Likelihood-based Sensor Calibration using Affine Transformation

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an affine transformation between different systems. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. Both data set and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data

The Unreliable Narrator : A Narrative Analysis of Dr. Jekyll and Mr. Hyde

Uppsatsens syfte är att göra en berättarteknisk analys av olika konflikter i Dr. Jekyll and Mr. Hyde av Robert L. Stevenson. De konflikter som undersöks är bland annat om Dr. Jekyll är en opålitlig berättare och vilka etiska funderingar detta kan väcka kring den klassiska hjälterollen som vanligtvis finns i berättelser. Den narratologiska analysen utgår från Gérard Genettes teorier om berättarteknik. Metoden utgår från Greimas aktantmodell och är grundad på textanalys. Tidigare forskning har undersökt hur identitet delvis kan skapas genom narrativ/berättande. Tidigare forskning har även ifrågasatt Dr. Jekylls roll som hjälte och visat på att han inte är en god person trots att doktorn försöker framställa sig som oskyldig genom sitt narrativ. Resultatet av analysen har visat att det finns perspektivbyten som är avgörande för tolkningen av Dr. Jekylls roll som antingen protagonist eller antagonist. Resultatet har också visat att Dr. Jekyll inte är en pålitlig berättare, då han undanhåller information. Jekylls motsägelsefulla narrativ avslöjar slutligen hans sanna karaktär som antagonist och som offer för sin egen ondska

Barns språk och kommunikation. : En social-semiotisk analys av barns icke-verbala kommunikation i den fria leken.

Syftet med denna undersökning har varit att se över vilka typer av semiotiska resurser som används i den fria leken i förskolor. Vi har valt att lägga vårt fokus på barn mellan 1–3 år då vårt intresse var att se hur barnen kommunicerar och skapar mening med varandra i den fria leken. Studien har byggts upp genom att vi har observerat barn på olika förskolor under den fria leken och fört fältanteckningar som vi sedan har analyserat. Resultaten som vi kom fram till i vår analys var att barn kommunicerar på många olika sätt. Många barn i ålder 1–3 år har ännu inte utvecklat den verbala kommunikationen och väljer att använda sig av den icke-verbala kommunikationen såsom semiotiska resurser. Barn använder sig av många olika typer av teckensystem under den fria leken både mellan barn-barn och barn-pedagog. Barnen kunde bli multimodala och de använde sig av liknande tecken som ljud, gester och ögonkontakt. Med hjälp av dessa tecken så skapade barnen även ett meningsskapande. Slutligen kom vi fram till att barn kan både medvetet och omedvetet kommunicerar med hjälp av olika semiotiska resurser i den fria leken och att använda sig av semiotiska resurser är något som sker vardagligen

Fuzzy system based on two-step cascade genetic optimization strategy for tobacco tar prediction

There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generation methods related to unstable mixtures. In this research were developed algorithms using fusion of artificial intelligence methods to predict tar concentration. Outputs of development are three fuzzy structures optimized with genetic algorithms resulting in genetic algorithm (GA)-FUZZY, GA-adaptive neuro fuzzy inference system (ANFIS), GA-GA-FUZZY algorithms. Proposed algorithms are used for the tar prediction in the cigarette production process. The results of prediction are compared with gas chromatograph (high-performance liquid chromatography (HPLC)) readings

Predicting abnormalities in laboratory values of patients in the intensive care unit using different deep learning models: Comparative study

Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case. Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values. Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets. Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%. Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment

A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study

Background: High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed. Objective: The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture. Methods: We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances. Results: Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present (P=.01; mean recognition accuracy 0.78; SD 0.11), compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling (eg, barcodes, radio frequency labeling) while maintaining a high standard for quality and hygiene with minimal consumption of resources (eg, cost, time, training, and computational power). Conclusions: Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed

A Novel Hybrid Methodology for Anomaly Detection in Time Series

Abstract Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts

Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient 'data fingerprint' of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (Fi