Difference between revisions of "SOCR News 2018 UMSN SummerInstitute"

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(Simplifying High-dimensional Complex data)
(Coverage)
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== Coverage==
 
== Coverage==
As time permits, we will cover some of hte topics listed below.
+
As time permits, we will cover some of the topics listed below.
  
 
===[http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/00_Motivation.html Motivation]===
 
===[http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/00_Motivation.html Motivation]===
 
We will begin by examining several Big Biomedical case-studies (AD, PD, ALS). Then, we will try hands-on some complex visualization of neurodegenerative imaging, clinical and genetics data. The key point will be to identify the common characteristics of Big (Biomedical and Health) Data and define predictive analytics.
 
We will begin by examining several Big Biomedical case-studies (AD, PD, ALS). Then, we will try hands-on some complex visualization of neurodegenerative imaging, clinical and genetics data. The key point will be to identify the common characteristics of Big (Biomedical and Health) Data and define predictive analytics.
  
=== [http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html Simplifying Complex High-dimensional \Ddata] ===
+
=== [http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html Simplifying Complex High-dimensional Data] ===
 
We will demonstrate examples of interrogating multisource, multidimensional and heterogeneous datasets.
 
We will demonstrate examples of interrogating multisource, multidimensional and heterogeneous datasets.
  

Revision as of 14:44, 20 March 2018

SOCR News & Events: Big Healthcare Data: Research Challenges, AI Capabilities, and Educational Opportunities

Logistics

Abstract

This session will focus on three Big Healthcare Data topics (1) healthcare challenges, (2) analytical capabilities, and (3) educational opportunities. Participants are encouraged to bring their laptops to experiment with some of the interactive content and hands-on demonstrations. JavaScript- and Java-enabled web-browsers would be useful. Participants interested in diving deeper into health analytics should also install R and RStudio graphical user interface. The unique URI for this session is http://myumi.ch/6xNgd and it contains links to all resources that will be demonstrated.

Desired learning outcomes

  • Understand the nature of Big Biomedical and Health data archives (challenges, strategies and pitfalls)
  • Gain access to biomedical and health data, analytical protocols, software tools, and learning modules
  • Experiment with some of the available software and computational services
  • Compare and contrast advanced statistical concepts, grasp model assumptions/limitations and apply them for quantitative analyses in healthcare research.

Coverage

As time permits, we will cover some of the topics listed below.

Motivation

We will begin by examining several Big Biomedical case-studies (AD, PD, ALS). Then, we will try hands-on some complex visualization of neurodegenerative imaging, clinical and genetics data. The key point will be to identify the common characteristics of Big (Biomedical and Health) Data and define predictive analytics.

Simplifying Complex High-dimensional Data

We will demonstrate examples of interrogating multisource, multidimensional and heterogeneous datasets.

Model-based vs. Model-free Analytical Methods

Learning Modules and Instructional Resources

SOCR Tools

The Statistics Online Computational Resource (SOCR) provides tools and services for capturing and interrogating biomedical and healthcare data. These include SOCR Analytical Toolkit (SOCRAT), SOCR Data Dashboard Webapp, SOCR PubMed Navigator, Motion Charts, Randomization, Resampling and Simulation Webapp Violin Chart, Interactive (3D) Bivariate Normal Distribution Calculator webapp, Normal Distribution Calculator, Distributome Probability Calculators, Virtual Experiments, Simulators, Probability Distributome Navigator, Econometrics Webapps, XTK/HTML5 Brain Viewer and BrainBook Painter, DataSifter: Sharing and Obfuscation of Sensitive Data, CBDA: Compressive Big Data Analytics, 2D Interactive Voronoi Tessellation App, SOCR t-SNE Dimensionaltiy Reduction (TensorBoard) UKBB Machine Learning Modules, SOCR GitHub Resources, Apps, Code, Tools, and other Services.

References





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