References
[1]. Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016. Learning to learn by gradient descent by gradient descent. In NIPS.
[2]. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).
[3]. Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, and Jiayu Zhou. 2017.Patient subtyping via time-aware LSTM networks. In KDD.
[4]. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79, 1-2 (2010).
[5]. Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997).
[6]. Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In KDD.
[7]. Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun. 2017. GRAM: graph-based attention model for healthcare representation learning. In KDD. 787–795.
[8]. Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2016. Using recurrent neural network models for early detection of heart failure onset. JAMIA 24, 2 (2016).
[9]. Ignasi Clavera, Anusha Nagabandi, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn. 2018. Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning. (2018).
[10]. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML.
[11]. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A largescale hierarchical image database. In CVPR.
[12]. Li Deng, Jinyu Li, Jui-Ting Huang, Kaisheng Yao, Dong Yu, Frank Seide, Michael L Seltzer, Geoffrey Zweig, Xiaodong He, Jason D Williams, et al. 2013. Recent advances in deep learning for speech research at Microsoft.. In ICASSP, Vol. 26.
[13]. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[14]. Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017).
[15]. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In ICML.
[16]. Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine. 2017. One-Shot Visual Imitation Learning via Meta-Learning. In Conference on Robot Learning. 357–368.
[17]. Jiatao Gu, Yong Wang, Yun Chen, Victor OK Li, and Kyunghyun Cho. 2018. Meta-Learning for Low-Resource Neural Machine Translation. In EMNLP.
[18]. Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 22 (2016).
[19]. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997).
[20]. Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML.
[21]. Peter B Jensen, Lars J Jensen, and Søren Brunak. 2012. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13, 6 (2012).
[22]. Marjan Kerkhof, Daryl Freeman, Rupert Jones, Alison Chisholm, and David B Price. 2015. Predicting frequent COPD exacerbations using primary care data. International journal of chronic obstructive pulmonary disease 10 (2015).
[23]. Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al. 2018. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 5 (2018).
[24]. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).
[25]. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[26]. Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, Vol. 2.
[27]. Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015. Humanlevel concept learning through probabilistic program induction. Science 350, 6266 (2015).
[28]. Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015. Humanlevel concept learning through probabilistic program induction. Science 350, 6266 (2015).
[29]. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015).
[30]. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019. BioBERT: pre-trained biomedical language representation model for biomedical text mining. arXiv preprint arXiv:1901.08746 (2019).
[31]. Sinno Jialin Pan, Qiang Yang, et al. 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2010), 1345–1359.
[32]. Sachin Ravi and Hugo Larochelle. 2016. Optimization as a model for few-shot learning. (2016).
[33]. Samuel Ritter, Jane Wang, Zeb Kurth-Nelson, Siddhant Jayakumar, Charles Blundell, Razvan Pascanu, and Matthew Botvinick. 2018. Been There, Done That: Meta-Learning with Episodic Recall. In ICML.
[34]. Efrat Shadmi, Natalie Flaks-Manov, Moshe Hoshen, Orit Goldman, Haim Bitterman, and Ran D Balicer. 2015. Predicting 30-day readmissions with preadmission electronic health record data. Medical care 53, 3 (2015).
[35]. Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In NIPS.
[36]. Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang, and Jiayu Zhou. 2018. Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models. In KDD.
[37]. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In CVPR.
[38]. Fengyi Tang, Cao Xiao, Fei Wang, and Jiayu Zhou. 2018. Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open (2018).
[39]. Sebastian Thrun and Lorien Pratt. 1998. Learning to learn: Introduction and overview. In Learning to learn.
[40]. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS.
[41]. Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. 2016. Matching networks for one shot learning. In NIPS.
[42]. Fei Wang, Noah Lee, Jianying Hu, Jimeng Sun, and Shahram Ebadollahi. 2012. Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach. In KDD.
