Fetching the paper…
Reading the bibliography…
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.
ClinicalBERT: Modeling clinical notes and predicting hospital readmission
Kexin Huang, Jaan Altosaar, and Rajesh Ranganath. 2019 · 1904
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
Automated encoding of clinical documents based on natural language processing
Carol Friedman, Lyudmila Shagina, Yves Lussier, and George Hripcsak. 2004 · 2004
Earlier work this paper cites.
MT-clinical BERT: scaling clinical information extraction with multitask learning
Andriy Mulyar and Bridget T McInnes. 2020 · 2004
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Earlier work this paper cites.
Medical coding classification by leveraging inter-code relationships
Yan Yan, Glenn Fung, Jennifer G Dy, and Romer Rosales. 2010 · 2010
Earlier work this paper cites.
Unfolding physiological state: Mortality modelling in intensive care units
Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, Nicole Brimmer, Rohit Joshi, Anna Rumshisky, and Peter Szolovits. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Deep computational phenotyping
Zhengping Che, David Kale, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu. 2015 · 2015
Earlier work this paper cites.
Performance comparison of multi-label learning algorithms on clinical data for chronic diseases
Damien Zufferey, Thomas Hofer, Jean Hennebert, Michael Schumacher, Rolf Ingold, and Stefano Bromuri. 2015 · 2015
Earlier work this paper cites.
Doctor AI: Predicting clinical events via recurrent neural networks
Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2016 · 2016
Cited alongside, same era.
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. 2016 · 2016
Cited alongside, same era.
Hypernetworks
David Ha, Andrew Dai, and Quoc V Le. 2017 · 2017
Cited alongside, same era.
Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis
Benjamin Shickel, Patrick James Tighe, Azra Bihorac, and Parisa Rashidi. 2017 · 2017
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Prediction of patients severity at emergency department using n and ensemble learning arx
Amin Naemi, Marjan Mansourvar, Thomas Schmidt, and Uffe Kock Wiil. 2020 · 2020
Later among the works it cites.
Improving clinical outcome predictions using convolution over medical entities with multimodal learning
Batuhan Bardak and Mehmet Tan. 2021 · 2021
Closest in time.
Medical code assignment with gated convolution and note-code interaction
Shaoxiong Ji, Shirui Pan, and Pekka Marttinen. 2021 · 2021
Closest in time.
Neural natural language processing for unstructured data in electronic health records: a review
Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlalı, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, et al. 2021 · 2021
Closest in time.
Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, and James Henderson. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
Cited alongside, same era.
Training neural networks on high-dimensional data using random projection
Piotr Iwo Wójcik and Marcin Kurdziel. 2019 · 2019
Cited alongside, same era.
ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling
Yuan Zhang, Xi Yang, Julie Ivy, and Min Chi. 2019 · 2019
Cited alongside, same era.
Identification of semantically similar sentences in clinical notes: Iterative intermediate training using multi-task learning
Diwakar Mahajan, Ananya Poddar, Jennifer J Liang, Yen-Ting Lin, John M Prager, Parthasarathy Suryanarayanan, Preethi Raghavan, and Ching-Huei Tsou. 2020 · 2020
Cited alongside, same era.
Closest in time.
Multi-disease prediction using lstm recurrent neural networks
Lu Men, Noyan Ilk, Xinlin Tang, and Yuan Liu. 2021 · 2021
Closest in time.
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Wei Sun, Shaoxiong Ji, Erik Cambria, and Pekka Marttinen. 2021 · 2021
Closest in time.
Clinical outcome prediction from admission notes using self-supervised knowledge integration
Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, and Alexander Loeser. 2021 · 2021
Closest in time.
A survey on multi-task learning
Yu Zhang and Qiang Yang. 2021 · 2021
Closest in time.