Classifying patient portal messages using Convolutional Neural Networks

[1]  S. Trent Rosenbloom,et al.  A comparison of rule-based and machine learning approaches for classifying patient portal messages , 2017, Int. J. Medical Informatics.

[2]  Jamie R. Robinson,et al.  Complexity of medical decision-making in care provided by surgeons through patient portals. , 2017, The Journal of surgical research.

[3]  Matthew Richardson,et al.  Do Deep Convolutional Nets Really Need to be Deep and Convolutional? , 2016, ICLR.

[4]  Yann LeCun,et al.  Very Deep Convolutional Networks for Natural Language Processing , 2016, ArXiv.

[5]  Richard Socher,et al.  Dynamic Memory Networks for Visual and Textual Question Answering , 2016, ICML.

[6]  Richard Socher,et al.  Ask Me Anything: Dynamic Memory Networks for Natural Language Processing , 2015, ICML.

[7]  Gretchen Purcell Jackson,et al.  Use of a Patient Portal During Hospital Admissions to Surgical Services , 2016, AMIA.

[8]  Gretchen Purcell Jackson,et al.  Adoption of Secure Messaging in a Patient Portal across Pediatric Specialties , 2016, AMIA.

[9]  Sharon E. Davis,et al.  Rapid growth in surgeons’ use of secure messaging in a patient portal , 2016, Surgical Endoscopy.

[10]  Gretchen Purcell Jackson,et al.  Application of a Consumer Health Information Needs Taxonomy to Questions in Maternal-Fetal Care , 2015, AMIA.

[11]  Joshua C. Denny,et al.  Automated Classification of Consumer Health Information Needs in Patient Portal Messages , 2015, AMIA.

[12]  Weihong Deng,et al.  Very deep convolutional neural network based image classification using small training sample size , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[13]  Peter Szolovits,et al.  Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text , 2015, J. Am. Medical Informatics Assoc..

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[15]  Jing Zhou,et al.  Hate Speech Detection with Comment Embeddings , 2015, WWW.

[16]  Sharon E. Davis,et al.  Growth of Secure Messaging Through a Patient Portal as a Form of Outpatient Interaction across Clinical Specialties , 2015, Applied Clinical Informatics.

[17]  Michael Boffa,et al.  Analysis of Patient Portal Message Content in an Academic Multi-specialty Neurology Practice (S11.005) , 2015 .

[18]  Manuel Amunategui,et al.  Prediction Using Note Text: Synthetic Feature Creation with word2vec , 2015, ArXiv.

[19]  Abeed Sarker,et al.  Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features , 2015, J. Am. Medical Informatics Assoc..

[20]  Matthias Samwald,et al.  Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation , 2015, ArXiv.

[21]  Jun Zhao,et al.  Recurrent Convolutional Neural Networks for Text Classification , 2015, AAAI.

[22]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[23]  Hanspeter Pfister,et al.  UpSet: Visualization of Intersecting Sets , 2014, IEEE Transactions on Visualization and Computer Graphics.

[24]  Lynette Hirschman,et al.  De-identification of clinical narratives through writing complexity measures , 2014, Int. J. Medical Informatics.

[25]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[26]  Peter Szolovits,et al.  Automatic lymphoma classification with sentence subgraph mining from pathology reports. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[27]  Eric Gilbert,et al.  Overload is overloaded: email in the age of Gmail , 2014, CHI.

[28]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[29]  Devdatt P. Dubhashi,et al.  Extractive Summarization using Continuous Vector Space Models , 2014, CVSC@EACL.

[30]  Stephanie L. Shimada,et al.  Evaluating User Experiences of the Secure Messaging Tool on the Veterans Affairs’ Patient Portal System , 2014, Journal of medical Internet research.

[31]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[32]  Stephen S. Cha,et al.  Research and applications: Patient-generated secure messages and eVisits on a patient portal: are patients at risk? , 2013, J. Am. Medical Informatics Assoc..

[33]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[34]  Günther Palm,et al.  Learning convolutional neural networks from few samples , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[35]  Kevin B. Johnson,et al.  Understanding Patient Portal Use: Implications for Medication Management , 2013, Journal of medical Internet research.

[36]  Chandra Y. Osborn,et al.  Secure messaging and diabetes management: experiences and perspectives of patient portal users , 2013, J. Am. Medical Informatics Assoc..

[37]  David D. Cox,et al.  Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms , 2013, SciPy.

[38]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[39]  Sven Behnke,et al.  Deep Learning , 2012, KI - Künstliche Intelligenz.

[40]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[41]  S. Trent Rosenbloom,et al.  MyHealthAtVanderbilt: policies and procedures governing patient portal functionality , 2011, J. Am. Medical Informatics Assoc..

