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Digital transformation in healthcare
Published in Edward M. Rafalski, Ross M. Mullner, Healthcare Analytics, 2022
What we also realized from this experience and past infectious disease crises [32] that regional coordination with local health departments and other medical centers is a critical part of a successful response. Respectful and collaborative interactions with regional partners, linked to a digital and data transformation approach, could be a foundation for a novel method to improve the public health infrastructure and drive better data.
Exploratory Data Analysis with Unsupervised Machine Learning
Published in Altuna Akalin, Computational Genomics with R, 2020
For the same problem above using the unscaled data and different data transformation strategies, use the ward.d distance in hierarchical clustering and plot multiple heatmaps. You can try to use the pheatmap library or any other library that can plot a heatmap with a dendrogram. Which data-scaling strategy provides more homogeneous clusters with respect to disease types? [Difficulty: Beginner/Intermediate]
Healthcare Data Organization
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
This requires the development of software interfaces. Each destination will have its own format of data representation. Data transformation between multiple data-formats is provided by adapters. Adaptersconvert the information from one format used in an organization or sensor to the standardized format used in health-information-bus and vice versa. This conversion is necessary for the interoperability of the databases and interoperability between sensors and databases. Adapters are also needed to exchange information between two institutions or two heterogeneous units (within the same institutions) using incompatible formats due to hardware provided by different vendors.
Barriers to the Recognition of Geriatric Depression in Residential Care Facilities in Alberta
Published in Issues in Mental Health Nursing, 2020
Data comparison from all data sources was conducted using the integrative data analytic approach (Castro et al., 2010), including data transformation approach and data consolidation matrix (Jang et al., 2008). Qualitative data from the open-ended survey on the barriers and facilitators in the depression assessment process were quantified to facilitate comparison of the major themes and subthemes against survey data on the barriers. Data consolidation matrix involved creating a blended database for the main study constructs (Bazeley, 2006) and combining the results from all data sources to integrate blending of the data for further analysis. Given that the findings were extensive, it was not feasible to include the consolidated matrix in this paper. Instead, the integration of the findings is presented as a narrative in the results section below.
Skew-normal Bayesian spatial heterogeneity panel data models
Published in Journal of Applied Statistics, 2020
Mohadeseh Alsadat Farzammehr, Mohammad Reza Zadkarami, Geoffrey J. McLachlan, Sharon X. Lee
Spatial panel data usually show features like skewness in practice. The most routinely adopted strategy in empirical econometric models is that the response and the explanatory variables are transformed so that classical models that are based on the normality assumption can be applied. Although using a transformation to handle the departure from normality may lead to reasonable empirical results, this work is often inappropriate and restrictive. Frequently, a suitable alternative theoretical model (that can directly handle skewness) can perform better than data transformation; see, for instance, Arellano-Valle et al. [4,5], Allard and Naveau [2], Schmidt et al. [35] and Mahmoudian [28]. Moreover, there are several limitations of using data transformation, including reduced information, no guarantee of joint normality, difficulty in interpreting the transformed variables, and no general transformation (i.e. transforms used for one particular data may not be suitable for a different data). In view of this, we propose new spatial heterogeneity panel models that provide greater flexibility in the distributional assumption for random error components to reduce the impact of the unrealistic normality assumption.
NHDL, a recombinant VL/VH hybrid antibody control for IgG2/4 antibodies
Published in mAbs, 2020
Corinna Lau, Martin Berner McAdam, Grethe Bergseth, Algirdas Grevys, Jack Ansgar Bruun, Judith Krey Ludviksen, Hilde Fure, Terje Espevik, Anders Moen, Jan Terje Andersen, Tom Eirik Mollnes
DSF was performed using a LightCycler® 480 RT-PCR instrument (Roche Diagnostics Norge AS, Oslo, Norway). Sypro® Orange (Sigma-Aldrich, S5692) was mixed (1:1000) with purified antibody (0.1 mg/mL) in 25 µL PBS. Samples were run in triplicates in LightCycler® 480 multi-well plates 96 (Roche, 04729692001). The RT-PCR instrument was programmed to ramp the temperature from 20⁰C to 95⁰C within 30 min, after an initial period of 10 min at 20⁰C. Data were collected every 0.5⁰C using 450 nm excitation and 568 nm emission filters. Data transformation and analysis were performed using the DSF analysis protocol as described by Niesen et al.53 To calculate the Tm values, the lowest temperature value at steady state (before protein starts to unfold) and the temperature value at highest fluorescence intensity (protein is completely unfolded) were used.