no code implementations • 19 Mar 2024 • Hejie Cui, Zhuocheng Shen, Jieyu Zhang, Hui Shao, Lianhui Qin, Joyce C. Ho, Carl Yang
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction.
no code implementations • 25 Feb 2024 • ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Bowen Jin, May D. Wang, Joyce C. Ho, Carl Yang
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
no code implementations • 19 Feb 2024 • Hejie Cui, Xinyu Fang, ran Xu, Xuan Kan, Joyce C. Ho, Carl Yang
While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied.
no code implementations • 3 Oct 2023 • Jianghong Zhou, Joyce C. Ho, Chen Lin, Eugene Agichtein
Interactive search can provide a better experience by incorporating interaction feedback from the users.
1 code implementation • 12 Jun 2023 • ran Xu, Yue Yu, Joyce C. Ho, Carl Yang
To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only.
no code implementations • 28 Mar 2022 • Gerardo Flores, George H. Chen, Tom Pollard, Joyce C. Ho, Tristan Naumann
A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022.
no code implementations • 3 Sep 2021 • Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Sivasubramanium Bhavani, Joyce C. Ho
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients' history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts.
no code implementations • 22 Aug 2021 • Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Joyce C. Ho
Representation learning on static graph-structured data has shown a significant impact on many real-world applications.
no code implementations • 31 Mar 2021 • Mani Sotoodeh, Li Xiong, Joyce C. Ho
Samples with ground truth labels may not always be available in numerous domains.
1 code implementation • 26 Oct 2020 • Bonggun Shin, Sungsoo Park, JinYeong Bak, Joyce C. Ho
Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process.
no code implementations • 8 Oct 2020 • Ardavan Afshar, Kejing Yin, Sherry Yan, Cheng Qian, Joyce C. Ho, Haesun Park, Jimeng Sun
In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it.
no code implementations • 21 Jun 2020 • Jing Ma, Qiuchen Zhang, Joyce C. Ho, Li Xiong
In this paper, we propose SkeTenSmooth, a novel tensor factorization framework that uses adaptive sampling to compress the tensor in a temporally streaming fashion and preserves the underlying global structure.
no code implementations • 21 Apr 2020 • Payam Karisani, Joyce C. Ho, Eugene Agichtein
Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons.
General Classification Semi-Supervised Text Classification +1
no code implementations • 26 Aug 2019 • Jing Ma, Qiuchen Zhang, Jian Lou, Joyce C. Ho, Li Xiong, Xiaoqian Jiang
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
no code implementations • 15 Aug 2019 • Bonggun Shin, Sungsoo Park, Keunsoo Kang, Joyce C. Ho
Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost.
no code implementations • 8 Aug 2018 • Jette Henderson, Bradley A. Malin, Joyce C. Ho, Joydeep Ghosh
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping.
no code implementations • 22 Jul 2018 • Yubin Park, Joyce C. Ho
We propose PaloBoost, a Stochastic Gradient TreeBoost model that uses novel regularization techniques to guard against overfitting and is robust to parameter settings.