Automating detection of diagnostic error of infectious diseases using machine learning.

Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality. Our objective was to identify cases of emergency department infectious disease misdiagnoses by measuring the deviation between predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24h of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or timely diagnosis. The proposed approach was validated against manual reviews and compared using the Spearman rank correlation. We analyzed 6.5 million ED visits and over 700 million associated clinical features from over one hundred emergency departments. The testing set performances of the infectious disease (Macro F1 = 86.7, AUROC 90.6 to 94.7) and mortality model (Macro F1 = 97.6, AUROC 89.1 to 89.1) were in expected ranges. Human reviews and the proposed automated metric demonstrated positive correlations ranging from 0.231 to 0.358. The proposed approach for diagnostic deviation shows promise as a potential tool for clinicians to find diagnostic errors. Given the vast number of clinical features used in this analysis, further improvements likely need to either take greater account of data structure (what occurs before when) or involve natural language processing. Further work is needed to explain the potential reasons for divergence and to refine and validate the approach for implementation in real-world settings.

Method to derive the infrared complex refractive indices n(λ) and k(λ) for organic solids from KBr pellet absorption measurements.

Obtaining the complex refractive index vectors n(ν~) and k(ν~) allows calculation of the (infrared) reflectance spectrum that is obtained from a solid in any of its many morphological forms. We report an adaptation to the KBr pellet technique using two gravimetric dilutions to derive quantitative n(ν~)/k(ν~) for dozens of powders with greater repeatability. The optical constants of bisphenol A and sucrose are compared to those derived by other methods, particularly for powdered materials. The variability of the k values for bisphenol A was examined by 10 individual measurements, showing an average coefficient of variation for k peak heights of 5.6%. Though no established standards exist, the pellet-derived k peak values of bisphenol A differ by 11% and 31% from their single-angle- and ellipsometry-derived values, respectively. These values provide an initial estimate of the precision and accuracy of complex refractive indices that can be derived using this method. Limitations and advantages of the method are discussed, the salient advantage being a more rapid method to derive n/k for those species that do not readily form crystals or specular pellets.

A Linguistic Analysis Examining the Impact of COVID-19 on Pneumonia Diagnosis and Disease Models.

Written clinical language embodies and reflects the clinician's mental models of disease. Prior to the COVID-19 pandemic, pneumonia was shifting away from concern for healthcare-associated pneumonia and toward recognition of heterogeneity of pathogens and host response. How these models are reflected in clinical language or whether they were impacted by the pandemic has not been studied. We aimed to assess changes in the language used to describe pneumonia following the COVID-19 pandemic.

The Impact of Resilience on Employment Among Post-9/11 Veterans With and Without Military Sexual Trauma Exposure.

