Difference between revisions of "SOCR News 2018 MNORC SOCR HAC Workshop"

From SOCR
Jump to: navigation, search
(Logistics)
 
(18 intermediate revisions by the same user not shown)
Line 1: Line 1:
 
== [[SOCR_News | SOCR News & Events]]: MNORC-IBIC/SOCR/HAC Health Data Analytics Workshop ==
 
== [[SOCR_News | SOCR News & Events]]: MNORC-IBIC/SOCR/HAC Health Data Analytics Workshop ==
 +
 +
This workshop will provide unique hands-on Health Data Analytics train that may be appropriate to students, postdocs, fellows, early career scholars, and junior faculty with interests in novel strategies for interrogating Big heterogeneous, time-varying, incongruent, incomplete, and multi-scale biomedical data. The workshop is divided in two complementary parts. Part one will provide didactic training in data science methods, available computational infrastructure, statistical inference techniques, and data wrangling strategies. The participants will drive all activities in the second part of the workshop where new datasets, case-studies, and scenarios brought up by all attendees will guide the demonstrations of specific predictive health analytics methods. All registered participants are encouraged to bring their laptops for ''try-it-now'' experiences.
 +
 +
* ''Part 1'', introduction to various SOCR capabilities.
 +
* ''Part 2'', ad-hoc biomed and health analytics challenges presented by attendees.
 +
* ''Part 3'', breakout sessions - smaller groups discuss attendees' driven analytical needs.
  
 
==Logistics==
 
==Logistics==
* '''Date''': TBD (Fri Sept 28, 1-5 PM; Thu Oct 4, 2018, 12-4 PM; Fri Oct 12, 2018, 1-5 PM)
+
[[Image:SOCR_Logo_April_2018.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
* '''Place/Time''': TBD, 426 N. Ingalls ([https://maps.studentlife.umich.edu/building/school-of-nursing-building SNB 1250])
+
* '''Date''': Fri Oct 12, 2018
 +
* '''Place/Time''': 1-5 PM, 426 N. Ingalls ([https://maps.studentlife.umich.edu/building/school-of-nursing-building SNB 1250]). Lunch will be served at 12 Noon (all are welcome)
 
* '''Organizers''': [http://mmoc.med.umich.edu/CoreIntegrativeBiostatisticsInformatics.php MNORC-IBIC], [http://www.socr.umich.edu/people/ SOCR Team], [http://hac.nursing.umich.edu/ HAC]
 
* '''Organizers''': [http://mmoc.med.umich.edu/CoreIntegrativeBiostatisticsInformatics.php MNORC-IBIC], [http://www.socr.umich.edu/people/ SOCR Team], [http://hac.nursing.umich.edu/ HAC]
* '''Registration''': (space is limited to 25!) Please use [https://docs.google.com/forms/d/e/1FAIpQLSdeI5H7tC076S20-j-hnCyG4JEoPDeZwUmryLPTj5euzAxxpA/ this link to register for the training workshop]
+
* '''Registration''': (space is limited to 25!) Please use [https://docs.google.com/forms/d/1qwCCGdmTRaAoX6suBn1c1u5Ys9fviyvexgz07hWjtFw this link to register for the training workshop]. If there is sufficient interest, we may offer a live stream via BlueJeans.
 
+
* '''Format''':
==Outline==
+
** Presentations: capabilities, resources, and expertise (6 x 15-min)
 +
** Participant-led challenges, case-studies, template below, (20-30-min)
 +
** Hands-on Consulting, Try-It-Now, apply to new data (120-min)
 +
** Participants should bring laptops, and datasets, to try some of the resources hands-on at the training workshop
 +
* '''Flyer''': [http://wiki.socr.umich.edu/images/2/22/SOCR_Oct12_2018_Workshop_Flyer.pdf Training Event Flyer]
 +
* [https://drive.google.com/drive/folders/1bcFhCF1lDfHSf48lslOlxAjHS-e56egd Collaborative GDrive folder]
 +
* YouTube webcast archive: [https://youtu.be/_VOcmj0PrhQ Video Part 1] and [https://youtu.be/GZBCXcsa80o Video Part 2].
  
