Treffer 1 - 20 von 409

Python machine learning in 7 days
Packt Publishing, production company.

PyTorch deep learning in 7 days
Pytorch deep learning in 7 days
Packt Publishing, film distributor. ; Ballard, Will, speaker, author.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG
Chattopadhyay, Amit K ; Unkundiye, Aimee Pascaline N ; Pearce, Gillian
Annals of Biostatistics and Biometric Applications 6(3): 2025

New memory-based hybrid model for middle-term water demand forecasting in irrigated areas
González Perea, R. ; Fernández García, I. ; Camacho Poyato, E. ; et al.
In Agricultural Water Management 30 June 2023 284

UVBoost: An erythemal weighted ultraviolet radiation estimator based on a machine learning gradient boosting algorithm
Corrêa, Marcelo de Paula
In Journal of Quantitative Spectroscopy and Radiative Transfer April 2023 298

Методика и алгоритм определения периодов оперативного прогнозирования динамики максимально применимых частот КВ-связи на основе алгоритма машинного обучения XGBoost: Methodology and Algorithm for Determining the Periods of Operational Forecasting of the Maximum Usable Short Wave Communication Frequencies Based on the XGboost Machine Learning Algorithm
Vestnik of Volga State University of Technology. Series Radio Engineering and Infocommunication Systems. :6-16

PROSPECTS FOR SHORT-TERM FORECASTING OF RIVER STREAMFLOW FROM SMALL WATERSHED RUNOFF USING MACHINE LEARNING METHODS
Vsevolod M. Moreido ; Boris I. Gartsman ; Dimitri P. Solomatine ; et al.
Hydrosphere. Hazard processes and phenomena; Volume 2 Issue 4: Hydrosphere. Hazard processes and phenomena; 375-390; Гидросфера. Опасные процессы и явления; Том 2 Выпуск 4: Гидросфера. Опасные процессы и явления; 375-390; 2686-8385; 2686-7877

