Intensive Outpatient Program Response Among Service Members With Mild Traumatic Brain Injury: Change Between Distinct Post-Concussive Symptom Subgroups.

Objective: Among service members (SMs) with mild traumatic brain injury (mTBI) admitted to an intensive outpatient program (IOP), we identified qualitatively distinct subgroups based on post-concussive symptoms (PCSs) and characterized changes between subgroups from admission to discharge. Further, we examined whether co-morbid posttraumatic stress disorder (PTSD) influenced changes between subgroups.

Design: Quasi-experimental.

Traumatic Brain Injury Classification Variability During the Afghanistan/Iraq Conflicts: Surveillance, Clinical, Research, and Policy Implications.

Objective: Challenges associated with case ascertainment of traumatic brain injuries (TBIs) sustained during the Afghanistan/Iraq military operations have been widespread. This study was designed to examine how the prevalence and severity of TBI among military members who served during the conflicts were impacted when a more precise classification of TBI diagnosis codes was compared with the Department of Defense Standard Surveillance Case-Definition (DoD-Case-Definition).

Setting: Identification of TBI diagnoses in the Department of Defense's Military Health System from October 7, 2001, until December 31, 2019.

NSI and PCL-5 Normative Tables for Active Duty Service Members Affected by Traumatic Brain Injury.

Introduction: Many service members (SMs) have been diagnosed with traumatic brain injury. Currently, military treatment facilities do not have access to established normative tables which can assist clinicians in gauging and comparing patient-reported symptoms. The aim of this study is to provide average scores for both the Neurobehavioral Symptom Inventory (NSI) and Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) for active duty SMs based upon varying demographic groups.

Baseline eHealth Behaviors of Service Members: A Retrospective, Cross-Sectional Analysis of Patient Portal Use Before the Pandemic.

Introduction: The use of electronic health (eHealth) tools has the potential to support the overall health, wellness, fitness status, and ability to deploy worldwide of active duty service members (SMs). Additionally, the Coronavirus Disease 2019 pandemic forced healthcare organizations to quickly convert to virtual care settings to decrease face-to-face interactions and increase access to healthcare using technology. The shift to virtual care and the push to increase use of eHealth tools heightened the need to understand how military members interact with eHealth tools.

Evaluation of Machine Learning Techniques to Predict the Likelihood of Mental Health Conditions Following a First mTBI.

Objective: Limited research has evaluated the utility of machine learning models and longitudinal data from electronic health records (EHR) to forecast mental health outcomes following a traumatic brain injury (TBI). The objective of this study is to assess various data science and machine learning techniques and determine their efficacy in forecasting mental health (MH) conditions among active duty Service Members (SMs) following a first diagnosis of mild traumatic brain injury (mTBI).

Materials And Methods: Patient demographics and encounter metadata of 35,451 active duty SMs who have sustained an initial mTBI, as documented within the EHR, were obtained.

Correlations Between the Neurobehavioral Symptom Inventory and Other Commonly Used Questionnaires for Traumatic Brain Injury.

Objective: To evaluate the correlations between the Neurobehavioral Symptom Inventory (NSI) and other questionnaires commonly administered within military traumatic brain injury clinics.

Setting: Military outpatient traumatic brain injury clinics.

Participants: In total, 15,428 active duty service members who completed 24,162 NSI questionnaires between March 2009 and May 2020.

Risk Factors Associated With the Prescription of Opioids Among Service Members Following a First Mild Traumatic Brain Injury.

Objective: To evaluate factors impacting opioid receipt among active-duty service members (SMs) following a first mild traumatic brain injury (mTBI).

Setting: Active-duty SMs receiving care within the Military Health System.

Participants: In total, 14 757 SMs who have sustained an initial mTBI, as documented within electronic health records (EHRs), between 2016 and 2017.

Burden of Behavioral Health Comorbidities on Outpatient Health Care Utilization by Active Duty Service Members With a First Documented mTBI.

Objective: More than 280,000 Active Duty Service Members (ADSMs) sustained a mild traumatic brain injury (mTBI) between 2000 and 2019 (Q3). Previous studies of veterans have shown higher utilization of outpatient health clinics by veterans diagnosed with mTBI. Additionally, veterans with mTBI and comorbid behavioral health (BH) conditions such as post-traumatic stress disorder, depression, and substance use disorders have significantly higher health care utilization than veterans diagnosed with mTBI alone.

Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation.

Objective: This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices.

Methods: A systematic literature review was conducted following PRISMA guidelines.

A K-Reversible Approach to Model Clinical Trajectories.

A clinical trajectory can be defined as the path followed by patients between an initial heath state S such as being healthy to another state S such as being diagnosed with a specific clinical condition. Being able to identify the common trajectories that a group of patients take can benefit clinicians at identifying the current state of patient and potentially provide early treatment to avoid going towards specific paths. In this paper we present our approach that enables a clinical dataset of patient encounters to be clustered into groups of similarity and run through our algorithm which produces an automaton displaying the most common trajectories taken by patients.

