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08/10/2023
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance
As data shift or new data become available, updating clinical machine le...
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07/13/2023
Leveraging Factored Action Spaces for Off-Policy Evaluation
Off-policy evaluation (OPE) aims to estimate the benefit of following a ...
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07/10/2023
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise
Noisy training labels can hurt model performance. Most approaches that a...
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05/02/2023
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Many reinforcement learning (RL) applications have combinatorial action ...
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04/17/2023
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose
In time-series forecasting, future target values may be affected by both...
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08/01/2022
Disparate Censorship Undertesting: A Source of Label Bias in Clinical Machine Learning
As machine learning (ML) models gain traction in clinical applications, ...
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08/27/2021
Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure
When patients develop acute respiratory failure, accurately identifying ...
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07/23/2021
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation
Once integrated into clinical care, patient risk stratification models m...
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07/23/2021
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings
Reinforcement learning (RL) can be used to learn treatment policies and ...
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10/27/2020
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
Many existing approaches for estimating feature importance are problemat...
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09/21/2020
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts
While deep learning has shown promise in improving the automated diagnos...
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09/18/2020
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control
People with type 1 diabetes (T1D) lack the ability to produce the insuli...
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07/24/2020
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Standard reinforcement learning (RL) aims to find an optimal policy that...
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06/30/2020
AdaSGD: Bridging the gap between SGD and Adam
In the context of stochastic gradient descent(SGD) and adaptive moment e...
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08/07/2019
Advocacy Learning: Learning through Competition and Class-Conditional Representations
We introduce advocacy learning, a novel supervised training scheme for a...
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06/07/2019
Automatically Evaluating Balance: A Machine Learning Approach
Compared to in-clinic balance training, in-home training is not as effec...
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06/07/2019
Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series
Recurrent neural networks (RNNs) are commonly applied to clinical time-s...
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11/29/2018
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale
In healthcare, patient risk stratification models are often learned usin...
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08/21/2018
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
Recently, researchers have started applying convolutional neural network...
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08/11/2018
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images
Given the wide success of convolutional neural networks (CNNs) applied t...
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06/14/2018
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
In many forecasting applications, it is valuable to predict not only the...
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03/08/2018
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with ...
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03/02/2018
Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment
Longitudinal patient data has the potential to improve clinical risk str...
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11/08/2017
