Artículos de revistas sobre el tema "Machine Learning, Bioinformatics, Rare Diseases, Healthcare"
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Hauschild, Anne-Christin, Marta Lemanczyk, Julian Matschinske, Tobias Frisch, Olga Zolotareva, Andreas Holzinger, Jan Baumbach y Dominik Heider. "Federated Random Forests can improve local performance of predictive models for various healthcare applications". Bioinformatics 38, n.º 8 (9 de febrero de 2022): 2278–86. http://dx.doi.org/10.1093/bioinformatics/btac065.
Texto completoR, Pooja M. "Application of Learning Approaches in Healthcare". International Journal of Advanced Medical Sciences and Technology 1, n.º 3 (10 de junio de 2021): 1–2. http://dx.doi.org/10.35940/ijamst.b3005.061321.
Texto completoM R, Pooja. "Application of Learning Approaches in Healthcare". International Journal of Advanced Medical Sciences and Technology 1, n.º 3 (10 de junio de 2021): 1–2. http://dx.doi.org/10.54105/ijamst.b3005.061321.
Texto completoSetty, Samarth Thonta, Marie-Pier Scott-Boyer, Tania Cuppens y Arnaud Droit. "New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches". International Journal of Molecular Sciences 23, n.º 12 (18 de junio de 2022): 6792. http://dx.doi.org/10.3390/ijms23126792.
Texto completoYao, Junfeng, Wen Sun, Zhongquan Jian, Qingqiang Wu y Xiaoli Wang. "Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction". Bioinformatics 38, n.º 8 (17 de febrero de 2022): 2315–22. http://dx.doi.org/10.1093/bioinformatics/btac094.
Texto completoKothari, Sonali, Shwetambari Chiwhane, Shruti Jain y Malti Baghel. "Cancerous brain tumor detection using hybrid deep learning framework". Indonesian Journal of Electrical Engineering and Computer Science 26, n.º 3 (1 de junio de 2022): 1651. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1651-1661.
Texto completoPrakash, PKS, Srinivas Chilukuri, Nikhil Ranade y Shankar Viswanathan. "RareBERT: Transformer Architecture for Rare Disease Patient Identification using Administrative Claims". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 1 (18 de mayo de 2021): 453–60. http://dx.doi.org/10.1609/aaai.v35i1.16122.
Texto completoAhmad, Iftikhar, Muhammad Javed Iqbal y Mohammad Basheri. "Biological Data Classification and Analysis Using Convolutional Neural Network". Journal of Medical Imaging and Health Informatics 10, n.º 10 (1 de octubre de 2020): 2459–65. http://dx.doi.org/10.1166/jmihi.2020.3179.
Texto completoAhmad, Iftikhar, Muhammad Javed Iqbal y Mohammad Basheri. "Biological Data Classification and Analysis Using Convolutional Neural Network". Journal of Medical Imaging and Health Informatics 10, n.º 10 (1 de octubre de 2020): 2459–65. http://dx.doi.org/10.1166/jmihi.2020.31792459.
Texto completoCesario, Alfredo, Marika D’Oria, Riccardo Calvani, Anna Picca, Antonella Pietragalla, Domenica Lorusso, Gennaro Daniele et al. "The Role of Artificial Intelligence in Managing Multimorbidity and Cancer". Journal of Personalized Medicine 11, n.º 4 (19 de abril de 2021): 314. http://dx.doi.org/10.3390/jpm11040314.
Texto completoYaqoob, Abrar, Rabia Musheer Aziz, Navneet Kumar Verma, Praveen Lalwani, Akshara Makrariya y Pavan Kumar. "A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification". Mathematics 11, n.º 5 (21 de febrero de 2023): 1081. http://dx.doi.org/10.3390/math11051081.
Texto completoBattineni, Gopi, Mohmmad Amran Hossain, Nalini Chintalapudi y Francesco Amenta. "A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review". Diagnostics 12, n.º 5 (9 de mayo de 2022): 1179. http://dx.doi.org/10.3390/diagnostics12051179.
Texto completoRevel-Vilk, Shoshana, Gabriel Chodick, Varda Shalev y Noga Gadir. "Study Design: Development of an Advanced Machine Learning Algorithm for the Early Diagnosis of Gaucher Disease Using Real-World Data". Blood 136, Supplement 1 (5 de noviembre de 2020): 13–14. http://dx.doi.org/10.1182/blood-2020-134414.
Texto completoTalwar, Vineet, Kundan Singh Chufal y Srujana Joga. "Artificial Intelligence: A New Tool in Oncologist's Armamentarium". Indian Journal of Medical and Paediatric Oncology 42, n.º 06 (diciembre de 2021): 511–17. http://dx.doi.org/10.1055/s-0041-1735577.
Texto completoKujawski, Stephanie, Boshu Ru, Amar K. Das, Nelson L. Afanador, richard baumgartner, Zhiwen Liu, Shuang Lu et al. "1344. Predicting Measles Outbreaks in the United States: Application of Different Modeling Approaches". Open Forum Infectious Diseases 8, Supplement_1 (1 de noviembre de 2021): S759. http://dx.doi.org/10.1093/ofid/ofab466.1536.
