Top Purchase Related
Home
>
Node
>
Subnode

Navigating data via machine learning

Kutscher, B.

Modern Healthcare 46(18): 28

2016

ISSN/ISBN: 0160-7480
PMID: 27363103
Document Number: 686494

Document emailed within 1 workday
Secure & encrypted payments

Juhola, M. 2008: On machine learning classification of otoneurological data Studies in Health Technology and Informatics 136: 211-216
Kim, D.; Quan, L.; Seo, M.; Kim, K.; Kim, J.-W.; Zhu, Y. 2023: Interpretable machine learning-based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data Suicide and Life-Threatening Behavior 53(3): 484-498
Su, Y.-S.; Lin, Y.-D.; Liu, T.-Q. 2022: Applying machine learning technologies to explore students' learning features and performance prediction Frontiers in Neuroscience 16: 1018005
Khumrin, P.; Ryan, A.; Judd, T.; Verspoor, K. 2017: Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students Studies in Health Technology and Informatics 245: 447-451
Li, J.; Wang, J.; Hang, W. 2012: Research on operating characteristics of multiclass receiver in machine learning Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 29(1): 170-174
Marsland, S.; Buchan, I. 2004: Clinical quality needs complex adaptive systems and machine learning Studies in Health Technology and Informatics 107(Part 1): 644-647
Lukacic, Z.; Kern, J. 2002: Consistency and reproducibility of attribute extraction by different machine learning systems Studies in Health Technology and Informatics 90: 316-319
Wu, G.-r.; Qi, F.-h. 2006: Non-linear registration of MR brain images integrated with machine learning Zhongguo Yi Liao Qi Xie Za Zhi 30(4): 268-270
Panahiazar, M.; Taslimitehrani, V.; Pereira, N.; Pathak, J. 2015: Using EHRs and Machine Learning for Heart Failure Survival Analysis Studies in Health Technology and Informatics 216: 40-44
Mahajan, S.M.; Mahajan, A.S.; King, R.; Negahban, S. 2018: Predicting Risk of 30-Day Readmissions Using two Emerging Machine Learning Methods Studies in Health Technology and Informatics 250: 250-255
Alghamdi, W.; Stamate, D.; Stahl, D.; Zamyatin, A.; Murray, R.; Di Forti, M. 2018: A new Machine Learning Framework for Understanding the Link Between Cannabis use and First-Episode Psychosis Studies in Health Technology and Informatics 248: 9-16
Sippl, P.; Ganslandt, T.; Prokosch, H.-U.; Muenster, T.; Toddenroth, D. 2017: Machine Learning Models of Post-Intubation Hypoxia During General Anesthesia Studies in Health Technology and Informatics 243: 212-216
Rubio-López, I.; Costumero, R.; Ambit, H.éc.; Gonzalo-Martín, C.; Menasalvas, E.; Rodríguez González, A. 2017: Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques Studies in Health Technology and Informatics 235: 251-255
Gharehbaghi, A.; Lindén, M.; Babic, A. 2017: A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods Studies in Health Technology and Informatics 235: 43-47
Yoon, S.; Choi, T.; Odlum, M.; Mitchell, D.A.; Kronish, I.M.; Davidson, K.W.; Finkelstein, J. 2018: Machine Learning to Identify Behavioral Determinants of Oral Health in Inner City Older Hispanic Adults Studies in Health Technology and Informatics 251: 253-256
Brenner, M. 2017: Debunking the Top Three Myths about Industrial Machine Safeguarding: Lack of understanding of OSHA machine guard regulations continues to make machine safeguarding one of OSHA's Top 10 violations Occupational Health and Safety 86(2): 22; 24-25
Li, B.; Lu, C.; Zhao, J.; Tian, J.; Sun, J.; Hu, C. 2023: Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model Journal of Environmental Management 333: 117416
Della Mea, V.; Fioresi, V. 2012: ICF machine: a web-based system for collection of ICF data Studies in Health Technology and Informatics 180: 1188-1190
Ireland, D.; Wang, Z.; Lamont, R.; Liddle, J. 2016: Classification of Movement of people with Parkinsons Disease Using Wearable Inertial Movement Units and Machine Learning Studies in Health Technology and Informatics 227: 61-66
Haake, K.W.; List, P.; Baier, D.; Zimmermann, G.; Pretzsch, G.; Alexander, H. 1997: Risk assessment in ovarian hyperstimulation syndrome (OHS) using the machine learning system (Decision Master) in 155 in-vitro fertilisations and embryo-transfer (IVF/ET) cycles with a long stimulation protocol Zentralblatt für Gynakologie 119(Suppl 1): 23-27