INTRODUCTION: Successful employment is a functional outcome of high importance for veterans after military discharge. There is a significant rising concern regarding exposure to military sexual trauma (MST) and related mental health outcomes that can impair functional outcomes, such as employment. Although resilience training is a key component of preparing for military service, to date the impact of resilience on employment outcomes for veterans with exposure to MST has yet to be examined. We sought to examine the relationship between resilience and employment in a national sample of post-9/11 veterans with and without MST exposure. MATERIALS AND METHODS: A national survey was conducted between October 2021 and January 2022 to respond to the 2021 National Defense Authorization Act mandate to identify factors affecting post-9/11 women veteran's unemployment. Of veterans, 1,185 completed the survey. Of these, 565 (47.6%) were post-9/11 veterans. The survey collected data on demographics and employment; MST, adult sexual trauma (AST, outside of military), and childhood sexual trauma (CST) exposure; resilience (Response to Stressful Experiences Scale); Post Traumatic Stress Disorder (PTSD) Checklist (PCL-5); and depression (Patient Health Questionnaire-2). Multivariable logistic regression models identified gender-specific associations of resilience with employment among those exposed and not exposed to MST, adjusting for AST, CST, PTSD, and depression. Significance was set at P < .05. RESULTS: Of 322 women and 243 men post-9/11 veterans, 86.5% were employed. MST exposure (MST[+]) was reported by 31.4% (n = 101) of women and 16.9% (n = 41) of men. MST(+) women veterans were more likely to report CST (35.6% vs. 14.5%; P < .001), AST (68.3% vs. 17.2%; P < .001), and both CST and AST (19.8% vs. 7.2%; P < .001) than MST(-) women. MST(+) men were more likely to report AST (65.9% vs. 7.9%; P < .001), and both CST and AST (14.6% vs. 1.0%; P < .001) than MST(-) men. Levels of self-reported resilience were similar for MST(+) women and men and their MST(-) counterparts (women: 11.1 vs. 11.0; men: 11.5 vs. 12.0). For MST(+) women, each unit increase in resilience was associated with a 36% increase in odds of employment (OR: 1.36, 95% CI, 1.08-1.71); resilience was not associated with increased odds of employment among MST(-) women. Among MST(+) men veterans, each unit increase in resilience was associated with an 83% increase in odds of employment (aOR: 1.83, 95% CI, 1.13-2.98), and like women veterans, resilience was not associated with employment among MST(-) men. CONCLUSIONS: Among MST(+) women and men post-9/11 veterans, higher resilience was associated with increased odds of employment, whereas resilience was not associated with employment in MST(-) veterans. These findings suggest that resiliency during and after military service is a key component for potentially improving long-term outcomes. Improving resilience using evidence-based approaches among post-9/11 veterans exposed to MST may be an important avenue for increasing successful functional outcomes such as employment. Moreover, MST(+) women and men veterans may benefit from trauma-informed care as a substantial proportion of these individuals also report exposure to CST, AST, PTSD, and depression.

A deep learning approach for medication disposition and corresponding attributes extraction.

OBJECTIVE: This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task. METHODS: The dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored. RESULTS: Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively. CONCLUSION: In this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model's performance.

A flexible framework for visualizing and exploring patient misdiagnosis over time.

Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths.

Analysis of a national response to a White House directive for ending veteran suicide.

OBJECTIVE: Analyze responses to a national request for information (RFI) to uncover gaps in policy, practice, and understanding of veteran suicide to inform federal research strategy. DATA SOURCE: An RFI with 21 open-ended questions generated from Presidential Executive Order #1386, administered nationally from July 3 to August 5, 2019. STUDY DESIGN: Semi-structured, open-ended responses analyzed using a collaborative qualitative and text-mining data process. DATA EXTRACTION METHODS: We aligned traditional qualitative methods with natural language processing (NLP) text-mining techniques to analyze 9040 open-ended question responses from 722 respondents to provide results within 3 months. Narrative inquiry and the medical explanatory model guided the data extraction and analytic process. RESULTS: Five major themes were identified: risk factors, risk assessment, prevention and intervention, barriers to care, and data/research. Individuals and organizations mentioned different concepts within the same themes. In responses about risk factors, individuals frequently mentioned generic terms like "illness" while organizations mentioned specific terms like "traumatic brain injury." Organizations and individuals described unique barriers to care and emphasized ways to integrate data and research to improve points of care. Organizations often identified lack of funding as barriers while individuals often identified key moments for prevention such as military transitions and ensuring care providers have military cultural understanding. CONCLUSIONS: This study provides an example of a rapid, adaptive analysis of a large body of qualitative, public response data about veteran suicide to support a federal strategy for an important public health topic. Combining qualitative and text-mining methods allowed a representation of voices and perspectives including the lived experiences of individuals who described stories of military transition, treatments that worked or did not, and the perspective of organizations treating veterans for suicide. The results supported the development of a national strategy to reduce suicide risks for veterans as well as civilians.

Trends in Illness Severity, Hospitalization, and Mortality for Community-Onset Pneumonia at 118 US Veterans Affairs Medical Centers.

