Fetching the paper…
Reading the bibliography…
Artificial intelligence has provided us with an exploration of a whole new research era.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
David H Hubel and Torsten N Wiesel · 1962
Earlier work this paper cites.
Learning and relearning in boltzmann machines
Geoffrey E Hinton, Terrence J Sejnowski, et al · 1986
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, Ronald J Williams, et al · 1988
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser · 1989
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, Paolo Frasconi, et al · 1994
Earlier work this paper cites.
Support vector machine
V Vapnik · 1995
Earlier work this paper cites.
Learning many related tasks at the same time with backpropagation
Rich Caruana · 1995
Earlier work this paper cites.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
Support vector machines
John Shawe-Taylor and Nello Cristianini · 2000
Earlier work this paper cites.
General practitioner-hospital communications: A review of discharge summaries
Stephen Wilson, Warwick Ruscoe, Margaret Chapman, and Rhona Miller · 2002
Earlier work this paper cites.
Multiple model-based reinforcement learning
Kenji Doya, Kazuyuki Samejima, Ken-ichi Katagiri, and Mitsuo Kawato · 2002
Earlier work this paper cites.
Disruption of an sf2/asf-dependent exonic splicing enhancer in smn2 causes spinal muscular atrophy in the absence of smn1
Luca Cartegni and Adrian R Krainer · 2002
Earlier work this paper cites.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
Earlier work this paper cites.
Support vector machine soft margin classifiers: error analysis
Di-Rong Chen, Qiang Wu, Yiming Ying, and Ding-Xuan Zhou · 2004
Earlier work this paper cites.
Multilinear operators for higher-order decompositions
Tamara Gibson Kolda · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
Earlier work this paper cites.
Efficient learning of sparse representations with an energy-based model
Christopher Poultney, Sumit Chopra, Yann L Cun, et al · 2007
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Exploring strategies for training deep neural networks
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin · 2009
Earlier work this paper cites.
Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey Hinton · 2009
Earlier work this paper cites.
Monitoring motor fluctuations in patients with parkinson’s disease using wearable sensors
Shyamal Patel, Konrad Lorincz, Richard Hughes, Nancy Huggins, John Growdon, David Standaert, Metin Akay, Jennifer Dy, Matt Welsh, and Paolo Bonato · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
Earlier work this paper cites.
Biometric and mobile gait analysis for early diagnosis and therapy monitoring in parkinson’s disease
Jens Barth, Jochen Klucken, Patrick Kugler, Thomas Kammerer, Ralph Steidl, Jürgen Winkler, Joachim Hornegger, and Björn Eskofier · 2011
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel P. Kuksa · 2011
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
Earlier work this paper cites.
Deep learners benefit more from out-of-distribution examples
Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger-Lewandowski, Thomas Breuel, Youssouf Chherawala, Moustapha Cisse, Myriam Côté, Dumitru Erhan, Jeremy Eustache, et al · 2011
Earlier work this paper cites.
Get online support, feel better–sentiment analysis and dynamics in an online cancer survivor community
Baojun Qiu, Kang Zhao, Prasenjit Mitra, Dinghao Wu, Cornelia Caragea, John Yen, Greta E Greer, and Kenneth Portier · 2011
Earlier work this paper cites.
A review of wearable sensors and systems with application in rehabilitation
Shyamal Patel, Hyung-Soon Park, Paolo Bonato, Leighton Chan, and Mary Rodgers · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
3d convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2012
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
Earlier work this paper cites.
A survey of actor-critic reinforcement learning: Standard and natural policy gradients
Ivo Grondman, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska · 2012
Earlier work this paper cites.
A practical guide to training restricted boltzmann machines
Geoffrey E Hinton · 2012
Earlier work this paper cites.
Next-generation phenotyping of electronic health records
George Hripcsak and David J Albers · 2012
Earlier work this paper cites.
Mining electronic health records: towards better research applications and clinical care
Peter B Jensen, Lars J Jensen, and Søren Brunak · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Manifold learning of brain mris by deep learning
Tom Brosch, Roger Tam, Alzheimer’s Disease Neuroimaging Initiative, et al · 2013
Earlier work this paper cites.
Deep learning-based feature representation for ad/mci classification
Heung-Il Suk and Dinggang Shen · 2013
Earlier work this paper cites.
