ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1705.08498
  4. Cited By
Clinical Intervention Prediction and Understanding using Deep Networks

Clinical Intervention Prediction and Understanding using Deep Networks

23 May 2017
Harini Suresh
Nathan Hunt
Alistair E. W. Johnson
L. A. Celi
Peter Szolovits
Marzyeh Ghassemi
    OOD
ArXivPDFHTML

Papers citing "Clinical Intervention Prediction and Understanding using Deep Networks"

13 / 13 papers shown
Title
Explanation Space: A New Perspective into Time Series Interpretability
Explanation Space: A New Perspective into Time Series Interpretability
Shahbaz Rezaei
Xin Liu
AI4TS
34
1
0
02 Sep 2024
SCAAT: Improving Neural Network Interpretability via Saliency
  Constrained Adaptive Adversarial Training
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training
Rui Xu
Wenkang Qin
Peixiang Huang
Hao Wang
Lin Luo
FAtt
AAML
28
2
0
09 Nov 2023
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency
  Towards Causal Explanations of Probabilistic Forecasts
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts
Chirag Raman
Hayley Hung
Marco Loog
16
3
0
01 Jun 2022
Conditional Generation of Medical Time Series for Extrapolation to
  Underrepresented Populations
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
Simon Bing
Andrea Dittadi
Stefan Bauer
Patrick Schwab
SyDa
20
17
0
20 Jan 2022
Improving Deep Learning Interpretability by Saliency Guided Training
Improving Deep Learning Interpretability by Saliency Guided Training
Aya Abdelsalam Ismail
H. C. Bravo
S. Feizi
FAtt
20
79
0
29 Nov 2021
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for
  Pre-training Debiasing
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
Sindhu C. M. Gowda
Shalmali Joshi
Haoran Zhang
Marzyeh Ghassemi
CML
29
8
0
27 Aug 2021
Temporal Dependencies in Feature Importance for Time Series Predictions
Temporal Dependencies in Feature Importance for Time Series Predictions
Kin Kwan Leung
Clayton Rooke
Jonathan Smith
S. Zuberi
M. Volkovs
OOD
AI4TS
23
24
0
29 Jul 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning
  Models on MIMIC-IV Dataset
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Chuizheng Meng
Loc Trinh
Nan Xu
Yan Liu
24
30
0
12 Feb 2021
Learning the Graphical Structure of Electronic Health Records with Graph
  Convolutional Transformer
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
E. Choi
Zhen Xu
Yujia Li
Michael W. Dusenberry
Gerardo Flores
Yuan Xue
Andrew M. Dai
MedIm
19
238
0
11 Jun 2019
Deep EHR: Chronic Disease Prediction Using Medical Notes
Deep EHR: Chronic Disease Prediction Using Medical Notes
Jingshu Liu
Zachariah Zhang
N. Razavian
11
105
0
15 Aug 2018
Learning Tasks for Multitask Learning: Heterogenous Patient Populations
  in the ICU
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Harini Suresh
Jen J. Gong
John Guttag
23
85
0
07 Jun 2018
Scalable and accurate deep learning for electronic health records
Scalable and accurate deep learning for electronic health records
A. Rajkomar
Eyal Oren
Kai Chen
Andrew M. Dai
Nissan Hajaj
...
A. Butte
M. Howell
Claire Cui
Greg S. Corrado
Jeffrey Dean
OOD
BDL
44
2,107
0
24 Jan 2018
Recurrent Neural Networks for Multivariate Time Series with Missing
  Values
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che
S. Purushotham
Kyunghyun Cho
David Sontag
Yan Liu
AI4TS
210
1,897
0
06 Jun 2016
1