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

Sleep monitor: A tool for monitoring and categorical scoring of lying position using 3D camera data

We present a software package for analysing body positions of a subject when they are lying or sleeping in their bed. The software is designed to interface to inexpensive sensors, such as the Microsoft Kinect, and is thus suitable for monitoring at the subjects own home, rather than a dedicated sleep lab. The system is invariant to bed clothing and levels of ambient lighting. Analysis time for a single night session is under five minutes, a significant improvement over the 30–60 min analysis time reported in the literature. Keywords: Lying position, Body position, Automated analysi

Toward the Standardization of Biochar Analysis: The COST Action TD1107 Interlaboratory Comparison

Biochar produced by pyrolysis of organic residues is increasingly used for soil amendment and many other applications. However, analytical methods for its physical and chemical characterization are yet far from being specifically adapted, optimized, and standardized. Therefore, COST Action TD1107 conducted an interlaboratory comparison in which 22 laboratories from 12 countries analyzed three different types of biochar for 38 physical–chemical parameters (macro- and microelements, heavy metals, polycyclic aromatic hydrocarbons, pH, electrical conductivity, and specific surface area) with their preferential methods. The data were evaluated in detail using professional interlaboratory testing software. Whereas intralaboratory repeatability was generally good or at least acceptable, interlaboratory reproducibility was mostly not (20% < mean reproducibility standard deviation < 460%). This paper contributes to better comparability of biochar data published already and provides recommendations to improve and harmonize specific methods for biochar analysis in the future.ISSN:0021-8561ISSN:1520-511