[42]  Adam Williams,et al.  Patient reported barriers to enrolling in a patient portal , 2011, J. Am. Medical Informatics Assoc..

[43]  Xiaobing Xue,et al.  Topic modeling for named entity queries , 2011, CIKM '11.

[44]  Luca Maria Gambardella,et al.  Convolutional Neural Network Committees for Handwritten Character Classification , 2011, 2011 International Conference on Document Analysis and Recognition.

[45]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[46]  Quoc V. Le,et al.  On optimization methods for deep learning , 2011, ICML.

[47]  John C. Tang,et al.  Am I wasting my time organizing email?: a study of email refinding , 2011, CHI.

[48]  R. Hasnain-Wynia,et al.  Disparities in Enrollment and Use of an Electronic Patient Portal , 2011, Journal of General Internal Medicine.

[49]  Dragomir R. Radev,et al.  Book Review: Graph-Based Natural Language Processing and Information Retrieval by Rada Mihalcea and Dragomir Radev , 2011, CL.

[50]  Martin J. Wainwright,et al.  Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.

[51]  Ralph Debusmann,et al.  Dependency Grammar: Classification and Exploration , 2011, Resource-Adaptive Cognitive Processes.

[52]  Anupam Shukla,et al.  Automatic Summary Generation from Single Document Using Information Gain , 2010, IC3.

[53]  Jeffery L. Belden,et al.  Issues and questions to consider in implementing secure electronic patient-provider web portal communications systems , 2010, Int. J. Medical Informatics.

[54]  P. Austin,et al.  Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community , 2010, Canadian Medical Association Journal.

[55]  Frans Coenen,et al.  Text Classification using Graph Mining-based Feature Extraction , 2010, SGAI Conf..

[56]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[57]  Angus Roberts,et al.  Building a semantically annotated corpus of clinical texts , 2009, J. Biomed. Informatics.

[58]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[59]  Yiming Yang,et al.  Mining social networks for personalized email prioritization , 2009, KDD.

[60]  Geoffrey E. Hinton,et al.  Deep, Narrow Sigmoid Belief Networks Are Universal Approximators , 2008, Neural Computation.

[61]  Paul C. Tang,et al.  Integrated Personal Health Records: Transformative Tools for Consumer-Centric Care , 2008, BMC Medical Informatics Decis. Mak..

[62]  D. Roden,et al.  Development of a Large‐Scale De‐Identified DNA Biobank to Enable Personalized Medicine , 2008, Clinical pharmacology and therapeutics.

[63]  Carlotta Domeniconi,et al.  Building semantic kernels for text classification using wikipedia , 2008, KDD.

[64]  David W. Bates,et al.  Medication safety messages for patients via the web portal: The MedCheck intervention , 2008, Int. J. Medical Informatics.

[65]  Kristof Coussement,et al.  Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors , 2007 .

[66]  S. Zickmund,et al.  Interest in the Use of Computerized Patient Portals: Role of the Provider–Patient Relationship , 2007, Journal of General Internal Medicine.

[67]  Frans Coenen,et al.  Statistical Identification of Key Phrases for Text Classification , 2007, MLDM.

[68]  N. Menachemi,et al.  Physicians’ Use of Email With Patients: Factors Influencing Electronic Communication and Adherence to Best Practices , 2006, Journal of medical Internet research.

[69]  Mark Dredze,et al.  Automatically classifying emails into activities , 2006, IUI '06.

[70]  Pat Langley.  Machine Learning as an Experimental Science , 2005, Machine Learning.

[71]  Anne F. Kittler,et al.  Primary care physician attitudes towards using a secure web-based portal designed to facilitate electronic communication with patients. , 2004, Informatics in primary care.

[72]  G. Purcell.  Surgical textbooks: past, present, and future. , 2003, Annals of surgery.

[73]  Abraham Kandel,et al.  Classification of Web documents using a graph model , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[74]  S. Raman,et al.  Phrase-based text representation for managing the Web documents , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[75]  Peter Jackson,et al.  Natural language processing for online applications : text retrieval, extraction and categorization , 2002 .

[76]  Michael I. Jordan,et al.  Learning with Mixtures of Trees , 2001, J. Mach. Learn. Res..

[77]  Stan Matwin,et al.  Feature Engineering for Text Classification , 1999, ICML.

[78]  Susan T. Dumais,et al.  Inductive learning algorithms and representations for text categorization , 1998, CIKM '98.

[79]  Betsy L. Humphreys,et al.  Technical Milestone: The Unified Medical Language System: An Informatics Research Collaboration , 1998, J. Am. Medical Informatics Assoc..

[80]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[81]  D. Lindberg,et al.  The Unified Medical Language System , 1993, Methods of Information in Medicine.

[82]  David D. Lewis,et al.  An evaluation of phrasal and clustered representations on a text categorization task , 1992, SIGIR '92.