Systems biology approaches and applications in obesity, diabetes, and cardiovascular diseases
Qingying Meng, Ville-Petteri Mäkinen, Helen Luk, and Xia Yang · 2013
Earlier work this paper cites.
Predicting postpartum changes in emotion and behavior via social media
Munmun De Choudhury, Scott Counts, and Eric Horvitz · 2013
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
On the ground validation of online diagnosis with twitter and medical records
Todd Bodnar, Victoria C Barclay, Nilam Ram, Conrad S Tucker, and Marcel Salathé · 2014
Earlier work this paper cites.
An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages
Suppawong Tuarob, Conrad S Tucker, Marcel Salathe, and Nilam Ram · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Probabilistic reasoning in intelligent systems: networks of plausible inference
Judea Pearl · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Deep learning for neuroimaging: a validation study
Sergey M Plis, Devon R Hjelm, Ruslan Salakhutdinov, Elena A Allen, Henry J Bockholt, Jeffrey D Long, Hans J Johnson, Jane S Paulsen, Jessica A Turner, and Vince D Calhoun · 2014
Earlier work this paper cites.
Hierarchical feature representation and multimodal fusion with deep learning for ad/mci diagnosis
Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al · 2014
Earlier work this paper cites.
Improved pattern learning for bootstrapped entity extraction
Sonal Gupta and Christopher Manning · 2014
Earlier work this paper cites.
De-identification in natural language processing
Veronika Vincze and Richard Farkas · 2014
Earlier work this paper cites.
De-identification of clinical narratives through writing complexity measures
Muqun Li, David Carrell, John Aberdeen, Lynette Hirschman, and Bradley A Malin · 2014
Earlier work this paper cites.
Dann: a deep learning approach for annotating the pathogenicity of genetic variants
Daniel Quang, Yifei Chen, and Xiaohui Xie · 2014
Earlier work this paper cites.
Multi-level gene/mirna feature selection using deep belief nets and active learning
Rania Ibrahim, Noha A Yousri, Mohamed A Ismail, and Nagwa M El-Makky · 2014
Earlier work this paper cites.
Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment
Will WK Ma and Albert Chan · 2014
Earlier work this paper cites.
Breast cancer and quality of life: medical information extraction from health forums
Thomas Opitz, Jérôme Azé, Sandra Bringay, Cyrille Joutard, Christian Lavergne, and Caroline Mollevi · 2014
Earlier work this paper cites.
Finding information about mental health in microblogging platforms: a case study of depression
Max L Wilson, Susan Ali, and Michel F Valstar · 2014
Earlier work this paper cites.
Mental health discourse on reddit: Self-disclosure, social support, and anonymity
Munmun De Choudhury and Sushovan De · 2014
Earlier work this paper cites.
Does sustained participation in an online health community affect sentiment?
Shaodian Zhang, Erin Bantum, Jason Owen, and Noémie Elhadad · 2014
Earlier work this paper cites.
A new initiative on precision medicine
Francis S Collins and Harold Varmus · 2015
Earlier work this paper cites.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Machine learning in genomic medicine: a review of computational problems and data sets
Michael KK Leung, Andrew Delong, Babak Alipanahi, and Brendan J Frey · 2015
Earlier work this paper cites.
The human splicing code reveals new insights into the genetic determinants of disease
Hui Y Xiong, Babak Alipanahi, Leo J Lee, Hannes Bretschneider, Daniele Merico, Ryan KC Yuen, Yimin Hua, Serge Gueroussov, Hamed S Najafabadi, Timothy R Hughes, et al · 2015
Earlier work this paper cites.
Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
Earlier work this paper cites.
Social restricted boltzmann machine: Human behavior prediction in health social networks
NhatHai Phan, Dejing Dou, Brigitte Piniewski, and David Kil · 2015
Earlier work this paper cites.
Uses of electronic health records for public health surveillance to advance public health
Guthrie S Birkhead, Michael Klompas, and Nirav R Shah · 2015
Earlier work this paper cites.
Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa · 2015
Earlier work this paper cites.
Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
Earlier work this paper cites.
Predicting alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks
Adrien Payan and Giovanni Montana · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Anatomy-specific classification of medical images using deep convolutional nets
Holger R Roth, Christopher T Lee, Hoo-Chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, and Ronald M Summers · 2015
Earlier work this paper cites.
