liu, Y; Steiner, DF; Zatloukal, K; Pagni, F
Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification.
JAMA Netw Open. 2023; 6(3):e2254891
Doi: 10.1001/jamanetworkopen.2022.54891
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Moser, T; Kühberger, S; Lazzeri, I; Vlachos, G; Heitzer, E
Bridging biological cfDNA features and machine learning approaches.
Trends Genet. 2023; 39(4):285-307
Doi: 10.1016/j.tig.2023.01.004
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Pfeifer, B; Bloice, MD; Schimek, MG
Parea: Multi-view ensemble clustering for cancer subtype discovery.
J Biomed Inform. 2023; 104406
Doi: 10.1016/j.jbi.2023.104406
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Polz, M; Bergmoser, K; Horn, M; Schörghuber, M; Lozanović, J; Rienmüller, T; Baumgartner, C
A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients.
Front Physiol. 2023; 14:1101966
Doi: 10.3389/fphys.2023.1101966
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Whitaker, J; Baum, TE; Qian, P; Prassl, AJ; Plank, G; Blankstein, R; Cochet, H; Sauer, WH; Bishop, MJ; Tedrow, U
Frequency Domain Analysis of Endocardial Electrograms for Detection of Nontransmural Myocardial Fibrosis in Nonischemic Cardiomyopathy.
JACC Clin Electrophysiol. 2023;
Doi: 10.1016/j.jacep.2022.11.019
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2022
Bonkhoff, AK; Hong, S; Bretzner, M; Schirmer, MD; Regenhardt, RW; Arsava, EM; Donahue, K; Nardin, M; Dalca, A; Giese, AK; Etherton, MR; Hancock, BL; Mocking, SJT; McIntosh, E; Attia, J; Benavente, O; Cole, JW; Donatti, A; Griessenauer, C; Heitsch, L; Holmegaard, L; Jood, K; Jimenez-Conde, J; Kittner, S; Lemmens, R; Levi, C; McDonough, CW; Meschia, J; Phuah, CL; Rolfs, A; Ropele, S; Rosand, J; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Soederholm, M; Sousa, A; Stanne, TM; Strbian, D; Tatlisumak, T; Thijs, V; Vagal, A; Wasselius, J; Woo, D; Zand, R; McArdle, P; Worrall, BB; Jern, C; Lindgren, AG; Maguire, J; Golland, P; Bzdok, D; Wu, O; Rost, NS
Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome.
Neurology. 2022; 99(13): e1364-e1379.
Doi: 10.1212/WNL.0000000000200926
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Brunt, EM; Clouston, AD; Goodman, Z; Guy, C; Kleiner, DE; Lackner, C; Tiniakos, DG; Wee, A; Yeh, M; Leow, WQ; Chng, E; Ren, YY; Bee, GGB; Powell, EE; Rinella, M; Sanyal, AJ; Neuschwander-Tetri, B; Younossi, Z; Charlton, M; Ratziu, V; Harrison, SA; Tai, DA; Anstee, QM
Complexity of ballooned hepatocyte feature recognition: Defining a training atlas for artificial intelligence-based imaging in NAFLD
J HEPATOL. 2022; 76(5): 1030-1041.
Doi: 10.1016/j.jhep.2022.01.011
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Egger, J; Gsaxner, C; Pepe, A; Pomykala, KL; Jonske, F; Kurz, M; Li, JN; Kleesiek, J
Medical deep learning-A systematic meta-review
COMPUT METH PROG BIO. 2022; 221: 106874
Doi: 10.1016/j.cmpb.2022.106874
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Finzel, B; Saranti, A; Angerschmid, A; Tafler, D; Pfeifer, B; Holzinger, A
Generating Explanations for Conceptual Validation of Graph Neural Networks
KUNSTL INTELL. 2022;
Doi: 10.1007/s13218-022-00781-7
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Geisler, A; Schmidt, A; Branzan, D
[Digital Patient Data, Artificial Intelligence and Machine Learning in the New Era of Endovascular Aortic Therapies].
