1,720,988 research outputs found
Gradual Fine-Tuning for accurate Blood Glucose Level Prediction
No full textBackground and Aims
For individuals with Type 1 diabetes (T1D) is of eminence importance to avoid hypo- and hyperglycemic events. The availability of long glucose time-series along with powerful AI methods allowed the development of glucose prediction algorithms. Nonetheless open issues remain such as prediction time-delays, amount of history needed, and how heterogeneous and sparse diabetes information affect the performance.
Method
In this study, we utilized data from 100 individuals with T1D provided by the Juvenile Diabetes Research Foundation. The dataset provides pump settings, sensor outputs (e.g. insulin-rates, continuous glucose monitoring- CGM) and conceptual information such as age, years of diabetes. To mitigate the adverse impact of large inter-patient variability, we propose a training scheme based on gradual fine-tuning. Initially, the novel AI-model is trained on all data and subsequently fine-tuned over groups with shared characteristics to individual patient-level. The individuals with T1D are assigned to groups based on similarity measures defined using glucose variability indices. For each individual, an ensemble of five dedicated sequence-to-sequence LSTM networks is used. The ensemble uses CGM data, bolus dose and meal intake as input and outputs blood glucose predictions 30 min ahead in time.
Results
As shown in Table 1 the root-mean-square-error (RMSE), mean-average-error (MAE), and time-lag used as performance measures for the various training schemes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Autonomous robotic system for active debris removal: requirements, state-of-the-art and concept architecture of the rendezvous and capture (RVC) control system
No full text
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A dual mode adaptive basal-bolus advisor based on reinforcement learning
Full text linkSelf-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients\u27 glucose level on the previous day. The ABBA is based on reinforcement learning (RL), a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimisation and achieve glucose control - independently of the type of glucose monitoring technology.9 pages, 8 figures, accepted by Journal of Biomedical and Health Informatics in December 201
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