 +
==[http://www.socr.umich.edu/people/ Presenters]==
 +
* Ivo Dinov: [http://socr.umich.edu/ SOCR Platform] and [http://dspa.predictive.space/ Data Science and Predictive Analytics]
 +
* Alexandr Kalinin: [http://socr.umich.edu/HTML5/SOCRAT/ SOCRAT, ML analytics]
 +
* Simeone Marino: [https://github.com/SOCR/CBDA Analytics, CBDA], [http://datasifter.org/ DataSifter]
 +
* Nina Zhou: [http://datasifter.org/ DataSifter], Biostats, Analytics
 +
* Syed Husain: [http://socr.umich.edu/HTML5/Dashboard/ Data Dashboard], Viz/DVT, ML/BlueML
 +
* Jerome Choi: Nutrition and Obesity Case-Study (mothers and newborns), collaboration with Amy Rothberg & Nicole Miller
  
 
==Background==
 
==Background==
 
* [http://mmoc.med.umich.edu Michigan Nutrition Obesity Research Center (MNORC)] and the [http://mmoc.med.umich.edu/CoreIntegrativeBiostatisticsInformatics.php Integrative Biostatistics and Informatics Core (IBIC)]
 
* [http://mmoc.med.umich.edu Michigan Nutrition Obesity Research Center (MNORC)] and the [http://mmoc.med.umich.edu/CoreIntegrativeBiostatisticsInformatics.php Integrative Biostatistics and Informatics Core (IBIC)]
* [http://socr.umich.edu SOCR Website]
+
* [http://socr.umich.edu SOCR Website] and [http://hac.nursing.umich.edu/ Health Analytics Collaboratory website]
 
* [http://socr.umich.edu/html/Navigators.html SOCR Navigators]
 
* [http://socr.umich.edu/html/Navigators.html SOCR Navigators]
 
* [[SOCR_Data | SOCR Datasets and Challenging Case-studies]]
 
* [[SOCR_Data | SOCR Datasets and Challenging Case-studies]]
Line 21: Line 41:
 
* Sprint 2018 SOCR Retreat, End-Of-The-Academic-Year, 12-2 PM on Wed,  4/18/18, in [https://maps.studentlife.umich.edu/building/school-of-nursing-building SNB 1250] with [https://drive.google.com/drive/folders/1kIFhuDmeNd3bwf4WdMQqhBMPbNiPFbij SOCR Spring 2018 Retreat Photos] (UMich GDrive).
 
* Sprint 2018 SOCR Retreat, End-Of-The-Academic-Year, 12-2 PM on Wed,  4/18/18, in [https://maps.studentlife.umich.edu/building/school-of-nursing-building SNB 1250] with [https://drive.google.com/drive/folders/1kIFhuDmeNd3bwf4WdMQqhBMPbNiPFbij SOCR Spring 2018 Retreat Photos] (UMich GDrive).
  
[[Image:SOCR_MDP_April_2018_Pic2.png|600px|thumbnail|center| [http://socr.umich.edu/people SOCR Team] ]]
+
==IBIC/SOCR/HAC Services==
 +
* Provide expertise in experimental design and modeling for preclinical, clinical and translational research studies that integrate clinical, molecular, neurobehavioral and other phenotype data.
 +
* Provide guidance on the appropriate data architecture to enable integration and mining of data.
 +
* Provide guidance and training in techniques and technologies to integrate and mine investigator generated or existing data sets. 
 +
* Assist investigators in the development of secure, Health Insurance Portability and Accountability Act (HIPAA)-compliant databases. 
 +
* Develop and promote the use of software tools for data visualization.
 +
* Collaborate with other investigators, projects and centers to develop optimal data handling procedures and data housing systems, provide researcher friendly reports with suggestions for appropriate analytical tools. 
 +
 
 +
==Case-Studies==
 +
 
 +
===Case-Study Template===
 +
Big Data is becoming ubiquitous. To examine complex health conditions, intricate biomedical phenotypes, and causal relations, advanced analytical techniques and powerful computational methods are necessary to ingest, harmonize, process, analyze and visualize large, heterogeneous, multisource, incomplete, multiscale, and incongruent datasets ([https://doi.org/10.1186/s13742-016-0117-6 DOI: 10.1186/s13742-016-0117-6]). This template shows some of the characteristics that need to be provided prior to data interrogation. Each case-study should include the following components:
 +
 
 +
All Training Workshop Participants are encouraged to prepare and [https://drive.google.com/drive/folders/1bcFhCF1lDfHSf48lslOlxAjHS-e56egd submit the the Workshop GDrive partition] a Case-Study that represents a common data, visualization, analytical, methodological, processing, or interpretation challenge encountered in their clinical, basic or translational research. [https://umich.instructure.com/courses/38100/files/folder/Case_Studies Examples of SOCR Case-studies are available on Canvas].
  