Zentralbl Chir. 2022; 147(5): 432-438.
Doi: 10.1055/a-1938-8227
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Güllmar, D; Jacobsen, N; Deistung, A; Timmann, D; Ropele, S; Reichenbach, JR
Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis.
Z Med Phys. 2022; 32(3): 346-360.
Doi: 10.1016/j.zemedi.2021.11.004
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Gunzer, F; Jantscher, M; Hassler, EM; Kau, T; Reishofer, G
Reproducibility of artificial intelligence models in computed tomography of the head: a quantitative analysis.
Insights Imaging. 2022; 13(1):173
Doi: 10.1186/s13244-022-01311-7
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Haimel, M; Pazmandi, J; Heredia, RJ; Dmytrus, J; Bal, SK; Zoghi, S; van, Daele, P; Briggs, TA; Wouters, C; Bader-Meunier, B; Aeschlimann, FA; Caorsi, R; Eleftheriou, D; Hoppenreijs, E; Salzer, E; Bakhtiar, S; Derfalvi, B; Saettini, F; Kusters, MAA; Elfeky, R; Trück, J; Rivière, JG; van, der, Burg, M; Gattorno, M; Seidel, MG; Burns, S; Warnatz, K; Hauck, F; Brogan, P; Gilmour, KC; Schuetz, C; Simon, A; Bock, C; Hambleton, S; de, Vries, E; Robinson, PN; van, Gijn, M; Boztug, K
Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity.
J Allergy Clin Immunol. 2022; 149(1):369-378
Doi: 10.1016/j.jaci.2021.04.033
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Holzinger, A; Dehmer, M; Emmert-Streib, F; Cucchiara, R; Augenstein, I; Del Ser, J; Samek, W; Jurisica, I; Diaz-Rodriguez, N
Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
INFORM FUSION. 2022; 79: 263-278.
Doi: 10.1016/j.inffus.2021.10.007
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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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Kern, WJ; Orlob, S; Alpers, B; Schörghuber, M; Bohn, A; Holler, M; Gräsner, JT; Wnent, J
A sliding-window based algorithm to determine the presence of chest compressions from acceleration data.
Data Brief. 2022; 41:107973
Doi: 10.1016/j.dib.2022.107973
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Kirnbauer, B; Hadzic, A; Jakse, N; Bischof, H; Stern, D
Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks.
J Endod. 2022; 48(11):1434-1440
Doi: 10.1016/j.joen.2022.07.013
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Müller, H; Holzinger, A; Plass, M; Brcic, L; Stumptner, C; Zatloukal, K
Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European In Vitro Diagnostic Regulation.
N Biotechnol. 2022; 70:67-72
Doi: 10.1016/j.nbt.2022.05.002
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Orlob, S; Kern, WJ; Alpers, B; Schörghuber, M; Bohn, A; Holler, M; Gräsner, JT; Wnent, J
Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data - Tested in Zoll X Series.
Resuscitation. 2022; 172:162-169
Doi: 10.1016/j.resuscitation.2021.12.028
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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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Pfeifer, B; Baniecki, H; Saranti, A; Biecek, P; Holzinger, A
Multi-omics disease module detection with an explainable Greedy Decision Forest.
Sci Rep. 2022; 12(1): 16857
Doi: 10.1038/s41598-022-21417-8
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Pfeifer, B; Holzinger, A; Schimek, MG
Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.
Stud Health Technol Inform. 2022; 294: 137-138.
Doi: 10.3233/SHTI220418
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Ren, Z; Chang, Y; Bartl-Pokorny, KD; Pokorny, FB; Schuller, BW
The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection.
J Voice. 2022;
Doi: 10.1016/j.jvoice.2022.06.011
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Saranti, A; Hudec, M; Mináriková, E; Takác̆, Z; Großschedl, U; Koch, C; Pfeifer, B; Angerschmid, A; Holzinger, A
Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning.
