Výsledky vyhledávání - (automatic OR automation) (machine OR machinery) learning algorithm based on genetic algorithms

Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Feb; Vol. 31 (10), pp. 14503-14536. Date of Electronic Publication: 2024 Feb 02.

Způsob vydávání: Journal Article; Review

Informace o časopise: Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE

Výrazy ze slovníku MeSH: Deep Learning*, Ecology ; Water Pollution/prevention & control ; Algorithms ; Water Resources

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12

Development and Validation of a Novel Myocardial Infarction Prediction Model Based on ECG-AI

Přispěvatelé: Liu yong, Professor

Zdroj: Development and Validation of a Novel Myocardial Infarction Prediction Model Based on ECG-AI: A Multicenter Retrospective Cohort Study
Sadasivuni S, Saha M, Bhatia N, Banerjee I, Sanyal A. Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset. Sci Rep. 2022 Apr 5;12(1):5711. doi: 10.1038/s41598-022-09712-w.
Jawade P, Khillare KM, Mangudkar S, Palange A, Dhadwad J, Deshmukh M. A Comparative Study of Ischemia-Modified Albumin: A Promising Biomarker for Early Detection of Acute Coronary Syndrome (ACS). Cureus. 2023 Aug 30;15(8):e44357. doi: 10.7759/cureus.44357. eCollection 2023 Aug.
Swenne CA, Ter Haar CC. Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients. J Electrocardiol. 2024 Jan-Feb;82:34-41. doi: 10.1016/j.jelectrocard.2023.10.009. Epub 2023 Nov 7.
Akbilgic O, Butler L, Karabayir I, Chang PP, Kitzman DW, Alonso A, Chen LY, Soliman EZ. ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure. Eur Heart J Digit Health. 2021 Oct 9;2(4):626-634. doi: 10.1093/ehjdh/ztab080. eCollection 2021 Dec.
Aufiero S, Bleijendaal H, Robyns T, Vandenberk B, Krijger C, Bezzina C, Zwinderman AH, Wilde AAM, Pinto YM. A deep learning approach identifies new ECG features in congenital long QT syndrome. BMC Med. 2022 May 3;20(1):162. doi: 10.1186/s12916-022-02350-z.
Serhal H, Abdallah N, Marion JM, Chauvet P, Oueidat M, Humeau-Heurtier A. Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG. Comput Biol Med. 2022 Mar;142:105168. doi: 10.1016/j.compbiomed.2021.105168. Epub 2022 Jan 1.
Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis. Proc Natl Acad Sci U S A. 2021 Jun 15;118(24):e2020620118. doi: 10.1073/pnas.2020620118.
Liu YL, Lin CS, Cheng CC, Lin C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. J Pers Med. 2022 Jul 15;12(7):1150. doi: 10.3390/jpm12071150.
Pignolo RJ. AI-ECG and the Prediction of Accelerated Aging. Mayo Clin Proc. 2023 Apr;98(4):502-503. doi: 10.1016/j.mayocp.2023.02.016. No abstract available.
Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. Circ Genom Precis Med. 2023 Aug;16(4):340-349. doi: 10.1161/CIRCGEN.122.003808. Epub 2023 Jun 6.
Attia ZI, Friedman PA. Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning? Eur Heart J. 2023 Feb 21;44(8):693-695. doi: 10.1093/eurheartj/ehac733. No abstract available.
Martinez-Selles M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023 Apr 17;10(4):175. doi: 10.3390/jcdd10040175.
Butler L, Ivanov A, Celik T, Karabayir I, Chinthala L, Tootooni MS, Jaeger BC, Patterson LT, Doerr AJ, McManus DD, Davis RL, Herrington D, Akbilgic O. Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study. J Cardiovasc Dev Dis. 2024 Dec 8;11(12):395. doi: 10.3390/jcdd11120395.
Sekhri N, Feder GS, Junghans C, Hemingway H, Timmis AD. How effective are rapid access chest pain clinics? Prognosis of incident angina and non-cardiac chest pain in 8762 consecutive patients. Heart. 2007 Apr;93(4):458-63. doi: 10.1136/hrt.2006.090894. Epub 2006 Jun 21.
Stubbs P, Collinson P, Moseley D, Greenwood T, Noble M. Prospective study of the role of cardiac troponin T in patients admitted with unstable angina. BMJ. 1996 Aug 3;313(7052):262-4. doi: 10.1136/bmj.313.7052.262.
Farkouh ME, Smars PA, Reeder GS, Zinsmeister AR, Evans RW, Meloy TD, Kopecky SL, Allen M, Allison TG, Gibbons RJ, Gabriel SE. A clinical trial of a chest-pain observation unit for patients with unstable angina. Chest Pain Evaluation in the Emergency Room (CHEER) Investigators. N Engl J Med. 1998 Dec 24;339(26):1882-8. doi: 10.1056/NEJM199812243392603.