Standard plane localization in fetal ultrasound via domain transferred deep neural networks
Hao Chen, Dong Ni, Jing Qin, Shengli Li, Xin Yang, Tianfu Wang, and Pheng Ann Heng · 2015
Earlier work this paper cites.
Multi-scale convolutional neural networks for lung nodule classification
Wei Shen, Mu Zhou, Feng Yang, Caiyun Yang, and Jie Tian · 2015
Earlier work this paper cites.
Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning
Youyi Song, Ling Zhang, Siping Chen, Dong Ni, Baiying Lei, and Tianfu Wang · 2015
Earlier work this paper cites.
Automated anatomical landmark detection ondistal femur surface using convolutional neural network
Dong Yang, Shaoting Zhang, Zhennan Yan, Chaowei Tan, Kang Li, and Dimitris Metaxas · 2015
Earlier work this paper cites.
Improving computer-aided detection using convolutional neural networks and random view aggregation
Holger R Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, and Ronald M Summers · 2015
Earlier work this paper cites.
A robust deep model for improved classification of ad/mci patients
Feng Li, Loc Tran, Kim-Han Thung, Shuiwang Ji, Dinggang Shen, and Jiang Li · 2015
Earlier work this paper cites.
3d deep learning for efficient and robust landmark detection in volumetric data
Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, and Dorin Comaniciu · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation
Marijn F Stollenga, Wonmin Byeon, Marcus Liwicki, and Juergen Schmidhuber · 2015
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
Earlier work this paper cites.
Interleaved text/image deep mining on a very large-scale radiology database
Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, and Ronald M Summers · 2015
Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
Earlier work this paper cites.
Predicting sequences of clinical events by using a personalized temporal latent embedding model
Cristóbal Esteban, Danilo Schmidt, Denis Krompaß, and Volker Tresp · 2015
Earlier work this paper cites.
Learning probabilistic phenotypes from heterogeneous ehr data
Rimma Pivovarov, Adler J Perotte, Edouard Grave, John Angiolillo, Chris H Wiggins, and Noémie Elhadad · 2015
Earlier work this paper cites.
Learning to diagnose with lstm recurrent neural networks
Zachary C Lipton, David C Kale, Charles Elkan, and Randall Wetzel · 2015
Earlier work this paper cites.
Deep computational phenotyping
Zhengping Che, David Kale, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu · 2015
Earlier work this paper cites.
Named entity recognition in chinese clinical text using deep neural network
Yonghui Wu, Min Jiang, Jianbo Lei, and Hua Xu · 2015
Earlier work this paper cites.
Subgraph augmented non-negative tensor factorization (santf) for modeling clinical narrative text
Yuan Luo, Yu Xin, Ephraim Hochberg, Rohit Joshi, Ozlem Uzuner, and Peter Szolovits · 2015
Earlier work this paper cites.
Deepdive: a data management system for automatic knowledge base construction
Ce Zhang · 2015
Earlier work this paper cites.
Learning vector representation of medical objects via emr-driven nonnegative restricted boltzmann machines (enrbm)
Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh · 2015
Earlier work this paper cites.
Temporal pattern and association discovery of diagnosis codes using deep learning
Saaed Mehrabi, Sunghwan Sohn, Dingheng Li, Joshua J Pankratz, Terry Therneau, Jennifer L St Sauver, Hongfang Liu, and Mathew Palakal · 2015
Earlier work this paper cites.
A deep learning framework for modeling structural features of rna-binding protein targets
Sai Zhang, Jingtian Zhou, Hailin Hu, Haipeng Gong, Ligong Chen, Chao Cheng, and Jianyang Zeng · 2015
Earlier work this paper cites.
Massively multitask networks for drug discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande · 2015
Earlier work this paper cites.
Social media usage: 2005-2015
Andrew Perrin · 2015
Earlier work this paper cites.
The impact of social media on the sexual and social wellness of adolescents
Lisa M Cookingham and Ginny L Ryan · 2015
Earlier work this paper cites.
Using distance estimation and deep learning to simplify calibration in food calorie measurement
Pallavi Kuhad, Abdulsalam Yassine, and Shervin Shimohammadi · 2015
Earlier work this paper cites.
Detecting changes in suicide content manifested in social media following celebrity suicides
Mrinal Kumar, Mark Dredze, Glen Coppersmith, and Munmun De Choudhury · 2015
Earlier work this paper cites.