Multi-omics Dissection of Gut Microbiome Engraftment During FMT (DissEcT)
Gianluca Ianiro, Principal Investigator, MD, PhD
Disentangling Microbiome Engraftment by Multi-omics of the Gut Ecosystem During Fecal Transplant
D'Haens GR, Jobin C. Fecal Microbial Transplantation for Diseases Beyond Recurrent Clostridium Difficile Infection. Gastroenterology. 2019 Sep;157(3):624-636. doi: 10.1053/j.gastro.2019.04.053. Epub 2019 Jun 17.
Ianiro G, Bibbo S, Gasbarrini A, Cammarota G. Therapeutic modulation of gut microbiota: current clinical applications and future perspectives. Curr Drug Targets. 2014;15(8):762-70. doi: 10.2174/1389450115666140606111402.
Tixier EN, Verheyen E, Luo Y, Grinspan LT, Du CH, Ungaro RC, Walsh S, Grinspan AM. Systematic Review with Meta-Analysis: Fecal Microbiota Transplantation for Severe or Fulminant Clostridioides difficile. Dig Dis Sci. 2022 Mar;67(3):978-988. doi: 10.1007/s10620-021-06908-4. Epub 2021 Mar 22.
Baunwall SMD, Lee MM, Eriksen MK, Mullish BH, Marchesi JR, Dahlerup JF, Hvas CL. Faecal microbiota transplantation for recurrent Clostridioides difficile infection: An updated systematic review and meta-analysis. EClinicalMedicine. 2020 Nov 23;29-30:100642. doi: 10.1016/j.eclinm.2020.100642. eCollection 2020 Dec.
Ianiro G, Eusebi LH, Black CJ, Gasbarrini A, Cammarota G, Ford AC. Systematic review with meta-analysis: efficacy of faecal microbiota transplantation for the treatment of irritable bowel syndrome. Aliment Pharmacol Ther. 2019 Aug;50(3):240-248. doi: 10.1111/apt.15330. Epub 2019 May 28.
Costello SP, Soo W, Bryant RV, Jairath V, Hart AL, Andrews JM. Systematic review with meta-analysis: faecal microbiota transplantation for the induction of remission for active ulcerative colitis. Aliment Pharmacol Ther. 2017 Aug;46(3):213-224. doi: 10.1111/apt.14173. Epub 2017 Jun 14.
Proenca IM, Allegretti JR, Bernardo WM, de Moura DTH, Ponte Neto AM, Matsubayashi CO, Flor MM, Kotinda APST, de Moura EGH. Fecal microbiota transplantation improves metabolic syndrome parameters: systematic review with meta-analysis based on randomized clinical trials. Nutr Res. 2020 Nov;83:1-14. doi: 10.1016/j.nutres.2020.06.018. Epub 2020 Jul 3.
Ianiro G, Sanguinetti M, Gasbarrini A, Cammarota G. Predictors of failure after single faecal microbiota transplantation in patients with recurrent Clostridium difficile infection: results from a 3-year cohort study: authors' reply. Clin Microbiol Infect. 2017 Nov;23(11):891. doi: 10.1016/j.cmi.2017.05.005. Epub 2017 May 11. No abstract available.
Moayyedi P, Surette MG, Kim PT, Libertucci J, Wolfe M, Onischi C, Armstrong D, Marshall JK, Kassam Z, Reinisch W, Lee CH. Fecal Microbiota Transplantation Induces Remission in Patients With Active Ulcerative Colitis in a Randomized Controlled Trial. Gastroenterology. 2015 Jul;149(1):102-109.e6. doi: 10.1053/j.gastro.2015.04.001. Epub 2015 Apr 7.
Ianiro G, Maida M, Burisch J, Simonelli C, Hold G, Ventimiglia M, Gasbarrini A, Cammarota G. Efficacy of different faecal microbiota transplantation protocols for Clostridium difficile infection: A systematic review and meta-analysis. United European Gastroenterol J. 2018 Oct;6(8):1232-1244. doi: 10.1177/2050640618780762. Epub 2018 Jun 3.
Zuo T, Wong SH, Lam K, Lui R, Cheung K, Tang W, Ching JYL, Chan PKS, Chan MCW, Wu JCY, Chan FKL, Yu J, Sung JJY, Ng SC. Bacteriophage transfer during faecal microbiota transplantation in Clostridium difficile infection is associated with treatment outcome. Gut. 2018 Apr;67(4):634-643. doi: 10.1136/gutjnl-2017-313952. Epub 2017 May 24.
Zuo T, Wong SH, Cheung CP, Lam K, Lui R, Cheung K, Zhang F, Tang W, Ching JYL, Wu JCY, Chan PKS, Sung JJY, Yu J, Chan FKL, Ng SC. Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nat Commun. 2018 Sep 10;9(1):3663. doi: 10.1038/s41467-018-06103-6.