Fox KA, Goodman SG, Klein W, Brieger D, Steg PG, Dabbous O, Avezum A. Management of acute coronary syndromes. Variations in practice and outcome; findings from the Global Registry of Acute Coronary Events (GRACE). Eur Heart J. 2002 Aug;23(15):1177-89. doi: 10.1053/euhj.2001.3081.
Grech ED, Ramsdale DR. Acute coronary syndrome: unstable angina and non-ST segment elevation myocardial infarction. BMJ. 2003 Jun 7;326(7401):1259-61. doi: 10.1136/bmj.326.7401.1259. No abstract available.
Pollack CV Jr, Sites FD, Shofer FS, Sease KL, Hollander JE. Application of the TIMI risk score for unstable angina and non-ST elevation acute coronary syndrome to an unselected emergency department chest pain population. Acad Emerg Med. 2006 Jan;13(1):13-8. doi: 10.1197/j.aem.2005.06.031. Epub 2005 Dec 19.
Sinha MK, Roy D, Gaze DC, Collinson PO, Kaski JC. Role of "Ischemia modified albumin", a new biochemical marker of myocardial ischaemia, in the early diagnosis of acute coronary syndromes. Emerg Med J. 2004 Jan;21(1):29-34. doi: 10.1136/emj.2003.006007.
de Beer FC, Hind CR, Fox KM, Allan RM, Maseri A, Pepys MB. Measurement of serum C-reactive protein concentration in myocardial ischaemia and infarction. Br Heart J. 1982 Mar;47(3):239-43. doi: 10.1136/hrt.47.3.239.
Katus HA, Remppis A, Neumann FJ, Scheffold T, Diederich KW, Vinar G, Noe A, Matern G, Kuebler W. Diagnostic efficiency of troponin T measurements in acute myocardial infarction. Circulation. 1991 Mar;83(3):902-12. doi: 10.1161/01.cir.83.3.902.
Yagi R, Goto S, Himeno Y, Katsumata Y, Hashimoto M, MacRae CA, Deo RC. Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms. Nat Commun. 2024 Mar 21;15(1):2536. doi: 10.1038/s41467-024-45733-x.
Shen CP, Muse ED. Towards an artificial intelligence-augmented, ECG-enabled physical exam. Lancet Digit Health. 2022 Feb;4(2):e78-e79. doi: 10.1016/S2589-7500(21)00281-8. Epub 2022 Jan 5. No abstract available.
Lincoff AM, Brown-Frandsen K, Colhoun HM, Deanfield J, Emerson SS, Esbjerg S, Hardt-Lindberg S, Hovingh GK, Kahn SE, Kushner RF, Lingvay I, Oral TK, Michelsen MM, Plutzky J, Tornoe CW, Ryan DH; SELECT Trial Investigators. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023 Dec 14;389(24):2221-2232. doi: 10.1056/NEJMoa2307563. Epub 2023 Nov 11.
Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. Eur Heart J. 2025 Mar 13;46(11):1044-1053. doi: 10.1093/eurheartj/ehae914.
Biscaglia S, Guiducci V, Escaned J, Moreno R, Lanzilotti V, Santarelli A, Cerrato E, Sacchetta G, Jurado-Roman A, Menozzi A, Amat Santos I, Diez Gil JL, Ruozzi M, Barbierato M, Fileti L, Picchi A, Lodolini V, Biondi-Zoccai G, Maietti E, Pavasini R, Cimaglia P, Tumscitz C, Erriquez A, Penzo C, Colaiori I, Pignatelli G, Casella G, Iannopollo G, Menozzi M, Varbella F, Caretta G, Dudek D, Barbato E, Tebaldi M, Campo G; FIRE Trial Investigators. Complete or Culprit-Only PCI in Older Patients with Myocardial Infarction. N Engl J Med. 2023 Sep 7;389(10):889-898. doi: 10.1056/NEJMoa2300468. Epub 2023 Aug 26.
Kromhout D, Giltay EJ, Geleijnse JM; Alpha Omega Trial Group. n-3 fatty acids and cardiovascular events after myocardial infarction. N Engl J Med. 2010 Nov 18;363(21):2015-26. doi: 10.1056/NEJMoa1003603. Epub 2010 Aug 28.
Dupulthys S, Dujardin K, Anne W, Pollet P, Vanhaverbeke M, McAuliffe D, Lammertyn PJ, Berteloot L, Mertens N, De Jaeger P. Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm. Europace. 2024 Feb 1;26(2):euad354. doi: 10.1093/europace/euad354.
Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel). 2024 Aug 23;14(17):1839. doi: 10.3390/diagnostics14171839.
Chen HY, Lin CS, Fang WH, Lee CC, Ho CL, Wang CH, Lin C. Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction. Front Med (Lausanne). 2022 Apr 11;9:870523. doi: 10.3389/fmed.2022.870523. eCollection 2022.
Lee HS, Kang S, Jo YY, Son JM, Lee MS, Kwon JM, Kim KH. AI-Enabled Smartwatch ECG: A Feasibility Study for Early Prediction and Prevention of Heart Failure Rehospitalization. JACC Basic Transl Sci. 2025 Mar;10(3):250-252. doi: 10.1016/j.jacbts.2025.01.005. Epub 2025 Feb 11. No abstract available.
Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 8.

