Faculty Bibliography

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

The cause of Alzheimer's disease (AD) is poorly understood, so forecasting AD remains a hard task in population health. Failure of clinical trials for AD treatments indicates that AD should be intervened at the earlier, pre-symptomatic stages. Developing an explainable method for predicting AD is critical for providing better treatment targets, better clinical trial recruitment, and better clinical care for the AD patients. In this paper, we present a novel approach for disease (AD) prediction based on Electronic Health Records (EHR) and graph neural network. Our method improves the performance on sparse data which is common in EHR, and obtains state-of-art results in predicting AD 12 to 24 months in advance on real-world EHR data, compared to other baseline results. Our approach also provides an insight into the structural relationship among different diagnosis, Lab values, and procedures from EHR as per graph structures learned by our model

Twitter data has been shown broadly applicable for public health surveillance. Previous public health studies based on Twitter data have largely relied on keyword-matching or topic models for clustering relevant tweets. However, both methods suffer from the short-length of texts and unpredictable noise that naturally occurs in user-generated contexts. In response, we introduce a deep learning approach that uses hashtags as a form of supervision and learns tweet embeddings for extracting informative textual features. In this case study, we address the specific task of estimating state-level obesity from dietary-related textual features. Our approach yields an estimation that strongly correlates the textual features to government data and outperforms the keyword-matching baseline. The results also demonstrate the potential of discovering risk factors using the textual features. This method is general-purpose and can be applied to a wide range of Twitter-based public health studies

Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+ aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~ 1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation)

As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients' EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models

OBJECTIVE:Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION/CONCLUSIONS:We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.