Cite this article
Li,Z. (2023). A Comparative Study of Clinical Risk Prediction Using Limited Patient Electronic Health Records. Applied and Computational Engineering,8,584-592.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).
References
[1]. Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016. Learning to learn by gradient descent by gradient descent. In NIPS.
[2]. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).
[3]. Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, and Jiayu Zhou. 2017.Patient subtyping via time-aware LSTM networks. In KDD.
[4]. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79, 1-2 (2010).
[5]. Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997).
[6]. Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In KDD.
[7]. Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun. 2017. GRAM: graph-based attention model for healthcare representation learning. In KDD. 787–795.
[8]. Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2016. Using recurrent neural network models for early detection of heart failure onset. JAMIA 24, 2 (2016).
[9]. Ignasi Clavera, Anusha Nagabandi, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn. 2018. Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning. (2018).
[10]. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML.
[11]. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A largescale hierarchical image database. In CVPR.
[12]. Li Deng, Jinyu Li, Jui-Ting Huang, Kaisheng Yao, Dong Yu, Frank Seide, Michael L Seltzer, Geoffrey Zweig, Xiaodong He, Jason D Williams, et al. 2013. Recent advances in deep learning for speech research at Microsoft.. In ICASSP, Vol. 26.
[13]. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[14]. Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017).
[15]. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In ICML.
[16]. Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine. 2017. One-Shot Visual Imitation Learning via Meta-Learning. In Conference on Robot Learning. 357–368.
[17]. Jiatao Gu, Yong Wang, Yun Chen, Victor OK Li, and Kyunghyun Cho. 2018. Meta-Learning for Low-Resource Neural Machine Translation. In EMNLP.
[18]. Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 22 (2016).
[19]. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997).
[20]. Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML.
[21]. Peter B Jensen, Lars J Jensen, and Søren Brunak. 2012. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13, 6 (2012).
[22]. Marjan Kerkhof, Daryl Freeman, Rupert Jones, Alison Chisholm, and David B Price. 2015. Predicting frequent COPD exacerbations using primary care data. International journal of chronic obstructive pulmonary disease 10 (2015).
[23]. Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al. 2018. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 5 (2018).
[24]. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).
[25]. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[26]. Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, Vol. 2.
[27]. Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015. Humanlevel concept learning through probabilistic program induction. Science 350, 6266 (2015).
[28]. Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015. Humanlevel concept learning through probabilistic program induction. Science 350, 6266 (2015).
[29]. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015).
[30]. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019. BioBERT: pre-trained biomedical language representation model for biomedical text mining. arXiv preprint arXiv:1901.08746 (2019).
[31]. Sinno Jialin Pan, Qiang Yang, et al. 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2010), 1345–1359.
[32]. Sachin Ravi and Hugo Larochelle. 2016. Optimization as a model for few-shot learning. (2016).
[33]. Samuel Ritter, Jane Wang, Zeb Kurth-Nelson, Siddhant Jayakumar, Charles Blundell, Razvan Pascanu, and Matthew Botvinick. 2018. Been There, Done That: Meta-Learning with Episodic Recall. In ICML.
[34]. Efrat Shadmi, Natalie Flaks-Manov, Moshe Hoshen, Orit Goldman, Haim Bitterman, and Ran D Balicer. 2015. Predicting 30-day readmissions with preadmission electronic health record data. Medical care 53, 3 (2015).
[35]. Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In NIPS.
[36]. Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang, and Jiayu Zhou. 2018. Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models. In KDD.
[37]. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In CVPR.
[38]. Fengyi Tang, Cao Xiao, Fei Wang, and Jiayu Zhou. 2018. Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open (2018).
[39]. Sebastian Thrun and Lorien Pratt. 1998. Learning to learn: Introduction and overview. In Learning to learn.
[40]. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS.
[41]. Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. 2016. Matching networks for one shot learning. In NIPS.
[42]. Fei Wang, Noah Lee, Jianying Hu, Jimeng Sun, and Shahram Ebadollahi. 2012. Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach. In KDD.