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Found 14 papers, 5 papers with code
Multiply-Robust Causal Change Attribution
Comparing two samples of data, we observe a change in the distribution of an outcome variable.
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
In the process of optimization, these weights are automatically tuned to the specific dataset and causal inference algorithm being used.
Improving Generalizability of Protein Sequence Models via Data Augmentations
While protein sequence data is an emerging application domain for machine learning methods, small modifications to protein sequences can result in difficult-to-predict changes to the protein's function.
Debiasing Concept-based Explanations with Causal Analysis
Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for the domain experts.
Discovering Invariances in Healthcare Neural Networks
We also analyze the invariances of BioBERT on clinical notes and discover words that it is invariant to.
Temporal-Clustering Invariance in Irregular Healthcare Time Series
We postulate that fine temporal detail, e. g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data.
Causal Regularization
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable.
GRAM: Graph-based Attention Model for Healthcare Representation Learning
-Interpretation:The representations learned by deep learning methods should align with medical knowledge.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
Ranked #2 on
Disease Trajectory Forecasting
on UK CF trust
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts.
Multi-layer Representation Learning for Medical Concepts
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications.