 +
* '''Title''': Brief but descriptive case-study title
 +
* '''Overview''': A brief summary of the case-study
 +
* '''Driving Challenges''': List a set of 3-5 questions that have clear healthcare applications that might be addressed, or at least examined by, using the dataset
 +
* '''Meta-data''': Define all data elements, describe the dataset, data dictionary, data format, etc.
 +
* '''Data''': Package (e.g., as ZIP and share on GDrive, M+Box, etc.) the complete dataset. No PHI! The data could represent observational, derived, or simulated data. In general, to justify use of advanced analytics, the case-study should represent a real and interesting phenomena (e.g., include at least 10 variables, one or more time-points and represent 100 + cases/subjects/instances, hopefully, hundreds or thousands of cases)
 +
* '''Provenance''': Include appropriate, references, URLs, PMCIDs, comments, credits, etc. describing the provenance of these data
 +
 +
Examples of many case-studies are available on the [https://umich.instructure.com/courses/38100/files/folder/Case_Studies SMHS Case-Studies Canvas Site].
 +
 +
=== Case-Study 1: Deaths in Guatemala (2009-2016)===
 +
 +
* [https://umich.instructure.com/courses/38100/files/folder/Case_Studies/28_GuatemalaDeaths_CaseStudy Case-study Folder]
 +
* [https://umich.instructure.com/files/8882923/download?download_frd=1 Dataset (ZIP of *.sav and XLSX docs)]
 +
* [https://umich.instructure.com/files/8883243/download?download_frd=1 Rmd source]
 +
* [https://umich.instructure.com/files/8883240/download?download_frd=1 Initial HTML report]
  
 
<hr>
 
<hr>
 +
 
==References==
 
==References==
* This workshop is sponsored in part by [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 NIH Grant P30 DK089503] and NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
+
* This workshop is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8907508&icde=22205754 U54 EB020406], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8882615&icde=22023333 P50 NS091856], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9172096&icde=30598205 P30AG053760], as well as, NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
 
* SOCR Home page: http://www.socr.umich.edu
 
* SOCR Home page: http://www.socr.umich.edu
 
* [https://drive.google.com/drive/folders/1CaJptsqjSudMMv2ma-G_x5T-IMgc9Gzr GDrive]
 
* [https://drive.google.com/drive/folders/1CaJptsqjSudMMv2ma-G_x5T-IMgc9Gzr GDrive]
  
 
{{translate|pageName=http://wiki.socr.umich.edu/index.php/SOCR_News_SOCR_Fall2018_retreat}}
 
{{translate|pageName=http://wiki.socr.umich.edu/index.php/SOCR_News_SOCR_Fall2018_retreat}}

Latest revision as of 18:13, 12 October 2018

SOCR News & Events: MNORC-IBIC/SOCR/HAC Health Data Analytics Workshop

This workshop will provide unique hands-on Health Data Analytics train that may be appropriate to students, postdocs, fellows, early career scholars, and junior faculty with interests in novel strategies for interrogating Big heterogeneous, time-varying, incongruent, incomplete, and multi-scale biomedical data. The workshop is divided in two complementary parts. Part one will provide didactic training in data science methods, available computational infrastructure, statistical inference techniques, and data wrangling strategies. The participants will drive all activities in the second part of the workshop where new datasets, case-studies, and scenarios brought up by all attendees will guide the demonstrations of specific predictive health analytics methods. All registered participants are encouraged to bring their laptops for try-it-now experiences.

  • Part 1, introduction to various SOCR capabilities.
  • Part 2, ad-hoc biomed and health analytics challenges presented by attendees.
  • Part 3, breakout sessions - smaller groups discuss attendees' driven analytical needs.