BACKGROUND: Deaths from pneumonia were decreasing globally prior to the COVID-19 pandemic, but it is unclear whether this was due to changes in patient populations, illness severity, diagnosis, hospitalization thresholds, or treatment. Using clinical data from the electronic health record among a national cohort of patients initially diagnosed with pneumonia, we examined temporal trends in severity of illness, hospitalization, and short- and long-term deaths. DESIGN: Retrospective cohort PARTICIPANTS: All patients >18 years presenting to emergency departments (EDs) at 118 VA Medical Centers between 1/1/2006 and 12/31/2016 with an initial clinical diagnosis of pneumonia and confirmed by chest imaging report. EXPOSURES: Year of encounter. MAIN MEASURES: Hospitalization and 30-day and 90-day mortality. Illness severity was defined as the probability of each outcome predicted by machine learning predictive models using age, sex, comorbidities, vital signs, and laboratory data from encounters during years 2006-2007, and similar models trained on encounters from years 2015 to 2016. We estimated the changes in hospitalizations and 30-day and 90-day mortality between the first and the last 2 years of the study period accounted for by illness severity using time covariate decompositions with model estimates. RESULTS: Among 196,899 encounters across the study period, hospitalization decreased from 71 to 63%, 30-day mortality 10 to 7%, 90-day mortality 16 to 12%, and 1-year mortality 29 to 24%. Comorbidity risk increased, but illness severity decreased. Decreases in illness severity accounted for 21-31% of the decrease in hospitalizations, and 45-47%, 32-24%, and 17-19% of the decrease in 30-day, 90-day, and 1-year mortality. Findings were similar among underrepresented patients and those with only hospital discharge diagnosis codes. CONCLUSIONS: Outcomes for community-onset pneumonia have improved across the VA healthcare system after accounting for illness severity, despite an increase in cases and comorbidity burden.

Impact of coronavirus disease 2019 (COVID-19) on US Hospitals and Patients, April-July 2020.

OBJECTIVE: The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN), the nation's largest hospital surveillance system, launched a module for collecting hospital coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020. DESIGN: From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near-real-time daily national and state estimates to be computed. RESULTS: During the pandemic's April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July. CONCLUSIONS: The NHSN hospital capacity estimates served as important, near-real-time indicators of the pandemic's magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

A pilot study of total personal exposure to volatile organic compounds among Hispanic female domestic cleaners.

Cleaners have an elevated risk for the development or exacerbation of asthma and other respiratory conditions, possibly due to exposure to cleaning products containing volatile organic compounds (VOCs) leading to inflammation and oxidative stress. This pilot study aimed to quantify total personal exposure to VOCs and to assess biomarkers of inflammation and pulmonary oxidative stress in 15 predominantly Hispanic women working as domestic cleaners in San Antonio, Texas, between November 2019 and July 2020. In partnership with a community organization, Domésticas Unidas, recruited women were invited to attend a training session where they were provided 3M 3500 passive organic vapor monitors (badges) and began a 72-hr sampling period during which they were instructed to wear one badge during the entire period ("AT," for All the Time), a second badge only while they were inside their home ("INS," for INSide), and a third badge only when they were outside their home ("OUT," for OUTside). At the end of the sampling period, women returned the badges and provided blood and exhaled breath condensate (EBC) samples. From the badges, 30 individual VOCs were measured and summed to inform total VOC (TVOC) concentrations, as well as concentrations of the following VOC groups: aromatic hydrocarbons, alkanes, halogenated hydrocarbons, and terpenes. From the blood and EBC samples, concentrations of serum C-reactive protein (CRP) and EBC 8-isoprostane (8-ISP) and pH were quantified. Data analyses included descriptive statistics. The 72-hr average of personal exposure to TVOC was 34.4 ppb and ranged from 9.2 to 219.5 ppb. The most prevalent class of VOC exposures for most women (66.7%) was terpenes, specifically d-limonene. Overall, most women also experienced higher TVOC concentrations while outside their home (86.7%) as compared to inside their home. Serum CRP concentrations ranged from 0.3 to 20.3 mg/dL; 8-ISP concentrations ranged from 9.5 to 44.1 pg/mL; and EBC pH ranged from 7.1 to 8.6. Overall, this pilot study demonstrated personal VOC exposure among Hispanic domestic cleaners, particularly to d-limonene, which may result from the use of scented cleaning products.