Texto completoAkushevich, Igor, Carl V. Hill y Heather E. Whitson. "LEVERAGING ANALYTIC METHODS TO EXPAND OPPORTUNITIES IN AGING-RELATED HEALTH DISPARITIES RESEARCH". Innovation in Aging 3, Supplement_1 (noviembre de 2019): S426. http://dx.doi.org/10.1093/geroni/igz038.1592.
Texto completoDutt, Yogesh, Ruby Dhiman, Tanya Singh, Arpana Vibhuti, Archana Gupta, Ramendra Pati Pandey, V. Samuel Raj, Chung-Ming Chang y Anjali Priyadarshini. "The Association between Biofilm Formation and Antimicrobial Resistance with Possible Ingenious Bio-Remedial Approaches". Antibiotics 11, n.º 7 (11 de julio de 2022): 930. http://dx.doi.org/10.3390/antibiotics11070930.
Texto completoMaurits, M., T. Huizinga, M. Reinders, S. Raychaudhuri, E. Karlson, E. Van den Akker y R. Knevel. "FRI0585 HIGH-THROUGHPUT METHODOLOGY FOR EMR-BASED IDENTIFICATION OF CLINICAL SUB-PHENOTYPES IN COMPLEX PATIENT POPULATIONS". Annals of the Rheumatic Diseases 79, Suppl 1 (junio de 2020): 897.2–897. http://dx.doi.org/10.1136/annrheumdis-2020-eular.3489.
Texto completoShang, Aijing, Imi Faghmous, Dan Drozd y Pablo Katz. "COMMODORE Cohort: A Novel, Real-World, Noninterventional Cohort Study Using a Patient-Centered Approach to Evaluate the Safety and Effectiveness of C5 Inhibitors in Patients with Paroxysmal Nocturnal Hemoglobinuria". Blood 136, Supplement 1 (5 de noviembre de 2020): 31–32. http://dx.doi.org/10.1182/blood-2020-137454.
Texto completoPressl, Christina, Caroline Jiang, Joel Correa da Rosa, Maximilian Friedrich, Winrich Freiwald y Jonathan Tobin. "2093". Journal of Clinical and Translational Science 1, S1 (septiembre de 2017): 23. http://dx.doi.org/10.1017/cts.2017.93.
Texto completoSchaefer, Julia, Moritz Lehne, Josef Schepers, Fabian Prasser y Sylvia Thun. "The use of machine learning in rare diseases: a scoping review". Orphanet Journal of Rare Diseases 15, n.º 1 (9 de junio de 2020). http://dx.doi.org/10.1186/s13023-020-01424-6.
Texto completoLabory, Justine, Gwendal Le Bideau, David Pratella, Jean-Elisée Yao, Samira Ait-El-Mkadem Saadi, Sylvie Bannwarth, Loubna El-Hami, Véronique Paquis-Fluckinger y Silvia Bottini. "ABEILLE: a novel method for ABerrant Expression Identification empLoying machine Learning from RNA-sequencing data". Bioinformatics, 5 de septiembre de 2022. http://dx.doi.org/10.1093/bioinformatics/btac603.
Texto completoPati, Sarthak, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-Han Wang, G. Anthony Reina, Patrick Foley et al. "Federated learning enables big data for rare cancer boundary detection". Nature Communications 13, n.º 1 (5 de diciembre de 2022). http://dx.doi.org/10.1038/s41467-022-33407-5.
Texto completoFernandes, Felipe, Ingridy Barbalho, Daniele Barros, Ricardo Valentim, César Teixeira, Jorge Henriques, Paulo Gil y Mário Dourado Júnior. "Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review". BioMedical Engineering OnLine 20, n.º 1 (15 de junio de 2021). http://dx.doi.org/10.1186/s12938-021-00896-2.
Texto completoTisdale, Ainslie, Christine M. Cutillo, Ramaa Nathan, Pierantonio Russo, Bryan Laraway, Melissa Haendel, Douglas Nowak et al. "The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems". Orphanet Journal of Rare Diseases 16, n.º 1 (22 de octubre de 2021). http://dx.doi.org/10.1186/s13023-021-02061-3.
Texto completoHallowell, Nina, Shirlene Badger, Aurelia Sauerbrei, Christoffer Nellåker y Angeliki Kerasidou. "“I don’t think people are ready to trust these algorithms at face value”: trust and the use of machine learning algorithms in the diagnosis of rare disease". BMC Medical Ethics 23, n.º 1 (16 de noviembre de 2022). http://dx.doi.org/10.1186/s12910-022-00842-4.
Texto completoDros, Jesper T., Isabelle Bos, Frank C. Bennis, Sytske Wiegersma, John Paget, Chiara Seghieri, Jaime Barrio Cortés y Robert A. Verheij. "Detection of primary Sjögren’s syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning". BMC Primary Care 23, n.º 1 (9 de agosto de 2022). http://dx.doi.org/10.1186/s12875-022-01804-w.
Texto completoJamian, Lia, Lee Wheless, Leslie J. Crofford y April Barnado. "Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record". Arthritis Research & Therapy 21, n.º 1 (diciembre de 2019). http://dx.doi.org/10.1186/s13075-019-2092-7.
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