A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process.

Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets.

A Large-Scale Informatics Database to Advance Research and Discovery of the Effects of Mild Traumatic Brain Injury.

Clinical research advances in traumatic brain injury (TBI) and behavioral health have always been restricted by the quantity and quality of the data as well as the difficulty of collecting standardized clinical elements. Those barriers, together with the complexity of evaluating TBI, have resulted in serious challenges for clinicians, researchers, and organizations interested in analyzing the short- and long-term effects of TBI. In an effort to raise awareness about existing and cost-effective ways to collect clinical data within the Department of Defense, this article describes some of the steps taken to quickly build a large-scale informatics database to facilitate collection of standardized clinical data and obtain trends of the longitudinal outcomes of service members diagnosed with mild TBI.

Findings from Structural MR Imaging in Military Traumatic Brain Injury.

Purpose: To describe the initial neuroradiology findings in a cohort of military service members with primarily chronic mild traumatic brain injury (TBI) from blast by using an integrated magnetic resonance (MR) imaging protocol.

Materials And Methods: This study was approved by the Walter Reed National Military Medical Center institutional review board and is compliant with HIPAA guidelines. All participants were military service members or dependents recruited between August 2009 and August 2014.

Five-dimensional ultrasound system for soft tissue visualization.

Purpose: A five-dimensional ultrasound (US) system is proposed as a real-time pipeline involving fusion of 3D B-mode data with the 3D ultrasound elastography (USE) data as well as visualization of these fused data and a real-time update capability over time for each consecutive scan. 3D B-mode data assist in visualizing the anatomy of the target organ, and 3D elastography data adds strain information.

Methods: We investigate the feasibility of such a system and show that an end-to-end real-time system, from acquisition to visualization, can be developed.

Three Scoring Approaches to the Neurobehavioral Symptom Inventory for Measuring Clinical Change in Service Members Receiving Intensive Treatment for Combat-Related mTBI.

Objective: To examine the use of the Neurobehavioral Symptom Inventory to measure clinical changes over time in a population of US service members undergoing treatment of mild traumatic brain injury and comorbid psychological health conditions.

Setting: A 4-week, 8-hour per day, intensive, outpatient, interdisciplinary, comprehensive treatment program at the National Intrepid Center of Excellence in Bethesda, Maryland.

Participants: Three hundred fourteen active-duty service members being treated for combat-related comorbid mild traumatic brain injury and psychological health conditions.

Circulating sex hormones and terminal duct lobular unit involution of the normal breast.

Background: Terminal duct lobular units (TDLU) are the predominant source of breast cancers. Lesser degrees of age-related TDLU involution have been associated with increased breast cancer risk, but factors that influence involution are largely unknown. We assessed whether circulating hormones, implicated in breast cancer risk, are associated with levels of TDLU involution using data from the Susan G.

AUTOMATIC QUANTIFICATION OF TREE-IN-BUD PATTERNS FROM CT SCANS.

In this paper, we present a fully automatic method to quantify Tree-in-Bud (TIB) patterns for respiratory tract infections. The proposed quantification method is based on our previous effort to detect and track TIB patterns with a computer assisted detection (CAD) system [9]. In addition to accurately identifying TIB on CT, quantifying TIB is important for measuring the volume of affected lung as a potantial marker of disease severity.

Quantitative Analysis of TDLUs using Adaptive Morphological Shape Techniques.

Within the complex branching system of the breast, terminal duct lobular units (TDLUs) are the anatomical location where most cancer originates. With aging, TDLUs undergo physiological involution, reflected in a loss of structural components (acini) and a reduction in total number. Data suggest that women undergoing benign breast biopsies that do not show age appropriate involution are at increased risk of developing breast cancer.

Characterizing non-linear dependencies among pairs of clinical variables and imaging data.

Advances in computer-aided diagnosis (CAD) systems have shown the benefits of using computer-based techniques to obtain quantitative image measurements of the extent of a particular disease. Such measurements provide more accurate information that can be used to better study the associations between anatomical changes and clinical findings. Unfortunately, even with the use of quantitative image features, the correlations between anatomical changes and clinical findings are often not apparent and definite conclusions are difficult to reach.

Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans.

This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) Möbius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns.

A graph-theoretic approach for segmentation of PET images.

Segmentation of positron emission tomography (PET) images is an important objective because accurate measurement of signal from radio-tracer activity in a region of interest is critical for disease treatment and diagnosis. In this study, we present the use of a graph based method for providing robust, accurate, and reliable segmentation of functional volumes on PET images from standardized uptake values (SUVs). We validated the success of the segmentation method on different PET phantoms including ground truth CT simulation, and compared it to two well-known threshold based segmentation methods.