34 citations · 63 across the 23 of their papers we have counts for
24 papers
A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
Marco Barbero-Mota, Eric V. Strobl, John M. Still +2
We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health record…
Self-supervised learning of imaging and clinical signatures using a multimodal joint-embedding predictive architecture
Thomas Z. Li, Aravind R. Krishnan, Lianrui Zuo +5
The development of multimodal models for pulmonary nodule diagnosis is limited by the scarcity of labeled data and the tendency for these models to overfit on the training distribu…
Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
Lucas W. Remedios, Chloe Cho, Trent M. Schwartz +11
Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morph…
Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record
Joshua W. Betts, John M. Still, Thomas A. Lasko
Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few c…
A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records
Marco Barbero Mota, John M. Still, Jorge L. Gamboa +4
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sour…
No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases
Thomas Z. Li, Kaiwen Xu, Aravind Krishnan +14
Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been pu…
Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence
Thomas A. Lasko, John M. Still, Thomas Z. Li +5
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large enough dataset, it m…
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
Thomas A. Lasko, Eric V. Strobl, William W. Stead
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sit…
Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification
Thomas Z. Li, John M. Still, Kaiwen Xu +11
The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic hea…
Sample-Specific Root Causal Inference with Latent Variables
Eric V. Strobl, Thomas A. Lasko
Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. In prior work, we defined sample-specific root causes of disease using exog…
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
Thomas Z. Li, Kaiwen Xu, Riqiang Gao +5
Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent features computed fro…
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Xin Yu, Qi Yang, Yinchi Zhou +12
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical…
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
Riqiang Gao, Thomas Li, Yucheng Tang +7
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains inc…
Identifying Patient-Specific Root Causes of Disease
Eric V. Strobl, Thomas A. Lasko
Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many…
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
Eric V. Strobl, Thomas A. Lasko
Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initi…
Characterizing Renal Structures with 3D Block Aggregate Transformers
Xin Yu, Yucheng Tang, Yinchi Zhou +10
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology. However, the development and evaluation of t…
Generalizing Clinical Trials with Convex Hulls
Eric V. Strobl, Thomas A. Lasko
Randomized clinical trials eliminate confounding but impose strict exclusion criteria that limit recruitment to a subset of the population. Observational datasets are more inclusiv…
Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
Riqiang Gao, Yucheng Tang, Kaiwen Xu +7
Data from multi-modality provide complementary information in clinical prediction, but missing data in clinical cohorts limits the number of subjects in multi-modal learning contex…
Synthesized Difference in Differences
Eric V. Strobl, Thomas A. Lasko
We consider estimating the conditional average treatment effect for everyone by eliminating confounding and selection bias. Unfortunately, randomized clinical trials (RCTs) elimina…
Semi-supervised Contrastive Learning Using Partial Label Information
Colin B. Hansen, Vishwesh Nath, Diego A. Mesa +3
In semi-supervised learning, information from unlabeled examples is used to improve the model learned from labeled examples. In some learning problems, partial label information ca…
Computational Phenotype Discovery via Probabilistic Independence
Thomas A. Lasko, Diego A. Mesa
Computational Phenotype Discovery research has taken various pragmatic approaches to disentangling phenotypes from the episodic observations in Electronic Health Records. In this w…
Embedding Complexity In the Data Representation Instead of In the Model: A Case Study Using Heterogeneous Medical Data
Jacek M. Bajor, Diego A. Mesa, Travis J. Osterman +1
Electronic Health Records have become popular sources of data for secondary research, but their use is hampered by the amount of effort it takes to overcome the sparsity, irregular…
Opportunities for Mining Radiology Archives for Pediatric Control Images
Camilo Bermudez, Varvara N Probst, Larry T Davis +2
A large database of brain imaging data from healthy, normal controls is useful to describe physiologic and pathologic structural changes at a population scale. In particular, these…
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data
Thomas A. Lasko
The episodic, irregular and asynchronous nature of medical data render them difficult substrates for standard machine learning algorithms. We would like to abstract away this diffi…