Anorexia on tumblr: A characterization study
Munmun De Choudhury · 2015
Earlier work this paper cites.
Impact of compliance with a care bundle on acute kidney injury outcomes: a prospective observational study
Nitin V Kolhe, David Staples, Timothy Reilly, Daniel Merrison, Christopher W Mcintyre, Richard J Fluck, Nicholas M Selby, and Maarten W Taal · 2015
Cited alongside, same era.
Deep learning for health informatics
Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang-Zhong Yang · 2016
Cited alongside, same era.
Biomarker tests for molecularly targeted therapies–the key to unlocking precision medicine
Gary H Lyman and Harold L Moses · 2016
Cited alongside, same era.
Deep learning for computational biology
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, and Oliver Stegle · 2016
Cited alongside, same era.
Deep learning trends for focal brain pathology segmentation in mri
Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, and Pierre-Marc Jodoin · 2016
Cited alongside, same era.
A multi-scale u-net for semantic segmentation of histological images from radical prostatectomies
Jiayun Li, Karthik V Sarma, King Chung Ho, Arkadiusz Gertych, Beatrice S Knudsen, and Corey W Arnold · 2017
Later among the works it cites.
Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images
Lequan Yu, Xin Yang, Hao Chen, Jing Qin, and Pheng Ann Heng · 2017
Later among the works it cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Later among the works it cites.
An artificial agent for robust image registration
Rui Liao, Shun Miao, Pierre de Tournemire, Sasa Grbic, Ali Kamen, Tommaso Mansi, and Dorin Comaniciu · 2017
Later among the works it cites.
Modeling healthcare quality via compact representations of electronic health records
Jelena Stojanovic, Djordje Gligorijevic, Vladan Radosavljevic, Nemanja Djuric, Mihajlo Grbovic, and Zoran Obradovic · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jake Luo, Min Wu, Deepika Gopukumar, and Yiqing Zhao · 2016
Cited alongside, same era.
Deep learning in drug discovery
Erik Gawehn, Jan A Hiss, and Gisbert Schneider · 2016
Cited alongside, same era.
Deep artificial neural networks and neuromorphic chips for big data analysis: pharmaceutical and bioinformatics applications
Lucas Pastur-Romay, Francisco Cedron, Alejandro Pazos, and Ana Porto-Pazos · 2016
Cited alongside, same era.
3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients
Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, and Dinggang Shen · 2016
Cited alongside, same era.
Multimodal deep learning for cervical dysplasia diagnosis
Tao Xu, Han Zhang, Xiaolei Huang, Shaoting Zhang, and Dimitris N Metaxas · 2016
Cited alongside, same era.
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
Ravi K Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A Helvie, Jun Wei, and Kenny Cha · 2016
Cited alongside, same era.
Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition
Zhennan Yan, Yiqiang Zhan, Zhigang Peng, Shu Liao, Yoshihisa Shinagawa, Shaoting Zhang, Dimitris N Metaxas, and Xiang Sean Zhou · 2016
Cited alongside, same era.
Later among the works it cites.
A hybrid neural network model for joint prediction of presence and period assertions of medical events in clinical notes
Li Rumeng, N Jagannatha Abhyuday, and Yu Hong · 2017
Later among the works it cites.
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Stephen F Weng, Jenna Reps, Joe Kai, Jonathan M Garibaldi, and Nadeem Qureshi · 2017
Later among the works it cites.
Evaluating phecodes, clinical classification software, and icd-9-cm codes for phenome-wide association studies in the electronic health record
Wei-Qi Wei, Lisa A. Bastarache, Robert J. Carroll, Joy E. Marlo, Travis J. Osterman, Eric R. Gamazon, Nancy J. Cox, Dan M. Roden, and Joshua C. Denny · 2017
Later among the works it cites.
Cluster analysis to identify possible subgroups in tinnitus patients
Minke JC van den Berge, Rolien H Free, Rosemarie Arnold, Emile de Kleine, Rutger Hofman, J Marc C van Dijk, and Pim van Dijk · 2017
Later among the works it cites.
Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations
Sunghyon Kyeong, Jae-Jin Kim, and Eunjoo Kim · 2017
Later among the works it cites.
