Severe sepsis and septic shock are conditions that affect millions of patients and have close to 50% mortality rate. Early identification of at-risk patients significantly improves outcomes. Electronic surveillance tools have been developed to monitor structured Electronic Medical Records and automatically recognize early signs of sepsis. However, many sepsis risk factors (e.g. symptoms and signs of infection) are often captured only in free text clinical notes. In this study, we developed a method for automatic monitoring of nursing notes for signs and symptoms of infection. We utilized a creative approach to automatically generate an annotated dataset. The dataset was used to create a Machine Learning model that achieved an F1-score ranging from 79 to 96%.

Objective The heterogeneity of pediatric sepsis patients suggests the potential benefits of clustering analytics to derive phenotypes with distinct host response patterns that may help guide personalized therapeutics. We evaluate the relative performance of latent class analysis (LCA) and K‐means, 2 commonly used clustering methods toward the derivation of clinically useful pediatric sepsis phenotypes. Methods Data were extracted from anonymized medical records of 6446 pediatric patients that presented to 1 of 6 emergency departments (EDs) between 2013 and 2018 and were thereafter admitted. Using International Classification of Diseases (ICD)‐9 and ICD‐10 discharge codes, 151 patients were identified with a sepsis continuum diagnosis that included septicemia, sepsis, severe sepsis, and septic shock. Using feature sets used in related clustering studies, LCA and K‐means algorithms were used to derive 4 distinct phenotypic pediatric sepsis segmentations. Each segmentation was evaluated for phenotypic homogeneity, separation, and clinical use. Results Using the 2 feature sets, LCA clustering resulted in 2 similar segmentations of 4 clinically distinct phenotypes, while K‐means clustering resulted in segmentations of 3 and 4 phenotypes. All 4 segmentations identified at least 1 high severity phenotype, but LCA‐identified phenotypes reflected superior stratification, high entropy approaching 1 (eg, 0.994) indicating excellent separation between estimated phenotypes, and differential treatment/treatment response, and outcomes that were non‐randomly distributed across phenotypes ( P < 0.001). Conclusion Compared to K‐means, which is commonly used in clustering studies, LCA appears to be a more robust, clinically useful statistical tool in analyzing a heterogeneous pediatric sepsis cohort toward informing targeted therapies. Additional prospective studies are needed to validate clinical utility of predictive models that target derived pediatric sepsis phenotypes in emergency department settings.

ABSTRACT Introduction Although traumatic brain injury (TBI) is known to cause many visual problems, the correlation between the extent of severe visual acuity loss (SVAL) and severity of TBI has not been widely explored. In this retrospective analysis, combined information from Department of Defense (DoD)/Veterans Affairs ocular injury and TBI repositories were used to evaluate the relationship between chronic SVAL, TBI, ocular injuries, and associated ocular sequelae for U.S. service members serving between 2001 and 2015. Materials and Methods The Defense and Veterans Eye Injury and Vision Registry (DVEIVR) is an initiative led by the DoD and Veterans Affairs that consists of clinical and related data for service members serving in theater since 2001. The Defense and Veterans Brain Injury Center (DVBIC) is the DoD’s office for tracking TBI data in the military and maintains data on active-duty service members with a TBI diagnosis since 2000. Longitudinal data from these 2 resources for encounters between February 2001 and October 2015 were analyzed to understand the relation between SVAL, and TBI while adjusting for ocular covariates such as open globe injury (OGI), disorders of the anterior segment and disorders of the posterior segment in a logistic regression model. TBI cases in DVEIVR were identified using DVBIC data and classified according to International Statistical Classification of Diseases criteria established by DVBIC. Head trauma and other open head wounds (OOHW) were also included. SVAL cases in DVEIVR were identified using both International Statistical Classification of Diseases criteria for blindness and low vision as well as visual acuity test data recorded in DVEIVR. Results Data for a total of 25,193 unique patients with 88,996 encounters were recorded in DVEIVR from February, 2001 to November, 2015. Of these, 7,217 TBI and 1,367 low vision cases were identified, with 638 patients experiencing both. In a full logistic model, neither UTBI nor differentiated TBI (DTBI, ie, mild, moderate, severe, penetrating, or unclassified) were significant risk factors for SVAL although ocular injuries (disorders of the anterior segment, disorders of the posterior segment, and OGI) and OOHW were significant. Conclusion Any direct injury to the eye or head risks SVAL but the location and severity will modify that risk. After adjusting for OGIs, OOHW and their sequelae, TBI was found to not be a significant risk factor for SVAL in patients recorded in DVEIVR. Further research is needed to explore whether TBI is associated with more moderate levels of vision acuity loss.

The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS). We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes: Patient Context Vectors. Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network. The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition. Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.

Introduction Ulcerative keratitis (UK), or corneal ulcer, is a sight-threatening and readiness-lowering medical condition that begins with a corneal infiltrative event (CIE). Contact lens (CL) wear poses a particular risk for a CIE and therefore is restricted for most active duty service members (SMs). In this study, we explored a large Department of Defense/Veterans Affairs (DoD/VA) database to estimate the prevalence of UK and CIE and their association with CL wear. Materials and Methods The DoD/VA Defense and Veterans Eye Injury Vision Registry, an initiative of the DoD/VA Vision Center of Excellence, was explored using natural language processing software to search for words and diagnostic codes that might identify cornea injuries and CL wear. The effect of UK and CIE on readiness was explored by evaluating the duration between the first and final visits noted in the database. Results A total of 213 UK cases were identified among the 27,402 SMs for whom data were recorded in Defense and Veterans Eye Injury Vision Registry. The odds ratios of UK and CIE being associated with CL wear were 13.34 and 2.20, respectively. A less specific code (superficial corneal injury) was found to be the most commonly used diagnosis in the database, and the odds ratio of CL wearers having that diagnosis was 2.25. CL-wearing patients with corneal disease also required more clinic encounters than those who did not wear CLs. Conclusions This study supports the current restriction on CL wear among nonpilot active duty SMs and quantifies the significantly enhanced risk of developing corneal ulcers posed by that habit.