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13

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Autoři: Zuev VA Salmagambetova EG Djakov SN a další

Zdroj: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Způsob vydávání: Journal Article; Review

Informace o časopise: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

Výrazy ze slovníku MeSH: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

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14

Application of some artificial intelligence optimization methods to determine the freshness of eggs.

Autoři: ŞAHİN, Hasan Alp ÖNDER, Hasan

Zdroj: Turkish Journal of Veterinary & Animal Sciences. 2024, Vol. 48 Issue 3, p156-164. 9p.

Témata: Artificial intelligence, Digital image processing, Artificial neural networks, Object recognition (Computer vision), Particle swarm optimization, Image compression

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15

Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms.

Autoři: Guo, Yuchen Quan, Lihong Song, Lili a další

Zdroj: Journal of Hydrology. Feb2022, Vol. 605, pN.PAG-N.PAG. 1p.

Témata: *MACHINE learning, *BACK propagation, *DEEP learning, *GENETIC algorithms, *PRECIPITATION forecasting

Geografický termín: TIANJIN (China)

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16

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With Machine Learning (PREVIEW)

Autoři: Fondazione Don Carlo Gnocchi Onlus University of Florence Scuola Superiore Sant'Anna di Pisa a další

Přispěvatelé: Fondazione Don Carlo Gnocchi Onlus University of Florence Scuola Superiore Sant'Anna di Pisa a další