Deep learning for automated extraction of primary sites from cancer pathology reports
John X Qiu, Hong-Jun Yoon, Paul A Fearn, and Georgia D Tourassi · 2017
Later among the works it cites.
Assigning clinical codes with data-driven concept representation on dutch clinical free text
Elyne Scheurwegs, Kim Luyckx, Léon Luyten, Bart Goethals, and Walter Daelemans · 2017
Later among the works it cites.
Diagnostic inferencing via improving clinical concept extraction with deep reinforcement learning: A preliminary study
Yuan Ling, Sadid A Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, and Oladimeji Farri · 2017
Later among the works it cites.
De-identification of patient notes with recurrent neural networks
Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, and Peter Szolovits · 2017
Later among the works it cites.
Deep reinforcement learning for sepsis treatment
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, and Marzyeh Ghassemi · 2017
Later among the works it cites.
A reinforcement learning approach to weaning of mechanical ventilation in intensive care units
Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, and Barbara E Engelhardt · 2017
Later among the works it cites.
An unchartered journey for ribosomes: circumnavigating circular rnas to produce proteins
Deirdre C Tatomer and Jeremy E Wilusz · 2017
Later among the works it cites.
Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding
Xu Min, Wanwen Zeng, Ning Chen, Ting Chen, and Rui Jiang · 2017
Later among the works it cites.
A deep learning approach for cancer detection and relevant gene identification
Padideh Danaee, Reza Ghaeini, and David A Hendrix · 2017
Later among the works it cites.
Denoising genome-wide histone chip-seq with convolutional neural networks
Pang Wei Koh, Emma Pierson, and Anshul Kundaje · 2017
Later among the works it cites.
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2017
Later among the works it cites.
Chemical space mimicry for drug discovery
William Yuan, Dadi Jiang, Dhanya K Nambiar, Lydia P Liew, Michael P Hay, Joshua Bloomstein, Peter Lu, Brandon Turner, Quynh-Thu Le, Robert Tibshirani, et al · 2017
Later among the works it cites.
drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico
Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, Alex Aliper, and Alex Zhavoronkov · 2017
Later among the works it cites.
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E Turner, and Douglas Eck · 2017
Later among the works it cites.
The power of the patient voice: learning indicators of treatment adherence from an online breast cancer forum
Zhijun Yin, Bradley Malin, Jeremy Warner, Pei-Yun Hsueh, and Ching-Hua Chen · 2017
Later among the works it cites.
Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact
Shaodian Zhang, Erin O’Carroll Bantum, Jason Owen, Suzanne Bakken, and Noémie Elhadad · 2017
Later among the works it cites.
Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks
Shaodian Zhang, Edouard Grave, Elizabeth Sklar, and Noémie Elhadad · 2017
Later among the works it cites.
Cataloguing treatments discussed and used in online autism communities
Shaodian Zhang, Tian Kang, Lin Qiu, Weinan Zhang, Yong Yu, and Noémie Elhadad · 2017
Later among the works it cites.
Wearable sensors for remote health monitoring
Sumit Majumder, Tapas Mondal, and M Deen · 2017
Later among the works it cites.
Nutrinet: a deep learning food and drink image recognition system for dietary assessment
Simon Mezgec and Barbara Koroušić Seljak · 2017
Later among the works it cites.
Gender and cross-cultural differences in social media disclosures of mental illness
Munmun De Choudhury, Sanket S Sharma, Tomaz Logar, Wouter Eekhout, and René Clausen Nielsen · 2017
Later among the works it cites.
Detecting and characterizing eating-disorder communities on social media
Tao Wang, Markus Brede, Antonella Ianni, and Emmanouil Mentzakis · 2017
Later among the works it cites.
Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis
B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi · 2018
Later among the works it cites.
Biomedical informatics and machine learning for clinical genomics
James A Diao, Isaac S Kohane, and Arjun K Manrai · 2018
Later among the works it cites.
A review and assessment framework for mobile-based emergency intervention apps
Michal Yablowitz and David Schwartz · 2018
Later among the works it cites.
Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation
Lu Wang, Wei Zhang, Xiaofeng He, and Hongyuan Zha · 2018
Later among the works it cites.
Predicting infectious disease using deep learning and big data
Sangwon Chae, Sungjun Kwon, and Donghyun Lee · 2018
Later among the works it cites.
Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning
Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, and Kaori Togashi · 2018
Later among the works it cites.