Logistics

  • Date: Fri Oct 12, 2018
  • Place/Time: 1-5 PM, 426 N. Ingalls (SNB 1250). Lunch will be served at 12 Noon (all are welcome)
  • Organizers: MNORC-IBIC, SOCR Team, HAC
  • Registration: (space is limited to 25!) Please use this link to register for the training workshop. If there is sufficient interest, we may offer a live stream via BlueJeans.
  • Format:
    • Presentations: capabilities, resources, and expertise (6 x 15-min)
    • Participant-led challenges, case-studies, template below, (20-30-min)
    • Hands-on Consulting, Try-It-Now, apply to new data (120-min)
    • Participants should bring laptops, and datasets, to try some of the resources hands-on at the training workshop
  • Flyer: Training Event Flyer
  • Collaborative GDrive folder
  • YouTube webcast archive: Video Part 1 and Video Part 2.

Presenters

Background

IBIC/SOCR/HAC Services

  • Provide expertise in experimental design and modeling for preclinical, clinical and translational research studies that integrate clinical, molecular, neurobehavioral and other phenotype data.
  • Provide guidance on the appropriate data architecture to enable integration and mining of data.
  • Provide guidance and training in techniques and technologies to integrate and mine investigator generated or existing data sets.
  • Assist investigators in the development of secure, Health Insurance Portability and Accountability Act (HIPAA)-compliant databases.
  • Develop and promote the use of software tools for data visualization.
  • Collaborate with other investigators, projects and centers to develop optimal data handling procedures and data housing systems, provide researcher friendly reports with suggestions for appropriate analytical tools.

Case-Studies

Case-Study Template

Big Data is becoming ubiquitous. To examine complex health conditions, intricate biomedical phenotypes, and causal relations, advanced analytical techniques and powerful computational methods are necessary to ingest, harmonize, process, analyze and visualize large, heterogeneous, multisource, incomplete, multiscale, and incongruent datasets (DOI: 10.1186/s13742-016-0117-6). This template shows some of the characteristics that need to be provided prior to data interrogation. Each case-study should include the following components:

All Training Workshop Participants are encouraged to prepare and submit the the Workshop GDrive partition a Case-Study that represents a common data, visualization, analytical, methodological, processing, or interpretation challenge encountered in their clinical, basic or translational research. Examples of SOCR Case-studies are available on Canvas.

  • Title: Brief but descriptive case-study title
  • Overview: A brief summary of the case-study
  • Driving Challenges: List a set of 3-5 questions that have clear healthcare applications that might be addressed, or at least examined by, using the dataset
  • Meta-data: Define all data elements, describe the dataset, data dictionary, data format, etc.
  • Data: Package (e.g., as ZIP and share on GDrive, M+Box, etc.) the complete dataset. No PHI! The data could represent observational, derived, or simulated data. In general, to justify use of advanced analytics, the case-study should represent a real and interesting phenomena (e.g., include at least 10 variables, one or more time-points and represent 100 + cases/subjects/instances, hopefully, hundreds or thousands of cases)
  • Provenance: Include appropriate, references, URLs, PMCIDs, comments, credits, etc. describing the provenance of these data

Examples of many case-studies are available on the SMHS Case-Studies Canvas Site.

Case-Study 1: Deaths in Guatemala (2009-2016)


References



Translate this page:

(default)
Uk flag.gif

Deutsch
De flag.gif

Español
Es flag.gif

Français
Fr flag.gif

Italiano
It flag.gif

Português
Pt flag.gif

日本語
Jp flag.gif

България
Bg flag.gif

الامارات العربية المتحدة
Ae flag.gif

Suomi
Fi flag.gif

इस भाषा में
In flag.gif

Norge
No flag.png

한국어
Kr flag.gif

中文
Cn flag.gif

繁体中文
Cn flag.gif

Русский
Ru flag.gif

Nederlands
Nl flag.gif

Ελληνικά
Gr flag.gif

Hrvatska
Hr flag.gif

Česká republika
Cz flag.gif

Danmark
Dk flag.gif

Polska
Pl flag.png

România
Ro flag.png

Sverige
Se flag.gif