Zdroj: PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With Machine Learning
Giacomucci G, Mazzeo S, Bagnoli S, Casini M, Padiglioni S, Polito C, Berti V, Balestrini J, Ferrari C, Lombardi G, Ingannato A, Sorbi S, Nacmias B, Bessi V. Matching Clinical Diagnosis and Amyloid Biomarkers in Alzheimer's Disease and Frontotemporal Dementia. J Pers Med. 2021 Jan 14;11(1):47. doi: 10.3390/jpm11010047.
Mazzeo S, Bessi V, Bagnoli S, Giacomucci G, Balestrini J, Padiglioni S, Tomaiuolo G, Ingannato A, Ferrari C, Bracco L, Sorbi S, Nacmias B. Dual Effect of PER2 C111G Polymorphism on Cognitive Functions across Progression from Subjective Cognitive Decline to Mild Cognitive Impairment. Diagnostics (Basel). 2021 Apr 18;11(4):718. doi: 10.3390/diagnostics11040718.
Bessi V, Balestrini J, Bagnoli S, Mazzeo S, Giacomucci G, Padiglioni S, Piaceri I, Carraro M, Ferrari C, Bracco L, Sorbi S, Nacmias B. Influence of ApoE Genotype and Clock T3111C Interaction with Cardiovascular Risk Factors on the Progression to Alzheimer's Disease in Subjective Cognitive Decline and Mild Cognitive Impairment Patients. J Pers Med. 2020 May 29;10(2):45. doi: 10.3390/jpm10020045.
Mazzeo S, Padiglioni S, Bagnoli S, Bracco L, Nacmias B, Sorbi S, Bessi V. The dual role of cognitive reserve in subjective cognitive decline and mild cognitive impairment: a 7-year follow-up study. J Neurol. 2019 Feb;266(2):487-497. doi: 10.1007/s00415-018-9164-5. Epub 2019 Jan 2.
Giacomucci G, Mazzeo S, Padiglioni S, Bagnoli S, Belloni L, Ferrari C, Bracco L, Nacmias B, Sorbi S, Bessi V. Gender differences in cognitive reserve: implication for subjective cognitive decline in women. Neurol Sci. 2022 Apr;43(4):2499-2508. doi: 10.1007/s10072-021-05644-x. Epub 2021 Oct 8.
Mazzeo S, Bessi V, Padiglioni S, Bagnoli S, Bracco L, Sorbi S, Nacmias B. KIBRA T allele influences memory performance and progression of cognitive decline: a 7-year follow-up study in subjective cognitive decline and mild cognitive impairment. Neurol Sci. 2019 Aug;40(8):1559-1566. doi: 10.1007/s10072-019-03866-8. Epub 2019 Apr 5.
Bessi V, Mazzeo S, Padiglioni S, Piccini C, Nacmias B, Sorbi S, Bracco L. From Subjective Cognitive Decline to Alzheimer's Disease: The Predictive Role of Neuropsychological Assessment, Personality Traits, and Cognitive Reserve. A 7-Year Follow-Up Study. J Alzheimers Dis. 2018;63(4):1523-1535. doi: 10.3233/JAD-171180.
Mazzeo S, Padiglioni S, Bagnoli S, Carraro M, Piaceri I, Bracco L, Nacmias B, Sorbi S, Bessi V. Assessing the effectiveness of subjective cognitive decline plus criteria in predicting the progression to Alzheimer's disease: an 11-year follow-up study. Eur J Neurol. 2020 May;27(5):894-899. doi: 10.1111/ene.14167. Epub 2020 Mar 8.
Amoroso N, Diacono D, Fanizzi A, La Rocca M, Monaco A, Lombardi A, Guaragnella C, Bellotti R, Tangaro S; Alzheimer's Disease Neuroimaging Initiative. Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge. J Neurosci Methods. 2018 May 15;302:3-9. doi: 10.1016/j.jneumeth.2017.12.011. Epub 2017 Dec 26.
Bansal D. et al. Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia - Procedia Computer Science (2018) 132: 1497-1502
Gouw AA, Alsema AM, Tijms BM, Borta A, Scheltens P, Stam CJ, van der Flier WM. EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects. Neurobiol Aging. 2017 Sep;57:133-142. doi: 10.1016/j.neurobiolaging.2017.05.017. Epub 2017 Jun 1.
Guillem F, Rougier A, Claverie B. Short- and long-delay intracranial ERP repetition effects dissociate memory systems in the human brain. J Cogn Neurosci. 1999 Jul;11(4):437-58. doi: 10.1162/089892999563526.
Amato LG, Lassi M, Vergani AA, Carpaneto J, Mazzeo S, Moschini V, Burali R, Salvestrini G, Fabbiani C, Giacomucci G, Galdo G, Morinelli C, Emiliani F, Scarpino M, Padiglioni S, Nacmias B, Sorbi S, Grippo A, Bessi V, Mazzoni A. Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease. Alzheimers Res Ther. 2025 May 31;17(1):125. doi: 10.1186/s13195-025-01765-z.
Mazzeo S, Lassi M, Padiglioni S, Vergani AA, Moschini V, Scarpino M, Giacomucci G, Burali R, Morinelli C, Fabbiani C, Galdo G, Amato LG, Bagnoli S, Emiliani F, Ingannato A, Nacmias B, Sorbi S, Grippo A, Mazzoni A, Bessi V. PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol. BMC Neurol. 2023 Aug 12;23(1):300. doi: 10.1186/s12883-023-03347-8.