Machine Learning and Knowledge Extraction. 2022; 4(4): 924-953.
Doi: 10.3390/make4040047
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Schrempf, M; Polat, Erdeniz, S; Kramer, D; Jauk, S; Veeranki, SPK; Ribitsch, W; Leodolter, W; Rainer, PP
Development of an Architecture to Implement Machine Learning Based Risk Prediction in Clinical Routine: A Service-Oriented Approach.
Stud Health Technol Inform. 2022; 293: 262-269.
Doi: 10.3233/SHTI220379
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Schulte, EC; Kondofersky, I; Budde, M; Papiol, S; Senner, F; Schaupp, SK; Reich-Erkelenz, D; Klöhn-Saghatolislam, F; Kalman, JL; Gade, K; Hake, M; Comes, AL; Anderson-Schmidt, H; Adorjan, K; Juckel, G; Schmauß, M; Zimmermann, J; Reimer, J; Wiltfang, J; Reininghaus, EZ; Anghelescu, IG; Konrad, C; Figge, C; von, Hagen, M; Jäger, M; Dietrich, DE; Spitzer, C; Witt, SH; Forstner, AJ; Rietschel, M; Nöthen, MM; Falkai, P; Heilbronner, U; Mueller, NS; Schulze, TG
A novel longitudinal clustering approach to psychopathology across diagnostic entities in the hospital-based PsyCourse study.
Schizophr Res. 2022; 244: 29-38.
Doi: 10.1016/j.schres.2022.05.001
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Schulte-Rüther, M; Kulvicius, T; Stroth, S; Wolff, N; Roessner, V; Marschik, PB; Kamp-Becker, I; Poustka, L
Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses.
J Child Psychol Psychiatry. 2022;
Doi: 10.1111/jcpp.13650
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van, Loon, W; de, Vos, F; Fokkema, M; Szabo, B; Koini, M; Schmidt, R; de, Rooij, M
Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.
FRONT NEUROSCI-SWITZ. 2022; 16: 830630
Doi: 10.3389/fnins.2022.830630
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2021
Bretzner, M; Bonkhoff, AK; Schirmer, MD; Hong, S; Dalca, AV; Donahue, KL; Giese, AK; Etherton, MR; Rist, PM; Nardin, M; Marinescu, R; Wang, C; Regenhardt, RW; Leclerc, X; Lopes, R; Benavente, OR; Cole, JW; Donatti, A; Griessenauer, CJ; Heitsch, L; Holmegaard, L; Jood, K; Jimenez-Conde, J; Kittner, SJ; Lemmens, R; Levi, CR; McArdle, PF; McDonough, CW; Meschia, JF; Phuah, CL; Rolfs, A; Ropele, S; Rosand, J; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Sousa, A; Stanne, TM; Strbian, D; Tatlisumak, T; Thijs, V; Vagal, A; Wasselius, J; Woo, D; Wu, O; Zand, R; Worrall, BB; Maguire, JM; Lindgren, A; Jern, C; Golland, P; Kuchcinski, G; Rost, NS
MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes.
Front Neurosci. 2021; 15: 691244
Doi: 10.3389/fnins.2021.691244
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Egger, J; Pepe, A; Gsaxner, C; Jin, Y; Li, JN; Kern, R
Deep learning-a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact
PEERJ COMPUT SCI. 2021; 7: e773
Doi: 10.7717/peerj-cs.773
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Feng, S; Azzollini, D; Kim, JS; Jin, CK; Gordon, SP; Yeoh, J; Kim, E; Han, M; Lee, A; Patel, A; Wu, J; Urschler, M; Fong, A; Simmers, C; Tarr, GP; Barnard, S; Wilson, B
Curation of the CANDID-PTX Dataset with Free-Text Reports.
