Interactive Simulation of Nonpharmaceutical Interventions of Airborne Disease Transmission in Office Settings.

The COVID-19 pandemic has caused major disruptions to workplace safety and productivity. A browser-based interactive disease transmission simulation was developed to enable managers and individuals (agents) to optimize safe office work activities during pandemic conditions. The application provides a user interface to evaluate the impact of non-pharmaceutical interventions (NPIs) policies on airborne disease exposure based on agents' meeting patterns and room properties.

Erratum: On the role of artificial intelligence in medical imaging of COVID-19.

[This corrects the article DOI: 10.1016/j.patter.

On the role of artificial intelligence in medical imaging of COVID-19.

Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance.

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist's dictation audio and eye gaze coordinates data.

An End-to-End Deep Learning Pipeline for Emphysema Quantification Using Multi-label Learning.

We propose and validate an end-to-end deep learning pipeline employing multi-label learning as a tool for creating differential diagnoses of lung pathology as well as quantifying the extent and distribution of emphysema in chest CT images. The proposed pipeline first employs deep learning based volumetric lung segmentation using a 3D CNN to extract the entire lung out of CT images. Then, a multi-label learning model is exploited for the classification creation differential diagnoses for emphysema and then used to correlate with the emphysema diagnosed by radiologists.

Improving the Path from Diagnoses to Documentation: A Cognitive Review Tool for Clinical Notes and Administrative Records.

EMR systems are intended to improve patient-centered care management and hospital administrative processing. However, the information stored in EMRs can be disorganized, incomplete, or inconsistent, creating problems at the patient and system level. We present a technology that reconciles inconsistencies between clinical diagnoses and administrative records by analyzing free-text notes, problem lists and recorded diagnoses in real time.

Metric hashing forests.

In this paper, we propose metric Hashing Forests (mHF) which is a supervised variant of random forests tailored for the task of nearest neighbor retrieval through hashing. This is achieved by training independent hashing trees that parse and encode the feature space such that local class neighborhoods are preserved and encoded with similar compact binary codes. At the level of each internal node, locality preserving projections are employed to project data to a latent subspace, where separability between dissimilar points is enhanced.

Mining echocardiography workflows for disease discriminative patterns.

Objective: To provide quick diagnostic insights to medical practitioners into echocardiograms by only analyzing the echocardiogram workflows (defined as the sequence of modalities examined).

Methods: We define a dictionary of workflows, called subflows, which are commonly encountered in echocardiography workflows but are mutually exclusive. We represent each workflow as a mixture of dictionary subflows and learn discriminative models for various cardiac diseases using Support Vector Machines.

AALIM: a cardiac clinical decision support system powered by advanced multi-modal analytics.

Modern Electronic Medical Record (EMR) systems often integrate large amounts of data from multiple disparate sources. To do so, EMR systems must align the data to create consistency between these sources. The data should also be presented in a manner that allows a clinician to quickly understand the complete condition and history of a patient's health.

Cardiac disease recognition in echocardiograms using spatio-temporal statistical models.

In this paper we present a method of automatic disease recognition by using statistical spatio-temporal disease models in cardiac echo videos. Starting from echo videos of known viewpoints as training data, we form a statistical model of shape and motion information within a cardiac cycle for each disease. Specifically, an active shape model (ASM) is used to model shape and texture information in an echo frame.

Closed-form Jensen-Renyi divergence for mixture of Gaussians and applications to group-wise shape registration.

In this paper, we propose a generalized group-wise non-rigid registration strategy for multiple unlabeled point-sets of unequal cardinality, with no bias toward any of the given point-sets. To quantify the divergence between the probability distributions--specifically Mixture of Gaussians--estimated from the given point sets, we use a recently developed information-theoretic measure called Jensen-Renyi (JR) divergence. We evaluate a closed-form JR divergence between multiple probabilistic representations for the general case where the mixture models differ in variance and the number of components.

Shape-based matching of ECG recordings.

An electrocardiogram (ECG) is an important and commonly used diagnostic aid in cardiovascular disease diagnosis. Physicians routinely perform diagnosis by a simple visual examination of ECG waveform shapes. In this paper, we address the problem of shape-based retrieval of ECG recordings, both digital and scanned from paper, to infer similarity in diagnosed diseases.