3-d convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network
Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K Kalra, Ling Sun, Wenxiang Cong, and Ge Wang · 2018
Later among the works it cites.
Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification
Wentao Zhu, Chaochun Liu, Wei Fan, and Xiaohui Xie · 2018
Later among the works it cites.
A modified u-net convolutional network featuring a nearest-neighbor re-sampling-based elastic-transformation for brain tissue characterization and segmentation
SM Kamrul Hasan and Cristian A Linte · 2018
Later among the works it cites.
Fully convolutional structured lstm networks for joint 4d medical image segmentation
Yang Gao, Jeff M Phillips, Yan Zheng, Renqiang Min, P Thomas Fletcher, and Guido Gerig · 2018
Later among the works it cites.
Adversarial deep structured nets for mass segmentation from mammograms
Wentao Zhu, Xiang Xiang, Trac D Tran, Gregory D Hager, and Xiaohui Xie · 2018
Later among the works it cites.
Ultrasound image segmentation: A deeply supervised network with attention to boundaries
Deepak Mishra, Santanu Chaudhury, Mukul Sarkar, and Arvinder Singh Soin · 2018
Later among the works it cites.
Statistical iterative cbct reconstruction based on neural network
Binbin Chen, Kai Xiang, Zaiwen Gong, Jing Wang, and Shan Tan · 2018
Later among the works it cites.
Improving palliative care with deep learning
Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, and Nigam H Shah · 2018
Later among the works it cites.
Deep ehr: Chronic disease prediction using medical notes
Jingshu Liu, Zachariah Zhang, and Narges Razavian · 2018
Later among the works it cites.
Readmission prediction via deep contextual embedding of clinical concepts
Cao Xiao, Tengfei Ma, Adji B Dieng, David M Blei, and Fei Wang · 2018
Later among the works it cites.
Using machine learning approaches for emergency room visit prediction based on electronic health record data
Zhi Qiao, Ning Sun, Xiang Li, Eryu Xia, Shiwan Zhao, and Yong Qin · 2018
Later among the works it cites.
Mortality prediction in intensive care units (icus) using a deep rule-based fuzzy classifier
Raheleh Davoodi and Mohammad Hassan Moradi · 2018
Later among the works it cites.
A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
Sara Bersche Golas, Takuma Shibahara, Stephen Agboola, Hiroko Otaki, Jumpei Sato, Tatsuya Nakae, Toru Hisamitsu, Go Kojima, Jennifer Felsted, Sujay Kakarmath, et al · 2018
Later among the works it cites.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
Later among the works it cites.
Phenotyping through semi-supervised tensor factorization (psst)
Jette Henderson, Huan He, Bradley A Malin, Joshua C Denny, Abel N Kho, Joydeep Ghosh, and Joyce C Ho · 2018
Later among the works it cites.
Extraction of information related to adverse drug events from electronic health record notes: design of an end-to-end model based on deep learning
Fei Li, Weisong Liu, and Hong Yu · 2018
Later among the works it cites.
Clinical relation extraction toward drug safety surveillance using electronic health record narratives: classical learning versus deep learning
Tsendsuren Munkhdalai, Feifan Liu, and Hong Yu · 2018
Later among the works it cites.
Optimizing autoencoders for learning deep representations from health data
Chongyu Zhou, Jia Yao, and Mehul Motani · 2018
Later among the works it cites.
Deep patient similarity learning for personalized healthcare
Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, and Aidong Zhang · 2018
Later among the works it cites.
Ensemble-based methods to improve de-identification of electronic health record narratives
Youngjun Kim, Paul Heider, and Stéphane Meystre · 2018
Later among the works it cites.
Natural language generation for electronic health records
Scott Lee · 2018
Later among the works it cites.
Deephint: Understanding hiv-1 integration via deep learning with attention
Hailin Hu, An Xiao, Sai Zhang, Yangyang Li, Xuanling Shi, Tao Jiang, Linqi Zhang, Lei Zhang, and Jianyang Zeng · 2018
Later among the works it cites.
An unsupervised learning method for disease classification based on dna methylation signatures
Mohammad Firouzi, Andrei Turinsky, Sanaa Choufani, Michelle T Siu, Rosanna Weksberg, and Michael Brudno · 2018
Later among the works it cites.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Later among the works it cites.
Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2018
Later among the works it cites.
Deepprofile: Deep learning of cancer molecular profiles for precision medicine
Ayse Berceste Dincer, Safiye Celik, Naozumi Hiranuma, and Su-In Lee · 2018
Later among the works it cites.
Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients
Marta Amengual-Gual, Adriana Ulate-Campos, and Tobias Loddenkemper · 2018
Later among the works it cites.
Deep deterministic learning for pattern recognition of different cardiac diseases through the internet of medical things
Uzair Iqbal, Teh Wah, Muhammad Habib ur Rehman, Ghulam Mujtaba, Muhammad Imran, and Muhammad Shoaib · 2018
Later among the works it cites.
A deep learning approach for parkinson’s disease diagnosis from eeg signals
Shu Lih Oh, Yuki Hagiwara, U Raghavendra, Rajamanickam Yuvaraj, N Arunkumar, M Murugappan, and U Rajendra Acharya · 2018
Later among the works it cites.
Arrhythmia detection using deep convolutional neural network with long duration ecg signals
Özal Yıldırım, Paweł Pławiak, Ru-San Tan, and U Rajendra Acharya · 2018
Later among the works it cites.
Natural language processing of social media as screening for suicide risk
Glen Coppersmith, Ryan Leary, Patrick Crutchley, and Alex Fine · 2018
Later among the works it cites.
A comparative analysis of sepsis identification methods in an electronic database
Alistair EW Johnson, Jerome Aboab, Jesse D Raffa, Tom J Pollard, Rodrigo O Deliberato, Leo Anthony Celi, and David J Stone · 2018
Later among the works it cites.
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean · 2019
Closest in time.
A systematic literature review of machine learning in online personal health data
Zhijun Yin, Lina M Sulieman, and Bradley A Malin · 2019
Closest in time.
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
Siqi Liu, Kee Yuan Ngiam, and Mengling Feng · 2019
Closest in time.
Deep learning for healthcare biometrics
Upendra Kumar, Esha Tripathi, Surya Prakash Tripathi, and Kapil Kumar Gupta · 2019
Closest in time.
Deep learning: new computational modelling techniques for genomics
Gökcen Eraslan, Žiga Avsec, Julien Gagneur, and Fabian J Theis · 2019
Closest in time.
Critical care, critical data
Christopher V Cosgriff, Leo Anthony Celi, and David J Stone · 2019
Closest in time.
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomašev, Xavier Glorot, Jack W Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, et al · 2019
Closest in time.
Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis
Christopher W. Seymour, Jason N. Kennedy, Shu Wang, Chung-Chou H. Chang, Corrine F. Elliott, Zhongying Xu, Scott Berry, Gilles Clermont, Gregory Cooper, Hernando Gomez, David T. Huang, John A. Kellum, Qi Mi, Steven M. Opal, Victor Talisa, Tom van der Poll, Shyam Visweswaran, Yoram Vodovotz, Jeremy C. Weiss, Donald M. Yealy, Sachin Yende, and Derek C. Angus · 2019
Closest in time.
New Phenotypes for Sepsis
William A. Knaus and Richard D. Marks · 2019
Closest in time.
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomašev, Xavier Glorot, Jack W Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, et al · 2019
Closest in time.
Automated lung nodule detection and classification based on multiple classifiers voting
Tanzila Saba · 2019
Closest in time.
Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection
Amir Alansary, Ozan Oktay, Yuanwei Li, Loic Le Folgoc, Benjamin Hou, Ghislain Vaillant, Konstantinos Kamnitsas, Athanasios Vlontzos, Ben Glocker, Bernhard Kainz, and Daniel Rueckert · 2019
Closest in time.
Diagnosis of alzheimer’s disease via multi-modality 3d convolutional neural network
Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, Alzheimer’s Disease Neuroimaging Initiative (ADNI, et al · 2019
Closest in time.
Anatomynet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy
Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong Liu, Zhen Qian, Nan Du, Wei Fan, and Xiaohui Xie · 2019
Closest in time.
Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images
Meng-Xiao Li, Su-Qin Yu, Wei Zhang, Hao Zhou, Xun Xu, Tian-Wei Qian, and Yong-Jing Wan · 2019
Closest in time.
Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network
Md Zahangir Alom, Chris Yakopcic, Mst Shamima Nasrin, Tarek M Taha, and Vijayan K Asari · 2019
Closest in time.