Radiol Artif Intell. 2021; 3(6): e210136
Doi: 10.1148/ryai.2021210136
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Giovannini, I; Bosch, P; Dejaco, C; De, Marco, G; McGonagle, D; Quartuccio, L; De, Vita, S; Errichetti, E; Zabotti, A
The Digital Way to Intercept Psoriatic Arthritis.
FRONT MED-LAUSANNE. 2021; 8: 792972
Doi: 10.3389/fmed.2021.792972
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Hržić, F; Tschauner, S; Sorantin, E; Štajduhar, I
XAOM: A method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis.
Comput Biol Med. 2021; 132:104300
Doi: 10.1016/j.compbiomed.2021.104300
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Hudec, M; Minarikova, E; Mesiar, R; Saranti, A; Holzinger, A
Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions
KNOWL-BASED SYST. 2021; 220:
Doi: 10.1016/j.knosys.2021.106916
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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
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Hussain, S; Raza, Z; Kumar, TVV; Goswami, N
Diagnosing Neurally Mediated Syncope Using Classification Techniques.
J Clin Med. 2021; 10(21):
Doi: 10.3390/jcm10215016
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Igrec, J; Fuchsjäger, MH
Imaging of Bone Sarcomas and Soft-Tissue Sarcomas.
Rofo. 2021; 193(10):1171-1182
Doi: 10.1055/a-1401-0215
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Jauk, S; Kramer, D; Avian, A; Berghold, A; Leodolter, W; Schulz, S
Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.
J MED SYST. 2021; 45(4): 48-48.
Doi: 10.1007/s10916-021-01727-6
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Lee, AWC; Razeghi, O; Solis-Lemus, JA; Strocchi, M; Sidhu, B; Gould, J; Behar, JM; Elliott, M; Mehta, V; Plank, G; Rinaldi, CA; Niederer, SA
Non-invasive simulated electrical and measured mechanical indices predict response to cardiac resynchronization therapy.
Comput Biol Med. 2021; 138: 104872
Doi: 10.1016/j.compbiomed.2021.104872
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Li, J; Krall, M; Trummer, F; Memon, AR; Pepe, A; Gsaxner, C; Jin, Y; Chen, X; Deutschmann, H; Zefferer, U; Schäfer, U; Campe, GV; Egger, J
MUG500+: Database of 500 high-resolution healthy human skulls and 29 craniotomy skulls and implants.
Data Brief. 2021; 39: 107524
Doi: 10.1016/j.dib.2021.107524
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Majnarić, LT; Babič, F; O'Sullivan, S; Holzinger, A
AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity.
J CLIN MED. 2021; 10(4): 766
Doi: 10.3390/jcm10040766
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Martinez, RM; Muller, H; Negru, S; Ormenisan, A; Muhr, LSA; Zhang, XY; Moller, FT; Clements, MS; Kozlakidis, Z; Pimenoff, VN; Wilkowski, B; Boeckhout, M; Ohman, H; Chong, S; Holzinger, A; Lehtinen, M; van Veen, EB; Bala, P; Widschwendter, M; Dowling, J; Tornroos, J; Snyder, MP; Dillner, J
Human exposome assessment platform
ENVIRON EPIDEMIOL. 2021; 5(6): e182
Doi: 10.1097/EE9.0000000000000182
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Memon, AR; Li, JN; Egger, J; Chen, XJ
A review on patient-specific facial and cranial implant design using Artificial Intelligence (AI) techniques
EXPERT REV MED DEVIC. 2021;
Doi: 10.1080/17434440.2021.1969914
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Mrazek, C; Haschke-Becher, E; Felder, TK; Keppel, MH; Oberkofler, H; Cadamuro, J
Laboratory Demand Management Strategies-An Overview.
Diagnostics (Basel). 2021; 11(7):
Doi: 10.3390/diagnostics11071141
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Ranjan, R; Partl, R; Erhart, R; Kurup, N; Schnidar, H
The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.
Comput Biol Med. 2021; 139:104952
Doi: 10.1016/j.compbiomed.2021.104952
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