6 research outputs found
Accurate detection of sepsis at ED triage using machine learning with clinical natural language processing
Sepsis is a life-threatening condition with organ dysfunction and is a
leading cause of death and critical illness worldwide. Accurate detection of
sepsis during emergency department triage would allow early initiation of lab
analysis, antibiotic administration, and other sepsis treatment protocols. The
purpose of this study was to determine whether EHR data can be extracted and
synthesized with the latest machine learning algorithms (KATE Sepsis) and
clinical natural language processing to produce accurate sepsis models, and
compare KATE Sepsis performance with existing sepsis screening protocols, such
as SIRS and qSOFA. A machine learning model (KATE Sepsis) was developed using
patient encounters with triage data from 16 participating hospitals. KATE
Sepsis, SIRS, standard screening (SIRS with source of infection) and qSOFA were
tested in three settings. Cohort-A was a retrospective analysis on medical
records from a single Site 1. Cohort-B was a prospective analysis of Site 1.
Cohort-C was a retrospective analysis on Site 1 with 15 additional sites.
Across all cohorts, KATE Sepsis demonstrates an AUC of 0.94-0.963 with
73-74.87% TPR and 3.76-7.17% FPR. Standard screening demonstrates an AUC of
0.682-0.726 with 39.39-51.19% TPR and 2.9-6.02% FPR. The qSOFA protocol
demonstrates an AUC of 0.544-0.56, with 10.52-13.18% TPR and 1.22-1.68% FPR.
For severe sepsis, across all cohorts, KATE Sepsis demonstrates an AUC of
0.935-0.972 with 70-82.26% TPR and 4.64-8.62% FPR. For septic shock, across all
cohorts, KATE Sepsis demonstrates an AUC of 0.96-0.981 with 85.71-89.66% TPR
and 4.85-8.8% FPR. SIRS, standard screening, and qSOFA demonstrate low AUC and
TPR for severe sepsis and septic shock detection. KATE Sepsis provided
substantially better sepsis detection performance in triage than commonly used
screening protocols.Comment: 35 pages, 1 figure, 6 tables, 7 supplementary table
TRY plant trait database â enhanced coverage and open access
Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of traitâbased plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for âplant growth formâ. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and traitâenvironmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
The Evolution of Medical Countermeasures for Ebola Virus Disease: Lessons Learned and Next Steps
The Ebola virus disease outbreak that occurred in Western Africa from 2013â2016, and subsequent smaller but increasingly frequent outbreaks of Ebola virus disease in recent years, spurred an unprecedented effort to develop and deploy effective vaccines, therapeutics, and diagnostics. This effort led to the U.S. regulatory approval of a diagnostic test, two vaccines, and two therapeutics for Ebola virus disease indications. Moreover, the establishment of fieldable diagnostic tests improved the speed with which patients can be diagnosed and public health resources mobilized. The United States government has played and continues to play a key role in funding and coordinating these medical countermeasure efforts. Here, we describe the coordinated U.S. government response to develop medical countermeasures for Ebola virus disease and we identify lessons learned that may improve future efforts to develop and deploy effective countermeasures against other filoviruses, such as Sudan virus and Marburg virus
TRY plant trait database - enhanced coverage and open access
10.1111/gcb.14904GLOBAL CHANGE BIOLOGY261119-18