Deep attentive features for prostate segmentation in 3d transrectal ultrasound
Yi Wang, Haoran Dou, Xiaowei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, and Dong Ni · 2019
Closest in time.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
Closest in time.
Key challenges for delivering clinical impact with artificial intelligence
Christopher J Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, and Dominic King · 2019
Closest in time.
Disease prediction model based on bilstm and attention mechanism
Yang Yang, Xiangwei Zheng, and Cun Ji · 2019
Closest in time.
An attention based deep learning model of clinical events in the intensive care unit
Deepak A Kaji, John R Zech, Jun S Kim, Samuel K Cho, Neha S Dangayach, Anthony B Costa, and Eric K Oermann · 2019
Closest in time.
Using topic modeling via non-negative matrix factorization to identify relationships between genetic variants and disease phenotypes: A case study of lipoprotein(a) (lpa)
Juan Zhao, QiPing Feng, Patrick Wu, Jeremy L. Warner, Joshua C. Denny, and Wei-Qi Wei · 2019
Closest in time.
Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis
Christopher W Seymour, Jason N Kennedy, Shu Wang, Chung-Chou H Chang, Corrine F Elliott, Zhongying Xu, Scott Berry, Gilles Clermont, Gregory Cooper, Hernando Gomez, et al · 2019
Closest in time.
Feature extraction for phenotyping from semantic and knowledge resources
Wenxin Ning, Stephanie Chan, Andrew Beam, Ming Yu, Alon Geva, Katherine Liao, Mary Mullen, Kenneth D. Mandl, Isaac Kohane, Tianxi Cai, and Sheng Yu · 2019
Closest in time.
Attention-based deep residual learning network for entity relation extraction in chinese emrs
Zhichang Zhang, Tong Zhou, Yu Zhang, and Yali Pang · 2019
Closest in time.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
Closest in time.
Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis
Qigang Li, Keyan Zhao, Carlos D. Bustamante, Xin Ma, and Wing H. Wong · 2019
Closest in time.
circdeep: Deep learning approach for circular rna classification from other long non-coding rna
Mohamed Chaabane, Robert M Williams, Austin T Stephens, and Juw Won Park · 2019
Closest in time.
Deep genomic signature for early metastasis prediction in prostate cancer
Hossein Sharifi-Noghabi, Yang Liu, Nicholas Erho, Raunak Shrestha, Mohammed Alshalalfa, Elai Davicioni, Colin C Collins, and Martin Ester · 2019
Closest in time.
Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models
Mohsin Munir, Shoaib Ahmed Siddiqui, Muhammad Ali Chattha, Andreas Dengel, and Sheraz Ahmed · 2019
Closest in time.
Deep belief network for spectral–spatial classification of hyperspectral remote sensor data
Chenming Li, Yongchang Wang, Xiaoke Zhang, Hongmin Gao, Yao Yang, and Jiawei Wang · 2019
Closest in time.
How precision medicine and screening with big data could increase overdiagnosis
Henrik Vogt, Sara Green, Claus Thorn Ekstrøm, and John Brodersen · 2019
Closest in time.
Predicting need for vasopressors in the intensive care unit using an attention based deep learning model
Gloria Hyunjung Kwak, Lowell Ling, and Pan Hui · 2020
Closest in time.
Gate: Graph-attention augmented temporal neural network for medication recommendation
Chenhao Su, Sheng Gao, and Si Li · 2020
Closest in time.
Interpretable batch irl to extract clinician goals in icu hypotension management
Srivatsan Srinivasan and Finale Doshi-Velez · 2020
Closest in time.
Alternative splicing — Wikipedia, the free encyclopedia, 2019
Wikipedia contributors · 2020
Closest in time.
Monitoring depression trend on twitter during the covid-19 pandemic
Yipeng Zhang, Hanjia Lyu, Yubao Liu, Xiyang Zhang, Yu Wang, and Jiebo Luo · 2020
Closest in time.
Multidisciplinary research priorities for the covid-19 pandemic: a call for action for mental health science
Emily A Holmes, Rory C O’Connor, V Hugh Perry, Irene Tracey, Simon Wessely, Louise Arseneault, Clive Ballard, Helen Christensen, Roxane Cohen Silver, Ian Everall, et al · 2020
Closest in time.