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17

A weighted ensemble learning-based autonomous fault diagnosis method for photovoltaic systems using genetic algorithm

Autoři: Eskandari, Aref Aghaei, Mohammadreza Milimonfared, Jafar a další

Dostupnost: https://hdl.handle.net/11250/3181494

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18

Short-term and long-term solar irradiance forecasting with advanced machine learning techniques in Zafarana, Egypt.

Autoři: Taha A Makeen P Nazih N

Zdroj: Scientific reports [Sci Rep] 2025 Nov 12; Vol. 15 (1), pp. 39553. Date of Electronic Publication: 2025 Nov 12.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

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19

Artificial intelligence in biology and medicine.

Autoři: Iskuzhina L Turaev Z Rozhin A a další

Zdroj: Die Naturwissenschaften [Naturwissenschaften] 2025 Oct 17; Vol. 112 (6), pp. 80. Date of Electronic Publication: 2025 Oct 17.

Způsob vydávání: Journal Article; Review

Informace o časopise: Publisher: Springer Verlag Country of Publication: Germany NLM ID: 0400767 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1904 (Electronic) Linking ISSN: 00281042 NLM ISO Abbreviation: Naturwissenschaften Subsets: MEDLINE

Výrazy ze slovníku MeSH: Artificial Intelligence*/ethics , Biology*/methods , Medicine*/methods, Humans ; COVID-19 ; SARS-CoV-2

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20

Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients (CORO-CTAIOMICS) (CORO-CTAIOMICS)

Autoři: Ministry of Health, Italy Antonio Esposito, Professor

Přispěvatelé: Ministry of Health, Italy Antonio Esposito, Professor

Zdroj: Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients
Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.
Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973.
Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.
Tzolos E, McElhinney P, Williams MC, Cadet S, Dweck MR, Berman DS, Slomka PJ, Newby DE, Dey D. Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography. J Cardiovasc Comput Tomogr. 2021 Jan-Feb;15(1):81-84. doi: 10.1016/j.jcct.2020.03.007. Epub 2020 Apr 14.
Kolossvary M, Karady J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, Maurovich-Horvat P. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ Cardiovasc Imaging. 2017 Dec;10(12):e006843. doi: 10.1161/CIRCIMAGING.117.006843.
Lin A, Kolossvary M, Yuvaraj J, Cadet S, McElhinney PA, Jiang C, Nerlekar N, Nicholls SJ, Slomka PJ, Maurovich-Horvat P, Wong DTL, Dey D. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study. JACC Cardiovasc Imaging. 2020 Nov;13(11):2371-2383. doi: 10.1016/j.jcmg.2020.06.033. Epub 2020 Aug 26.
Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006 Feb 23;7:91. doi: 10.1186/1471-2105-7-91.
Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M; IBIS Network; Schlaggar BL, Pruett JR Jr, Piven J. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med. 2017 Jun 7;9(393):eaag2882. doi: 10.1126/scitranslmed.aag2882.
Brown PJ, Zhong J, Frood R, Currie S, Gilbert A, Appelt AL, Sebag-Montefiore D, Scarsbrook A. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT. Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2790-2799. doi: 10.1007/s00259-019-04495-1. Epub 2019 Sep 4.
Bardosi ZR, Dejaco D, Santer M, Kloppenburg M, Mangesius S, Widmann G, Ganswindt U, Rumpold G, Riechelmann H, Freysinger W. Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification. Cancers (Basel). 2022 Jan 18;14(3):477. doi: 10.3390/cancers14030477.
Cho HH, Lee HY, Kim E, Lee G, Kim J, Kwon J, Park H. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol. 2021 Nov 12;4(1):1286. doi: 10.1038/s42003-021-02814-7.
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006 Nov-Dec;26(6):565-74. doi: 10.1177/0272989X06295361.
Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015 Mar 4;10(3):e0118432. doi: 10.1371/journal.pone.0118432. eCollection 2015.

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Výsledky vyhledávání - (automatic OR automation) (machine OR machinery) learning algorithm based on genetic algorithms

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