Kootte RS, Levin E, Salojarvi J, Smits LP, Hartstra AV, Udayappan SD, Hermes G, Bouter KE, Koopen AM, Holst JJ, Knop FK, Blaak EE, Zhao J, Smidt H, Harms AC, Hankemeijer T, Bergman JJGHM, Romijn HA, Schaap FG, Olde Damink SWM, Ackermans MT, Dallinga-Thie GM, Zoetendal E, de Vos WM, Serlie MJ, Stroes ESG, Groen AK, Nieuwdorp M. Improvement of Insulin Sensitivity after Lean Donor Feces in Metabolic Syndrome Is Driven by Baseline Intestinal Microbiota Composition. Cell Metab. 2017 Oct 3;26(4):611-619.e6. doi: 10.1016/j.cmet.2017.09.008.
Vaughn BP, Vatanen T, Allegretti JR, Bai A, Xavier RJ, Korzenik J, Gevers D, Ting A, Robson SC, Moss AC. Increased Intestinal Microbial Diversity Following Fecal Microbiota Transplant for Active Crohn's Disease. Inflamm Bowel Dis. 2016 Sep;22(9):2182-90. doi: 10.1097/MIB.0000000000000893.
Turpin W, Espin-Garcia O, Xu W, Silverberg MS, Kevans D, Smith MI, Guttman DS, Griffiths A, Panaccione R, Otley A, Xu L, Shestopaloff K, Moreno-Hagelsieb G; GEM Project Research Consortium; Paterson AD, Croitoru K. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat Genet. 2016 Nov;48(11):1413-1417. doi: 10.1038/ng.3693. Epub 2016 Oct 3.
Goodrich JK, Davenport ER, Clark AG, Ley RE. The Relationship Between the Human Genome and Microbiome Comes into View. Annu Rev Genet. 2017 Nov 27;51:413-433. doi: 10.1146/annurev-genet-110711-155532. Epub 2017 Sep 20.
Danne C, Rolhion N, Sokol H. Recipient factors in faecal microbiota transplantation: one stool does not fit all. Nat Rev Gastroenterol Hepatol. 2021 Jul;18(7):503-513. doi: 10.1038/s41575-021-00441-5. Epub 2021 Apr 27.
Littmann ER, Lee JJ, Denny JE, Alam Z, Maslanka JR, Zarin I, Matsuda R, Carter RA, Susac B, Saffern MS, Fett B, Mattei LM, Bittinger K, Abt MC. Host immunity modulates the efficacy of microbiota transplantation for treatment of Clostridioides difficile infection. Nat Commun. 2021 Feb 2;12(1):755. doi: 10.1038/s41467-020-20793-x.
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Lavelle A, Sokol H. Gut microbiota-derived metabolites as key actors in inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. 2020 Apr;17(4):223-237. doi: 10.1038/s41575-019-0258-z. Epub 2020 Feb 19.
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Cammarota G, Masucci L, Ianiro G, Bibbo S, Dinoi G, Costamagna G, Sanguinetti M, Gasbarrini A. Randomised clinical trial: faecal microbiota transplantation by colonoscopy vs. vancomycin for the treatment of recurrent Clostridium difficile infection. Aliment Pharmacol Ther. 2015 May;41(9):835-43. doi: 10.1111/apt.13144. Epub 2015 Mar 1.
Baruch EN, Youngster I, Ben-Betzalel G, Ortenberg R, Lahat A, Katz L, Adler K, Dick-Necula D, Raskin S, Bloch N, Rotin D, Anafi L, Avivi C, Melnichenko J, Steinberg-Silman Y, Mamtani R, Harati H, Asher N, Shapira-Frommer R, Brosh-Nissimov T, Eshet Y, Ben-Simon S, Ziv O, Khan MAW, Amit M, Ajami NJ, Barshack I, Schachter J, Wargo JA, Koren O, Markel G, Boursi B. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science. 2021 Feb 5;371(6529):602-609. doi: 10.1126/science.abb5920. Epub 2020 Dec 10.
Cammarota G, Ianiro G, Kelly CR, Mullish BH, Allegretti JR, Kassam Z, Putignani L, Fischer M, Keller JJ, Costello SP, Sokol H, Kump P, Satokari R, Kahn SA, Kao D, Arkkila P, Kuijper EJ, Vehreschild MJG, Pintus C, Lopetuso L, Masucci L, Scaldaferri F, Terveer EM, Nieuwdorp M, Lopez-Sanroman A, Kupcinskas J, Hart A, Tilg H, Gasbarrini A. International consensus conference on stool banking for faecal microbiota transplantation in clinical practice. Gut. 2019 Dec;68(12):2111-2121. doi: 10.1136/gutjnl-2019-319548. Epub 2019 Sep 28.
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Nash AK, Auchtung TA, Wong MC, Smith DP, Gesell JR, Ross MC, Stewart CJ, Metcalf GA, Muzny DM, Gibbs RA, Ajami NJ, Petrosino JF. The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome. 2017 Nov 25;5(1):153. doi: 10.1186/s40168-017-0373-4.
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Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms.
Sujeeun LY ; Goonoo N ; Ramphul H ; et al.
Publisher: Royal Society Publishing Country of Publication: England NLM ID: 101647528 Publication Model: eCollection Cited Medium: Print ISSN: 2054-5703 (Print) Linking ISSN: 20545703 NLM ISO Abbreviation: R Soc Open Sci Subsets: PubMed not MEDLINE