Hospital capacities and shortages of healthcare resources among US hospitals during the coronavirus disease 2019 (COVID-19) pandemic, National Healthcare Safety Network (NHSN), March 27-July 14, 2020.

During March 27-July 14, 2020, the Centers for Disease Control and Prevention's National Healthcare Safety Network extended its surveillance to hospital capacities responding to COVID-19 pandemic. The data showed wide variations across hospitals in case burden, bed occupancies, ventilator usage, and healthcare personnel and supply status. These data were used to inform emergency responses.

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care and institutions.

Objective: To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions. Materials and Methods: A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU). Results: The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4). Conclusion: NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

ReHouSED: A novel measurement of Veteran housing stability using natural language processing.

Housing stability is an important determinant of health. The US Department of Veterans Affairs (VA) administers several programs to assist Veterans experiencing unstable housing. Measuring long-term housing stability of Veterans who receive assistance from VA is difficult due to a lack of standardized structured documentation in the Electronic Health Record (EHR). However, the text of clinical notes often contains detailed information about Veterans' housing situations that may be extracted using natural language processing (NLP). We present a novel NLP-based measurement of Veteran housing stability: Relative Housing Stability in Electronic Documentation (ReHouSED). We first develop and evaluate a system for classifying documents containing information about Veterans' housing situations. Next, we aggregate information from multiple documents to derive a patient-level measurement of housing stability. Finally, we demonstrate this method's ability to differentiate between Veterans who are stably and unstably housed. Thus, ReHouSED provides an important methodological framework for the study of long-term housing stability among Veterans receiving housing assistance.

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.

BACKGROUND: Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. OBJECTIVE: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. METHODS: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. RESULTS: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. CONCLUSIONS: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

Computerized Mortality Prediction for Community-acquired Pneumonia at 117 Veterans Affairs Medical Centers.

Rationale: Computerized severity assessment for community-acquired pneumonia could improve consistency and reduce clinician burden. Objectives: To develop and compare 30-day mortality-prediction models using electronic health record data, including a computerized score with all variables from the original Pneumonia Severity Index (PSI) except confusion and pleural effusion ("ePSI score") versus models with additional variables. Methods: Among adults with community-acquired pneumonia presenting to emergency departments at 117 Veterans Affairs Medical Centers between January 1, 2006, and December 31, 2016, we compared an ePSI score with 10 novel models employing logistic regression, spline, and machine learning methods using PSI variables, age, sex and 26 physiologic variables as well as all 69 PSI variables. Models were trained using encounters before January 1, 2015; tested on encounters during and after January 1, 2015; and compared using the areas under the receiver operating characteristic curve, confidence intervals, and patient event rates at a threshold PSI score of 970. Results: Among 297,498 encounters, 7% resulted in death within 30 days. When compared using the ePSI score (confidence interval [CI] for the area under the receiver operating characteristic curve, 0.77-0.78), performance increased with model complexity (CI for the logistic regression PSI model, 0.79-0.80; CI for the boosted decision-tree algorithm machine learning PSI model using the Extreme Gradient Boosting algorithm [mlPSI] with the 19 original PSI factors, 0.83-0.85) and the number of variables (CI for the logistic regression PSI model using all 69 variables, 0.84-085; CI for the mlPSI with all 69 variables, 0.86-0.87). Models limited to age, sex, and physiologic variables also demonstrated high performance (CI for the mlPSI with age, sex, and 26 physiologic factors, 0.84-0.85). At an ePSI score of 970 and a mortality-risk cutoff of <2.7%, the ePSI score identified 31% of all patients as being at "low risk"; the mlPSI with age, sex, and 26 physiologic factors identified 53% of all patients as being at low risk; and the mlPSI with all 69 variables identified 56% of all patients as being at low risk, with similar rates of mortality, hospitalization, and 7-day secondary hospitalization being determined. Conclusions: Computerized versions of the PSI accurately identified patients with pneumonia who were at low risk of death. More complex models classified more patients as being at low risk of death and as having similar adverse outcomes.

Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python.

