5 research outputs found
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data
representations that are more interpretable for clinical tasks. We propose a
framework for training supervised latent Dirichlet allocation that balances two
goals: faithful generative explanations of high-dimensional data and accurate
prediction of associated class labels. Existing approaches fail to balance
these goals by not properly handling a fundamental asymmetry: the intended task
is always predicting labels from data, not data from labels. Our new
prediction-constrained objective trains models that predict labels from heldout
data well while also producing good generative likelihoods and interpretable
topic-word parameters. In a case study on predicting depression medications
from electronic health records, we demonstrate improved recommendations
compared to previous supervised topic models and high- dimensional logistic
regression from words alone.Comment: Accepted poster at NIPS 2017 Workshop on Machine Learning for Health
(https://ml4health.github.io/2017/
Prediction Focused Topic Models for Electronic Health Records
Electronic Health Record (EHR) data can be represented as discrete counts
over a high dimensional set of possible procedures, diagnoses, and medications.
Supervised topic models present an attractive option for incorporating EHR data
as features into a prediction problem: given a patient's record, we estimate a
set of latent factors that are predictive of the response variable. However,
existing methods for supervised topic modeling struggle to balance prediction
quality and coherence of the latent factors. We introduce a novel approach, the
prediction-focused topic model, that uses the supervisory signal to retain only
features that improve, or do not hinder, prediction performance. By removing
features with irrelevant signal, the topic model is able to learn
task-relevant, interpretable topics. We demonstrate on a EHR dataset and a
movie review dataset that compared to existing approaches, prediction-focused
topic models are able to learn much more coherent topics while maintaining
competitive predictions.Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended
Abstract. arXiv admin note: substantial text overlap with arXiv:1910.0549
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
As machine learning systems get widely adopted for high-stake decisions,
quantifying uncertainty over predictions becomes crucial. While modern neural
networks are making remarkable gains in terms of predictive accuracy,
characterizing uncertainty over the parameters of these models is challenging
because of the high dimensionality and complex correlations of the network
parameter space. This paper introduces a novel variational inference framework
for Bayesian neural networks that (1) encodes complex distributions in
high-dimensional parameter space with representations in a low-dimensional
latent space, and (2) performs inference efficiently on the low-dimensional
representations. Across a large array of synthetic and real-world datasets, we
show that our method improves uncertainty characterization and model
generalization when compared with methods that work directly in the parameter
space
Prediction Focused Topic Models via Feature Selection
Supervised topic models are often sought to balance prediction quality and
interpretability. However, when models are (inevitably) misspecified, standard
approaches rarely deliver on both. We introduce a novel approach, the
prediction-focused topic model, that uses the supervisory signal to retain only
vocabulary terms that improve, or at least do not hinder, prediction
performance. By removing terms with irrelevant signal, the topic model is able
to learn task-relevant, coherent topics. We demonstrate on several data sets
that compared to existing approaches, prediction-focused topic models learn
much more coherent topics while maintaining competitive predictions.Comment: AISTATS 2020. arXiv admin note: substantial text overlap with
arXiv:1911.0855
Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions
Deep learning, an area of machine learning, is set to revolutionize patient
care. But it is not yet part of standard of care, especially when it comes to
individual patient care. In fact, it is unclear to what extent data-driven
techniques are being used to support clinical decision making (CDS).
Heretofore, there has not been a review of ways in which research in machine
learning and other types of data-driven techniques can contribute effectively
to clinical care and the types of support they can bring to clinicians. In this
paper, we consider ways in which two data driven domains - machine learning and
data visualizations - can contribute to the next generation of clinical
decision support systems. We review the literature regarding the ways heuristic
knowledge, machine learning, and visualization are - and can be - applied to
three types of CDS. There has been substantial research into the use of
predictive modeling for alerts, however current CDS systems are not utilizing
these methods. Approaches that leverage interactive visualizations and
machine-learning inferences to organize and review patient data are gaining
popularity but are still at the prototype stage and are not yet in use. CDS
systems that could benefit from prescriptive machine learning (e.g., treatment
recommendations for specific patients) have not yet been developed. We discuss
potential reasons for the lack of deployment of data-driven methods in CDS and
directions for future research