Determination of the electric vehicles driving modes in real life conditions by classification methods

AbstractAssessing the anticoagulant effect of dabigatran may be useful in certain clinical settings. When plasma sampling is not available, serum or urine samples may provide another option for dabigatran determinations.Dabigatran was assessed in patients on treatment under real-life conditions in plasma samples by four clotting time-based assays and in plasma, serum, and urine samples by two chromogenic substrate methods.The concentrations of dabigatran in patients’ plasma samples were not different for the Hemoclot test (106.8±89.4 ng/mL) and the ecarin clotting time (ECT, 109.5±74.5 ng/mL, p=0.58). Activated partial thromboplastin time and prothrombinase-induced clotting time showed low correlations with the other assays. Chromogenic assays measured similar concentrations as Hemoclot and ECT. For both chromogenic assays, the concentrations of dabigatran were about 70% lower in serum than in plasma samples (p<0.0001). The intra-class coefficient (ICC, Bland-Altman analysis) was strong comparing ECT, Hemoclot thrombin inhibitor (HTI) assay, and the two chromogenic assays (r=0.889–0.737). The ICC was low for comparisons of the chromogenic assays of serum vs. plasma values (ICC, 0.15 and 0.66). The ICC for the determination of dabigatran in urine samples by the two chromogenic assays (5641.6±4319.7 and 4730.0±3770.2 ng/mL) was 0.737.ECT, HTI, and chromogenic assays can be used to determine dabigatran in plasma samples from patients under real-life conditions. Chromogenic assays require further improvement to reliably measure dabigatran in serum samples. Dabigatran concentrations in urine samples can also be determined quantitatively.

Download Full-text

Quality of life in HCV patients treated with OBV/PTV/r ± DSV ± RBV under real-life conditions – Data from the German observational study LIFE-C

Zeitschrift für Gastroenterologie ◽

10.1055/s-0037-1612887 ◽

2018 ◽

Vol 56 (01) ◽

pp. E2-E89

Author(s):

P Buggisch ◽

R Link ◽

U Naumann ◽

G Teuber ◽

R Heyne ◽

...

Keyword(s):

Quality Of Life ◽

Observational Study ◽

Real Life ◽

Life Conditions

Download Full-text

Change in Patient-Reported Outcomes in Severe Eosinophilic Asthma Patients Treated With Benralizumab Under Real-life Conditions

Case Medical Research ◽

10.31525/ct1-nct04184284 ◽

2019 ◽

Author(s):

Keyword(s):

Patient Reported Outcomes ◽

Real Life ◽

Eosinophilic Asthma ◽

Severe Eosinophilic Asthma ◽

Life Conditions ◽

Patient Reported ◽

Asthma Patients

Download Full-text

Faculty Opinions recommendation of Screening for latent tuberculosis infection: performance of tuberculin skin test and interferon-γ release assays under real-life conditions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717961853.793464482 ◽

2012 ◽

Author(s):

David Scott ◽

Nicola Gullick

Keyword(s):

Tuberculin Skin Test ◽

Skin Test ◽

Latent Tuberculosis Infection ◽

Real Life ◽

Latent Tuberculosis ◽

Interferon Γ ◽

Tuberculosis Infection ◽

Life Conditions

Download Full-text

New Multi-View Classification Method with Uncertain Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458282 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Bo Liu ◽

Haowen Zhong ◽

Yanshan Xiao

Keyword(s):

Learning Strategy ◽

State Of The Art ◽

Uncertain Data ◽

Real Life ◽

Support Vector ◽

Classification Methods ◽

Complementary Information ◽

Novel Approach ◽

Svm Model ◽

Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

Download Full-text

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy160 ◽

2018 ◽

Vol 26 (3) ◽

pp. 198-210 ◽

Cited By ~ 3

Author(s):

Suat Gonul ◽

Tuncay Namli ◽

Sasja Huisman ◽

Gokce Banu Laleci Erturkmen ◽

Ismail Hakki Toroslu ◽

...

Keyword(s):

Real Life ◽

Personal Data ◽

Just In Time ◽

Intervention Design ◽

Intervention Delivery ◽

Life Conditions ◽

Triggering Conditions ◽

Delivery Strategies ◽

Design Mechanism ◽

Personalized Intervention

AbstractObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.ResultsWe evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.ConclusionWhile the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

Download Full-text