Despite impressive success of machine learning algorithms in clinical natural language processing (cNLP), rule-based approaches still have a prominent role. In this paper, we introduce medspaCy, an extensible, open-source cNLP library based on spaCy framework that allows flexible integration of rule-based and machine learning-based algorithms adapted to clinical text. MedspaCy includes a variety of components that meet common cNLP needs such as context analysis and mapping to standard terminologies. By utilizing spaCy's clear and easy-to-use conventions, medspaCy enables development of custom pipelines that integrate easily with other spaCy-based modules. Our toolkit includes several core components and facilitates rapid development of pipelines for clinical text.

A clinically applicable approach to continuous prediction of future acute kidney injury.

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2-17 and using acute kidney injury-a common and potentially life-threatening condition18-as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.

Detecting Adverse Drug Events with Rapidly Trained Classification Models.

INTRODUCTION: Identifying occurrences of medication side effects and adverse drug events (ADEs) is an important and challenging task because they are frequently only mentioned in clinical narrative and are not formally reported. METHODS: We developed a natural language processing (NLP) system that aims to identify mentions of symptoms and drugs in clinical notes and label the relationship between the mentions as indications or ADEs. The system leverages an existing word embeddings model with induced word clusters for dimensionality reduction. It employs a conditional random field (CRF) model for named entity recognition (NER) and a random forest model for relation extraction (RE). RESULTS: Final performance of each model was evaluated separately and then combined on a manually annotated evaluation set. The micro-averaged F1 score was 80.9% for NER, 88.1% for RE, and 61.2% for the integrated systems. Outputs from our systems were submitted to the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) competition (Yu et al. in http://bio-nlp.org/index.php/projects/39-nlp-challenges , 2018). System performance was evaluated in three tasks (NER, RE, and complete system) with multiple teams submitting output from their systems for each task. Our RE system placed first in Task 2 of the challenge and our integrated system achieved third place in Task 3. CONCLUSION: Adding to the growing number of publications that utilize NLP to detect occurrences of ADEs, our study illustrates the benefits of employing innovative feature engineering.

Activity of Meropenem-Vaborbactam against Carbapenem-Resistant Enterobacteriaceae in a Murine Model of Pyelonephritis.

The recently approved combination of meropenem and vaborbactam (Vabomere) is highly active against Gram-negative pathogens, especially Klebsiella pneumoniae carbapenemase (KPC)-producing, carbapenem-resistant Enterobacteriaceae We evaluated the efficacy of meropenem-vaborbactam against three clinically relevant isolates in a murine pyelonephritis model. The data indicate that the combination of meropenem and vaborbactam significantly increased bacterial killing compared to that with the untreated controls. These data suggest that this combination may have utility in the treatment of complicated urinary tract infections due to KPC-producing, carbapenem-resistant Enterobacteriaceae.

Morphology controls the thermoelectric power factor of a doped semiconducting polymer.

The electrical performance of doped semiconducting polymers is strongly governed by processing methods and underlying thin-film microstructure. We report on the influence of different doping methods (solution versus vapor) on the thermoelectric power factor (PF) of PBTTT molecularly p-doped with F n TCNQ (n = 2 or 4). The vapor-doped films have more than two orders of magnitude higher electronic conductivity (σ) relative to solution-doped films. On the basis of resonant soft x-ray scattering, vapor-doped samples are shown to have a large orientational correlation length (OCL) (that is, length scale of aligned backbones) that correlates to a high apparent charge carrier mobility (µ). The Seebeck coefficient (α) is largely independent of OCL. This reveals that, unlike σ, leveraging strategies to improve µ have a smaller impact on α. Our best-performing sample with the largest OCL, vapor-doped PBTTT:F4TCNQ thin film, has a σ of 670 S/cm and an α of 42 µV/K, which translates to a large PF of 120 µW m-1 K-2. In addition, despite the unfavorable offset for charge transfer, doping by F2TCNQ also leads to a large PF of 70 µW m-1 K-2, which reveals the potential utility of weak molecular dopants. Overall, our work introduces important general processing guidelines for the continued development of doped semiconducting polymers for thermoelectrics.