111 research outputs found
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Increasingly large electronic health records (EHRs) provide an opportunity to
algorithmically learn medical knowledge. In one prominent example, a causal
health knowledge graph could learn relationships between diseases and symptoms
and then serve as a diagnostic tool to be refined with additional clinical
input. Prior research has demonstrated the ability to construct such a graph
from over 270,000 emergency department patient visits. In this work, we
describe methods to evaluate a health knowledge graph for robustness. Moving
beyond precision and recall, we analyze for which diseases and for which
patients the graph is most accurate. We identify sample size and unmeasured
confounders as major sources of error in the health knowledge graph. We
introduce a method to leverage non-linear functions in building the causal
graph to better understand existing model assumptions. Finally, to assess model
generalizability, we extend to a larger set of complete patient visits within a
hospital system. We conclude with a discussion on how to robustly extract
medical knowledge from EHRs.Comment: 12 pages, presented at PSB 202
Using Multiple Instance Learning to Build Multimodal Representations
Image-text multimodal representation learning aligns data across modalities
and enables important medical applications, e.g., image classification, visual
grounding, and cross-modal retrieval. In this work, we establish a connection
between multimodal representation learning and multiple instance learning.
Based on this connection, we propose a generic framework for constructing
permutation-invariant score functions with many existing multimodal
representation learning approaches as special cases. Furthermore, we use the
framework to derive a novel contrastive learning approach and demonstrate that
our method achieves state-of-the-art results on a number of downstream tasks
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Purpose: To develop a machine learning model to classify the severity grades
of pulmonary edema on chest radiographs.
Materials and Methods: In this retrospective study, 369,071 chest radiographs
and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women)
patients from the MIMIC-CXR chest radiograph dataset were included. This
dataset was split into patients with and without congestive heart failure
(CHF). Pulmonary edema severity labels from the associated radiology reports
were extracted from patients with CHF as four different ordinal levels: 0, no
edema; 1, vascular congestion; 2, interstitial edema; and 3, alveolar edema.
Deep learning models were developed using two approaches: a semi-supervised
model using a variational autoencoder and a pre-trained supervised learning
model using a dense neural network. Receiver operating characteristic curve
analysis was performed on both models.
Results: The area under the receiver operating characteristic curve (AUC) for
differentiating alveolar edema from no edema was 0.99 for the semi-supervised
model and 0.87 for the pre-trained models. Performance of the algorithm was
inversely related to the difficulty in categorizing milder states of pulmonary
edema (shown as AUCs for semi-supervised model and pre-trained model,
respectively): 2 versus 0, 0.88 and 0.81; 1 versus 0, 0.79 and 0.66; 3 versus
1, 0.93 and 0.82; 2 versus 1, 0.69 and 0.73; and, 3 versus 2, 0.88 and 0.63.
Conclusion: Deep learning models were trained on a large chest radiograph
dataset and could grade the severity of pulmonary edema on chest radiographs
with high performance.Comment: The two first authors contributed equall
Bidirectional Captioning for Clinically Accurate and Interpretable Models
Vision-language pretraining has been shown to produce high-quality visual
encoders which transfer efficiently to downstream computer vision tasks. While
generative language models have gained widespread attention, image captioning
has thus far been mostly overlooked as a form of cross-modal pretraining in
favor of contrastive learning, especially in medical image analysis. In this
paper, we experiment with bidirectional captioning of radiology reports as a
form of pretraining and compare the quality and utility of learned embeddings
with those from contrastive pretraining methods. We optimize a CNN encoder,
transformer decoder architecture named RadTex for the radiology domain. Results
show that not only does captioning pretraining yield visual encoders that are
competitive with contrastive pretraining (CheXpert competition multi-label AUC
of 89.4%), but also that our transformer decoder is capable of generating
clinically relevant reports (captioning macro-F1 score of 0.349 using CheXpert
labeler) and responding to prompts with targeted, interactive outputs.Comment: 12 pages, 7 figures. Code release to follo
Sample-Specific Debiasing for Better Image-Text Models
Self-supervised representation learning on image-text data facilitates
crucial medical applications, such as image classification, visual grounding,
and cross-modal retrieval. One common approach involves contrasting
semantically similar (positive) and dissimilar (negative) pairs of data points.
Drawing negative samples uniformly from the training data set introduces false
negatives, i.e., samples that are treated as dissimilar but belong to the same
class. In healthcare data, the underlying class distribution is nonuniform,
implying that false negatives occur at a highly variable rate. To improve the
quality of learned representations, we develop a novel approach that corrects
for false negatives. Our method can be viewed as a variant of debiased
constrastive learning that uses estimated sample-specific class probabilities.
We provide theoretical analysis of the objective function and demonstrate the
proposed approach on both image and paired image-text data sets. Our
experiments demonstrate empirical advantages of sample-specific debiasing
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
The large amount of time clinicians spend sifting through patient notes and
documenting in electronic health records (EHRs) is a leading cause of clinician
burnout. By proactively and dynamically retrieving relevant notes during the
documentation process, we can reduce the effort required to find relevant
patient history. In this work, we conceptualize the use of EHR audit logs for
machine learning as a source of supervision of note relevance in a specific
clinical context, at a particular point in time. Our evaluation focuses on the
dynamic retrieval in the emergency department, a high acuity setting with
unique patterns of information retrieval and note writing. We show that our
methods can achieve an AUC of 0.963 for predicting which notes will be read in
an individual note writing session. We additionally conduct a user study with
several clinicians and find that our framework can help clinicians retrieve
relevant information more efficiently. Demonstrating that our framework and
methods can perform well in this demanding setting is a promising proof of
concept that they will translate to other clinical settings and data modalities
(e.g., labs, medications, imaging).Comment: To be published in Proceedings of Machine Learning Research Volume
219; accepted to the Machine Learning for Healthcare 2023 conferenc
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