Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Combining Bottom-up and Top-down Generation of Interactive Knowledge Maps for Enterprise Search

Our research project develops an intranet search engine with concept- browsing functionality, where the user is able to navigate the conceptual level in an interactive, automatically generated knowledge map. This knowledge map visualizes tacit, implicit knowledge, extracted from the intranet, as a network of semantic concepts. Inductive and deductive methods are combined; a text ana- lytics engine extracts knowledge structures from data inductively, and the en- terprise ontology provides a backbone structure to the process deductively. In addition to performing conventional keyword search, the user can browse the semantic network of concepts and associations to find documents and data rec- ords. Also, the user can expand and edit the knowledge network directly. As a vision, we propose a knowledge-management system that provides concept- browsing, based on a knowledge warehouse layer on top of a heterogeneous knowledge base with various systems interfaces. Such a concept browser will empower knowledge workers to interact with knowledge structures.

The implementation of DRG-based hospital reimbursement in Switzerland: A population-based perspective

Background Switzerland introduces a DRG (Diagnosis Related Groups) based system for hospital financing in 2012 in order to increase efficiency and transparency of Swiss health care. DRG-based hospital reimbursement is not simultaneously realized in all Swiss cantons and several cantons already implemented DRG-based financing irrespective of the national agenda, a setting that provides an opportunity to compare the situation in different cantons. Effects of introducing DRGs anticipated for providers and insurers are relatively well known but it remains less clear what effects DRGs will have on served populations. The objective of the study is therefore to analyze differences of volume and major quality indicators of care between areas with or without DRG-based hospital reimbursement from a population based perspective. Methods Small area analysis of all hospitalizations in acute care hospitals and of all consultations reimbursed by mandatory basic health insurance for physicians in own practice during 2003-2007. Results The results show fewer hospitalizations and a relocation of resources to outpatient care in areas with DRG reimbursement. Overall burden of disease expressed as per capita DRG cost weights was almost identical between the two types of hospital reimbursement and no distinct temporal differences were detected in this respect. But the results show considerably higher 90-day rehospitalization rates in DRG areas. Conclusion The study provides evidence of both desired and harmful effects related to the implementation of DRGs. Systematic monitoring of outcomes and quality of care are therefore essential elements to maintain in the Swiss health system after DRG's are implemented on a nationwide basis in 2012.

A novel 2D-based approach to the discovery of candidate substrates for the metalloendopeptidase meprin

In the past, protease-substrate finding proved to be rather haphazard and was executed by in vitro cleavage assays using singly selected targets. In the present study, we report the first protease proteomic approach applied to meprin, an astacin-like metalloendopeptidase, to determine physiological substrates in a cell-based system of Madin-Darby canine kidney epithelial cells. A simple 2D IEF/SDS/PAGE-based image analysis procedure was designed to find candidate substrates in conditioned media of Madin-Darby canine kidney cells expressing meprin in zymogen or in active form. The method enabled the discovery of hitherto unknown meprin substrates with shortened (non-trypsin-generated) N- and C-terminally truncated cleavage products in peptide fragments upon LC-MS/MS analysis. Of 22 (17 nonredundant) candidate substrates identified, the proteolytic processing of vinculin, lysyl oxidase, collagen type V and annexin A1 was analysed by means of immunoblotting validation experiments. The classification of substrates into functional groups may propose new functions for meprins in the regulation of cell homeostasis and the extracellular environment, and in innate immunity, respectively.

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. This dissertation can be split into three parts: In the first part, possible fuzzy clustering applications for the Social Semantic Web are investigated. The second part explores promising Social Semantic Web elements for organizational applications,while in the third part the former two parts are brought together and a fuzzy online reputation analysis framework is introduced and evaluated. Theentire PhD thesis is based on literature reviews as well as on argumentative-deductive analyses.The possible applications of Social Semantic Web elements within organizations have been researched using a scenario and an additional case study together with two ancillary case studies—based on qualitative interviews. For the conception and implementation of the online reputation analysis application, a conceptual framework was developed. Employing test installations and prototyping, the essential parts of the framework have been implemented.By following a design sciences research approach, this PhD has created two artifacts: a frameworkand a prototype as proof of concept. Bothartifactshinge on twocoreelements: a (cluster analysis-based) translation of tags used in the Social Web to a computer-understandable fuzzy grassroots ontology for the Semantic Web, and a (Topic Maps-based) knowledge representation system, which facilitates a natural interaction with the fuzzy grassroots ontology. This is beneficial to the identification of unknown but essential Web data that could not be realized through conventional online reputation analysis. Theinherent structure of natural language supports humans not only in communication but also in the perception of the world. Fuzziness is a promising tool for transforming those human perceptions intocomputer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management.

Knowledge representation through graphs

Due to the increasing amount of data, knowledge aggregation, representation and reasoning are highly important for companies. In this paper, knowledge aggregation is presented as the first step. In the sequel, successful knowledge representation, for instance through graphs, enables knowledge-based reasoning. There exist various forms of knowledge representation through graphs; some of which allow to handle uncertainty and imprecision by invoking the technology of fuzzy sets. The paper provides an overview of different types of graphs stressing their relationships and their essential features.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.