• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 29 matches

• 2022
• 
  Marcia Lourenco Baptista, Kai Goebel, Elsa M. P. Henriques:
  Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artif. Intell. 306: 103667 (2022)
• 
  Sylvia P. Snoeck-Krygsman, Frederieke G. Schaafsma, Birgit H. P. M. Donker-Cools, Carel T. J. Hulshof, Lyanne P. Jansen, René J. Kox, Jan L. Hoving:
  The perceived importance of prognostic aspects considered by physicians during work disability evaluation: a survey. BMC Medical Informatics Decis. Mak. 22(1): 25 (2022)
• 2021
• 
  Fanyueyang Zhang, Xunbo Zhang, Zirui Tang, Xianhua Song:
  Evaluation and Prognostics of the Higher Education Based on Neural Network and AHP-PLS Structural Equations. ICPCSEE (2) 2021: 475-490
• 2020
• 
  Weijie Kang, Jiyang Xiao, Mingqing Xiao, Yangguang Hu, Haizhen Zhu, Jianfeng Li:
  Research on Remaining Useful Life Prognostics Based on Fuzzy Evaluation-Gaussian Process Regression Method. IEEE Access 8: 71965-71973 (2020)
• 
  Enrico Longato, Martina Vettoretti, Barbara Di Camillo:
  A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J. Biomed. Informatics 108: 103496 (2020)
• 
  Jeba Afifa Zinia, Md. Shahedur Rahman:
  Evaluation of the prognostic significance of CDK6 in breast cancer. Netw. Model. Anal. Health Informatics Bioinform. 9(1): 40 (2020)
• 
  Farzin Negahbani, Rasool Sabzi, Bita Pakniyat Jahromi, Fatemeh Movahedi, Mahsa Kohandel Shirazi, Shayan Majidi, Dena Firouzabadi, Amirreza Dehganian:
  PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline. CoRR abs/2010.04713 (2020)
• 2019
• 
  J. Raja, K. Gunasekaran, R. Pitchai:
  Prognostic evaluation of multimodal biometric traits recognition based human face, finger print and iris images using ensembled SVM classifier. Clust. Comput. 22(Suppl 1): 215-228 (2019)
• 
  Salma Harrath, Jaouher Ben Ali, Taoufik Zouaghi, Noureddine Zerhouni:
  A New Adaptive Prognostic Strategy Based on Online Future Evaluation and Extended Kalman Filtering. CoDIT 2019: 2033-2038
• 2018
• 
  Gurudeeban Selvaraj, Satyavani Kaliamurthi, Aman Chandra Kaushik, Abbas Khan, Yong-Kai Wei, William C. S. Cho, Keren Gu, Dong-Qing Wei:
  Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms. J. Biomed. Informatics 86: 120-134 (2018)
• 2017
• 
  Lobna Belkacem, Zineb Simeu-Abazi, Hedi Dhouibi, Eric Gascard, Hassani Messaoud:
  Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies. Reliab. Eng. Syst. Saf. 164: 98-109 (2017)
• 
  Gopichandh Danala, Yunzhi Wang, Theresa C. Thai, Camille Gunderson, Katherine Moxley, Kathleen N. Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu:
  Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study. Medical Imaging: Computer-Aided Diagnosis 2017: 1013449
• 
  Kedun Mao, Yongxin Zhu, Zhixiong Chen, Xiang Tao, Qixuan Xue, Han Wu, Yishu Mao, Junjie Hou:
  A Visual Model-Based Evaluation Framework of Cloud-Based Prognostics and Health Management. SmartCloud 2017: 33-40
• 2016
• 
  James Kuria Kimotho:
  Development and performance evaluation of prognostic approaches for technical systems. University of Paderborn, Germany, 2016
• 2015
• 
  Nicola Dinapoli, Anna Rita Alitto, Mauro Vallati, Roberto Gatta, Rosa Autorino, Luca Boldrini, Andrea Damiani, Vincenzo Valentini:
  Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features. EMBC 2015: 771-774
• 
  Surajit Roy, Gerges Dib, Pradeep Ramuhalli, Evelyn H. Hirt, Matthew S. Prowant, Lorenzo Luzi, Allan F. Pardini, Stan G. Pitman:
  Progress towards prognostic health management of passive components in advanced reactors - Model selection and evaluation. ICPHM 2015: 1-8
• 2013
• 
  Yan Chen, Feibai Zhu, Jay Lee:
  Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method. Comput. Ind. 64(3): 214-225 (2013)
• 
  Fatih Camci, Kamal Medjaher, Noureddine Zerhouni, Patrick Nectoux:
  Feature Evaluation for Effective Bearing Prognostics. Qual. Reliab. Eng. Int. 29(4): 477-486 (2013)
• 
  Sayyed Mostafa Mostafavi, Janice I. Glasgow, Sean P. Dukelow, Stephen H. Scott, Parvin Mousavi:
  Prediction of stroke-related diagnostic and prognostic measures using robot-based evaluation. ICORR 2013: 1-6
• 2010
• 
  Birte Hellwig, Jan G. Hengstler, Marcus Schmidt, Mathias C. Gehrmann, Wiebke Schormann, Jörg Rahnenführer:
  Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes. BMC Bioinform. 11: 276 (2010)
• 2009
• 
  Cesar Souza, Ednaldo Pizzolato, Renata Gonçalves Mendes, Audrey Borghi-Silva, Mauricio Machado, Paulo Correa:
  Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery. ICMLA 2009: 799-803
• 2007
• 
  Qianfei Yuan, Congzhong Cai, Hanguang Xiao, Xinghua Liu, Yufeng Wen:
  Diagnosis of Breast Tumours and Evaluation of Prognostic Risk by Using Machine Learning Approaches. ICIC (3) 2007: 1250-1260
• 
  Chunsheng Yang, Sylvain Létourneau:
  Model evaluation for prognostics: estimating cost saving for the end users. ICMLA 2007: 304-309
• 2003
• 
  Marko Robnik-Sikonja, David Cukjati, Igor Kononenko:
  Comprehensible evaluation of prognostic factors and prediction of wound healing. Artif. Intell. Medicine 29(1-2): 25-38 (2003)
• 2001
• 
  Marko Robnik-Sikonja, David Cukjati, Igor Kononenko:
  Evaluation of Prognostic Factors and Prediction of Chronic Wound Healing Rate by Machine Learning Tools. AIME 2001: 77-87
• 
  Huseyin Seker, Michael O. Odetayo, Dobrila Petrovic, Raouf N. Gorgui-Naguib, Freddie C. Hamdy:
  Statistical and soft feature evaluation indices for prostate cancer prognostic factor assessment. EUSFLAT Conf. 2001: 26-29
• 1999
• 
  Sarabjot S. Anand, Ann E. Smith, P. W. Hamilton, J. S. Anand, John G. Hughes, P. H. Bartels:
  An evaluation of intelligent prognostic systems for colorectal cancer. Artif. Intell. Medicine 15(2): 193-214 (1999)
• 1995
• 
  F. Fiocchi, A. Gamba, Rita Pizzi, F. Sicurello:
  A Neural Support to the Prognostic Evaluation of Cardiac Surgery. AIME 1995: 435-436
• 1974
• 
  Ladislav Tondl:
  Semantic evaluation of prognostic statements on the base of probabilistic parameters. Kybernetika 10(3): 199-220 (1974)