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Tabular data prediction has been employed in medical applications such as patient health risk prediction.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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LightGBM: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Efficient task-specific data valuation for nearest neighbor algorithms
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas J Spanos, and Dawn Song · 2019
Earlier work this paper cites.
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Léon-Charles Tranchevent, Francisco Azuaje, and Jagath C Rajapakse · 2019
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Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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COMPOSE: cross-modal pseudo-siamese network for patient trial matching
Junyi Gao, Cao Xiao, Lucas M Glass, and Jimeng Sun · 2020
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Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
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Unifiedqa: Crossing format boundaries with a single qa system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi · 2020
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2020
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Concare: Personalized clinical feature embedding via capturing the healthcare context
Liantao Ma, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiangtao Wang, Wen Tang, Xinyu Ma, Xin Gao, and Junyi Gao · 2020
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Less is better: Unweighted data subsampling via influence function
Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, and Shao-Lun Huang · 2020
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Tabert: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel · 2020
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VIME: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar · 2020
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DeepEnroll: patient-trial matching with deep embedding and entailment prediction
Xingyao Zhang, Cao Xiao, Lucas M Glass, and Jimeng Sun · 2020
Cited alongside, same era.
Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
Cited alongside, same era.
Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
Cited alongside, same era.
Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, and Dawn Song · 2021
Cited alongside, same era.
Well-tuned simple nets excel on tabular datasets
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
Cited alongside, same era.
Resolving training biases via influence-based data relabeling
Shuming Kong, Yanyan Shen, and Linpeng Huang · 2021
SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C Bayan Bruss, and Tom Goldstein · 2022
Later among the works it cites.
Survtrace: Transformers for survival analysis with competing events
Zifeng Wang and Jimeng Sun · 2022
Later among the works it cites.
TransTab: Learning transferable tabular transformers across tables
Zifeng Wang and Jimeng Sun · 2022
Later among the works it cites.
Zhengyun Zhao, Qiao Jin, Fangyuan Chen, Tuorui Peng, and Sheng Yu · 2022
Later among the works it cites.
Tabcaps: A capsule neural network for tabular data classification with bow routing
Jintai Chen, KuanLun Liao, Yanwen Fang, Danny Chen, and Jian Wu · 2023
Closest in time.
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Cited alongside, same era.
Confident learning: Estimating uncertainty in dataset labels
Curtis Northcutt, Lu Jiang, and Isaac Chuang · 2021
Cited alongside, same era.
Detecting beneficial feature interactions for recommender systems
Yixin Su, Rui Zhang, Sarah Erfani, and Zhenghua Xu · 2021
Cited alongside, same era.
Subtab: Subsetting features of tabular data for self-supervised representation learning
Talip Ucar, Ehsan Hajiramezanali, and Lindsay Edwards · 2021
Cited alongside, same era.
SCARF: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 2022
Cited alongside, same era.
Synthetic clinical trial data while preserving subject-level privacy
Mandis Beigi, Afrah Shafquat, Jason Mezey, and Jacob W Aptekar · 2022
Cited alongside, same era.
Tabtext: a systematic approach to aggregate knowledge across tabular data structures
Dimitris Bertsimas, Kimberly Villalobos Carballo, Yu Ma, Liangyuan Na, Léonard Boussioux, Cynthia Zeng, Luis R Soenksen, and Ignacio Fuentes · 2022
Cited alongside, same era.
Genhpf: General healthcare predictive framework for multi-task multi-source learning
Kyunghoon Hur, Jungwoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, Louis Atallah, and Edward Choi · 2023
Closest in time.
MIMIC-IV (version 2.2), 2023
A. Johnson, L. Bulgarelli, T. Pollard, Horng, Celi S., L. A., and R. Mark · 2023
Closest in time.
Transfer learning with deep tabular models
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum · 2023
Closest in time.
Diwift: Discovering instance-wise influential features for tabular data
Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, and Xiuqiang He · 2023
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Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients
Liantao Ma, Chaohe Zhang, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Yinghao Zhu, Tianlong Wang, Xinyu Ma, Yasha Wang, Wen Tang, et al · 2023
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STUNT: Few-shot tabular learning with self-generated tasks from unlabeled tables
Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, and Jinwoo Shin · 2023
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Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model
Brandon Theodorou, Cao Xiao, and Jimeng Sun · 2023
Closest in time.
PyTrial: A python package for artificial intelligence in drug development, 11 2023
Zifeng Wang, Brandon Theodorou, Tianfan Fu, and Jimeng Sun · 2023
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SPOT: Sequential predictive modeling of clinical trial outcome with meta-learning
Zifeng Wang, Cao Xiao, and Jimeng Sun · 2023
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Data-centric artificial intelligence: A survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu · 2023
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XTab: Cross-table pretraining for tabular transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, and Mahsa Shoaran · 2023
Closest in time.
A comprehensive benchmark for covid-19 predictive modeling using electronic health records in intensive care
Junyi Gao, Yinghao Zhu, Wenqing Wang, Zixiang Wang, Guiying Dong, Wen Tang, Hao Wang, Yasha Wang, Wen Tang, Ewen M Harrison, and Liantao Ma · 2024
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