Automated detection of mouth opening in newborn infants.
Zeng G ; Ahn YA ; Leung TS ; et al.
Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE

A predictive model developed to classify Leishmania promastigotes at two distinct life stages using MALDI-TOF mass spectrometry.
Cubides-Cely S ; Serrano BM ; Mejía-Ospino E ; et al.
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 0410427 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-072X (Electronic) Linking ISSN: 03028933 NLM ISO Abbreviation: Arch Microbiol Subsets: MEDLINE

Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database.
Inaguma D ; Kitagawa A ; Yanagiya R ; et al.
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Machine learning approach to predict postpancreatectomy hemorrhage following pancreaticoduodenectomy: a retrospective study.
Ikuta S ; Fujikawa M ; Nakajima T ; et al.
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 9808285 Publication Model: Electronic Cited Medium: Internet ISSN: 1435-2451 (Electronic) Linking ISSN: 14352443 NLM ISO Abbreviation: Langenbecks Arch Surg Subsets: MEDLINE

The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.
Castellani D ; De Stefano V ; Brocca C ; et al.
Publisher: Springer International Country of Publication: Germany NLM ID: 8307716 Publication Model: Electronic Cited Medium: Internet ISSN: 1433-8726 (Electronic) Linking ISSN: 07244983 NLM ISO Abbreviation: World J Urol Subsets: MEDLINE

Nitric Oxide Levels Were Associated With Postoperative Delirium Following Cardiac Surgery
Qin Zhang, Professor
Identifying Nitric Oxide Levels as Predictors of Postoperative Delirium in Following Cardiac Surgical Patients

Deep Learning Detection of Pulmonary Hypertension and Low Ejection Fraction Via Digital Stethoscope and 3-Lead ECG (PH ELEFT 2-0)
Deep Learning for Detection of Pulmonary Hypertension and Reduced Left Ventricular Ejection Fraction Using a Combined Digital Stethoscope and Three-lead Electrocardiogram
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PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach (PEPPERMINT)
Britta Trautwein, Resident doctor
PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
Fernandez-Bustamante A, Frendl G, Sprung J, Kor DJ, Subramaniam B, Martinez Ruiz R, Lee JW, Henderson WG, Moss A, Mehdiratta N, Colwell MM, Bartels K, Kolodzie K, Giquel J, Vidal Melo MF. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017 Feb 1;152(2):157-166. doi: 10.1001/jamasurg.2016.4065.
Ferreyra GP, Baussano I, Squadrone V, Richiardi L, Marchiaro G, Del Sorbo L, Mascia L, Merletti F, Ranieri VM. Continuous positive airway pressure for treatment of respiratory complications after abdominal surgery: a systematic review and meta-analysis. Ann Surg. 2008 Apr;247(4):617-26. doi: 10.1097/SLA.0b013e3181675829.
Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.
Abbott TEF, Fowler AJ, Pelosi P, Gama de Abreu M, Moller AM, Canet J, Creagh-Brown B, Mythen M, Gin T, Lalu MM, Futier E, Grocott MP, Schultz MJ, Pearse RM; StEP-COMPAC Group. A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth. 2018 May;120(5):1066-1079. doi: 10.1016/j.bja.2018.02.007. Epub 2018 Mar 27.
Ball L, Pelosi P. Predictive scores for postoperative pulmonary complications: time to move towards clinical practice. Minerva Anestesiol. 2016 Mar;82(3):265-7. Epub 2015 Sep 4. No abstract available.
Nithiuthai J, Siriussawakul A, Junkai R, Horugsa N, Jarungjitaree S, Triyasunant N. Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital. Perioper Med (Lond). 2021 Dec 8;10(1):43. doi: 10.1186/s13741-021-00214-3.
Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.
Szabo M, Bozo A, Darvas K, Soos S, Ozse M, Ivanyi ZD. The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study. BMC Anesthesiol. 2021 Jan 14;21(1):19. doi: 10.1186/s12871-021-01236-6.
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