• 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 52 matches

• 2021
• 
  Hassan Raji, Muhammad Tayyab, Jianye Sui, Seyed Reza Mahmoodi, Mehdi Javanmard:
  Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review. CoRR abs/2101.01866 (2021)
• 2020
• 
  Yiru Zhao, Yifan Zhou, Yuan Liu, Yinyi Hao, Menglong Li, Xuemei Pu, Chuan Li, Zhining Wen:
  Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform. BMC Bioinform. 21(1): 195 (2020)
• 
  Bradley J. Nartowt, Gregory R. Hart, Wazir Muhammad, Ying Liang, Gigi Felice Stark, Jun Deng:
  Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification. Frontiers Big Data 3: 6 (2020)
• 
  Ankush D. Jamthikar, Deep Gupta, Amer M. Johri, Laura E. Mantella, Luca Saba, Raghu Kolluri, Aditya M. Sharma, Vijay Viswanathan, Andrew Nicolaides, Jasjit S. Suri:
  Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. J. Medical Syst. 44(12): 208 (2020)
• 
  Giovanna Sannino, Ivanoe De Falco, Giuseppe De Pietro:
  Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms. J. Sens. Actuator Networks 9(3): 34 (2020)
• 
  Bhanu Prakash Doppala, Debnath Bhattacharyya, Midhun Chakkravarthy:
  Stratification of Cardiovascular Diseases Using Deep Learning. Rev. d'Intelligence Artif. 34(4): 377-385 (2020)
• 
  Bino A. Varghese, Frank Chen, Darryl Hwang, Suzanne L. Palmer, Andre Luis De Castro Abreu, Osamu Ukimura, Monish Aron, Manju Aron, Inderbir Gill, Vinay A. Duddalwar, Gaurav Pandey:
  Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. BCB 2020: 22:1-22:10
• 
  Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré:
  Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. CHIL 2020: 151-159
• 
  Giovanna Sannino, Ivanoe De Falco, Giuseppe De Pietro:
  Photoplethysmography and Machine Learning for the Hypertension Risk Stratification. GLOBECOM (Workshops) 2020: 1-6
• 
  Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee, Sarah T. Cherng, Giulia Landi, Matteo Danieletto, Joel T. Dudley, Cesare Furlanello, Riccardo Miotto:
  Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale. CoRR abs/2003.06516 (2020)
• 2019
• 
  Cheng-Hong Yang, Sin-Hua Moi, Fu Ou-Yang, Li-Yeh Chuang, Ming-Feng Hou, Yu-Da Lin:
  Identifying Risk Stratification Associated With a Cancer for Overall Survival by Deep Learning-Based CoxPH. IEEE Access 7: 67708-67717 (2019)
• 
  Nishi Shahnaj Haider, Bikesh Kumar Singh, R. Periyasamy, Ajoy K. Behera:
  Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm. J. Medical Syst. 43(8): 255:1-255:13 (2019)
• 
  Karliane M. O. Vale, Anne Magály de Paula Canuto, Flavius L. Gorgônio, Amarildo J. F. de Lucena, Cainan T. Alves, Arthur C. Gorgônio, Araken M. Santos:
  A Data Stratification Process for Instances Selection in Semi-Supervised Learning. IJCNN 2019: 1-8
• 
  David Chettrit, Orna Bregman-Amitai, Itamar Tamir, Amir Bar, Eldad Elnekave:
  PHT-bot: a deep learning based system for automatic risk stratification of COPD patients based upon signs of pulmonary hypertension. Medical Imaging: Computer-Aided Diagnosis 2019: 109500O
• 
  Divya Shanmugam, Davis W. Blalock, John V. Guttag:
  Multiple Instance Learning for ECG Risk Stratification. MLHC 2019: 124-139
• 
  David Chettrit, Orna Bregman-Amitai, Itamar Tamir, Amir Bar, Eldad Elnekave:
  PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension. CoRR abs/1905.11773 (2019)
• 
  Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré:
  Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging. CoRR abs/1909.12475 (2019)
• 2018
• 
  Sumit K. Banchhor, Narendra D. Londhe, Tadashi Araki, Luca Saba, Petia Radeva, Narendra N. Khanna, Jasjit S. Suri:
  Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. Comput. Biol. Medicine 101: 184-198 (2018)
• 
  Mainak Biswas, Venkatanareshbabu Kuppili, Damodar Reddy Edla, Harman S. Suri, Luca Saba, Rui Tato Marinho, J. Miguel Sanches, Jasjit S. Suri:
  Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput. Methods Programs Biomed. 155: 165-177 (2018)
• 
  Venkatanareshbabu Kuppili, Mainak Biswas, Aswini Sreekumar, Harman S. Suri, Luca Saba, Damodar Reddy Edla, Rui Tato Marinho, J. Miguel Sanches, Jasjit S. Suri:
  Author Correction to: Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization. J. Medical Syst. 42(1): 18:1 (2018)
• 
  Md. Maniruzzaman, Md. Jahanur Rahman, Md. Al-MehediHasan, Harman S. Suri, Md. Menhazul Abedin, Ayman El-Baz, Jasjit S. Suri:
  Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers. J. Medical Syst. 42(5): 92:1-92:17 (2018)
• 
  William Tun, Ognjen Arandjelovic, Peter David Caie:
  Using Machine Learning and Urine Cytology for Bladder Cancer Prescreening and Patient Stratification. AAAI Workshops 2018: 507-513
• 
  Raj Brar, Shameer Khader, Abraham Saraya, Kevin Bock, Michael I. Oppenheim, John Chelico, Jamie S. Hirsch:
  Unsupervised Learning of an Advanced Illness Cohort to Develop Patient Stratification Models. AMIA 2018
• 
  Adam Brown, Bei Wang:
  Sheaf-Theoretic Stratification Learning. Symposium on Computational Geometry 2018: 14:1-14:14
• 
  Rory Raeper, Anna Lisowska, Islem Rekik:
  Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes. MICCAI (1) 2018: 599-607
• 
  M. Khalid Khan Niazi, Thomas E. Tavolara, Vidya Arole, Anil V. Parwani, Cheryl Lee, Metin N. Gurcan:
  Automated T1 bladder risk stratification based on depth of lamina propria invasion from H and E tissue biopsies: a deep learning approach. Medical Imaging: Digital Pathology 2018: 105810H
• 
  Alexandr A. Kalinin, Gerald A. Higgins, Narathip Reamaroon, S. M. Reza Soroushmehr, Ari Allyn-Feuer, Ivo D. Dinov, Kayvan Najarian, Brian D. Athey:
  Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification. CoRR abs/1801.08570 (2018)
• 
  Divya Shanmugam, Davis W. Blalock, Jen J. Gong, John V. Guttag:
  Multiple Instance Learning for ECG Risk Stratification. CoRR abs/1812.00475 (2018)
• 2017
• 
  Katherine L. Lloyd:
  Machine learning stratification for oncology patient survival. University of Warwick, Coventry, UK, 2017
• 
  Tadashi Araki, Pankaj K. Jain, Harman S. Suri, Narendra D. Londhe, Nobutaka Ikeda, Ayman El-Baz, Vimal K. Shrivastava, Luca Saba, Andrew Nicolaides, Shoaib Shafique, John R. Laird, Ajay Gupta, Jasjit S. Suri:
  Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm. Comput. Biol. Medicine 80: 77-96 (2017)

skipping 22 more matches