Difference between revisions of "SOCR Intro UMich 2018"

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(Created page with "== SOCR News & Events: Introduction to the Statistics Online Computational Resource (SOCR) == ==Logistics== Image:SOCR_Logo_April_2018.png|150px|thumbnail|r...")
 
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==Logistics==
 
==Logistics==
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* '''Event''': May 2018 [http://nursing.umich.edu/about/departments Health Behavior and Biological Sciences Department Meeting]
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* '''Place/Time''': 3-4 PM, 400 N. Ingalls (Suite #1330, 400 NIB)
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* '''Presenter''': [http://www.umich.edu/~dinov/ Ivo D Dinov]
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==Abstract==
 
[[Image:SOCR_Logo_April_2018.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
 
[[Image:SOCR_Logo_April_2018.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
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* '''Event''': 2018 [http://nursing.umich.edu/academics/global-sexual-health-summer-institute Global Sexual Health Summer Institute]
 
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* '''Series''': ''Big Data & Health Analytics''
 
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* '''Session''': ''Big Healthcare Data: Research Challenges, AI Capabilities, and Educational Opportunities''
 
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* '''Place/Time''': TBD
 
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* '''Instructor''': [http://www.umich.edu/~dinov/ Ivo D Dinov]
 
  
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==Abstract==
 
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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 [http://R-project.org R] and [http://www.rstudio.org/ RStudio graphical user interface]. The [http://myumi.ch/6xNgd unique URI for this session is http://myumi.ch/6xNgd] and it contains links to all resources that will be demonstrated.
 
  
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== Desired learning outcomes==
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== SOCR Services ==
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* Understand the nature of Big Biomedical and Health data archives (challenges, strategies and pitfalls)
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* Consultations on data analytic problems, computational challenges, statistical methods
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* Gain access to biomedical and health data, analytical protocols, software tools, and learning modules
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* Methodological support for research studies, grant applications, and publications,
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* Experiment with some of the available software and computational services
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* Technology support for data management, wrangling, visualization, and interrogation of complex, multi-source, heterogeneous biomedical and health data,  
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* Compare and contrast advanced statistical concepts, grasp model assumptions/limitations and apply them for quantitative analyses in healthcare research.
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* High-performance, large-memory computing services, and  
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* Biomedical informatics, health analytics, and machine-learning classification, prediction and clustering.  
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== Coverage==
 
== Coverage==
 
As time permits, we will cover some of the topics listed below.
 
As time permits, we will cover some of the topics listed below.
  
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===[http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/00_Motivation.html Motivation]===
 
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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.
 
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=== [http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html Simplifying Complex High-dimensional Data] ===
 
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We will demonstrate examples of interrogating multisource, multidimensional and heterogeneous datasets.
 
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=== Model-based vs. Model-free Analytical Methods ===
 
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* [http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/11_Apriory_AssocRuleLearning.html Association Rules Machine-Learning, Head and Neck Cancer Medications Case-Study]
 
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* [http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/12_kMeans_Clustering.html#4_case_study_1:_divorce_and_consequences_on_young_adults Divorce and Consequences on Young Adults Case-Study]
 
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* [http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/12_kMeans_Clustering.html#6_case_study_2:_pediatric_trauma Unsupervised Clustering, Pediatric Trauma Case-Study].
 
  
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=== Learning Modules and Instructional Resources ===
 
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* [http://wiki.socr.umich.edu/index.php/SOCR_Data Datasets] and [https://umich.instructure.com/courses/38100/files/folder/Case_Studies Case-Studies]
 
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* [http://wiki.socr.umich.edu/index.php/SMHS Scientific Methods for Health Sciences EBook]
 
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* [http://www.socr.umich.edu/people/dinov/courses.html Courses, including MOOCs]
 
  
 
=== [http://socr.umich.edu/HTML5/ SOCR Tools]===
 
=== [http://socr.umich.edu/HTML5/ SOCR Tools]===

Revision as of 10:18, 23 April 2018

SOCR News & Events: Introduction to the Statistics Online Computational Resource (SOCR)

Logistics

Abstract

SOCR Resource


SOCR Services

  • Consultations on data analytic problems, computational challenges, statistical methods
  • Methodological support for research studies, grant applications, and publications,
  • Technology support for data management, wrangling, visualization, and interrogation of complex, multi-source, heterogeneous biomedical and health data,
  • High-performance, large-memory computing services, and
  • Biomedical informatics, health analytics, and machine-learning classification, prediction and clustering.


Coverage

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


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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