SOCR News UMich CCC HnN Retreat 2018 - Revision history

Dinov: /* Outline */

2018-05-18T13:12:30Z

‎Outline

Dinov: /* Program */

2018-05-07T21:22:08Z

‎Program

Dinov at 21:21, 7 May 2018

2018-05-07T21:21:26Z

Dinov: /* Slidedeck */

2018-05-07T21:09:19Z

‎Slidedeck

Dinov: Created page with "== SOCR News & Events: Exploratory, Confirmatory, & Predictive Big Cancer Data Analytics == ==Logistics== * '''Event''': 2018 [https://www.mcancer.org/head-an..."

2018-05-07T21:03:50Z

Created page with "== SOCR News & Events: Exploratory, Confirmatory, & Predictive Big Cancer Data Analytics == ==Logistics== * '''Event''': 2018 [https://www.mcancer.org/head-an..."

New page

== [[SOCR_News | SOCR News & Events]]: Exploratory, Confirmatory, & Predictive Big Cancer Data Analytics ==

==Logistics==
* '''Event''': 2018 [https://www.mcancer.org/head-and-neck-cancer Head and Neck Oncology Program Retreat]
* '''Place/Time''': Friday May 11, 2018, 7:30AM-4:00PM, University of Michigan, Pierpont Commons (East Room), 2101 Bonisteel Blvd, Ann Arbor, MI 48109.
* '''Presenter''': [http://www.umich.edu/~dinov/ Ivo D Dinov]

==Outline==
[[Image:SOCR_Logo_April_2018.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
We explore computational statistics and machine learning methods, specifically for H&N Cancer analytics. Specifically, we developed new methods and tested them on the H&N cancer dataset (n~350). Specifically, we will demonstrate the infrastructure for predicting univariate clinical outcomes (e.g., cancer staging), processing unstructured clinical notes and medication data, and generating machine-learning models of association.

==Slidedeck==
[ Dr. Dinov's slides are available here].

==References==
* [http://www.socr.umich.edu/people/dinov/publications.html SOCR Publications]
* Dinov, ID, 2018. [http://www.springer.com/us/book/9783319723464 Data Science and Predictive Analytics: Biomedical and Health Applications using R], Springer, Computer Science, ISBN 978-3-319-72346-4.
* [http://predictive.space Data Science and Predictive Analytics (DSPA) Website: http://predictive.space]
* [http://socr.umich.edu/html/SOCR_Funding.html SOCR Funding]

<hr>
* SOCR Home page: http://www.socr.umich.edu

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 | 2:30-3:15  ||Head and Neck/Endocrine Clinical Trials Update  Inhibition in HNSCC ||  Frank Worden Shannon Musser
 |-
-| 3:15-3:30 Physical and Cognitive Function, Symptom Distress and Quality of Life in Head and Neck Cancer || Moira Visovatti
+| 3:15-3:30 || Physical and Cognitive Function, Symptom Distress and Quality of Life in Head and Neck Cancer || Moira Visovatti
 |-
 | 3:30-3:45  ||Novel Therapeutics and Emerging Biomarkers in Head & Neck Cancers || Paul Sweicicki
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 ==Outline==

@@ Line 78: / Line 78: @@
 ==Outline==
-We explore computational statistics and machine learning methods, specifically for H&N Cancer analytics. Specifically, we developed new methods and tested them on the H&N cancer dataset (n~350). Specifically, we will demonstrate the infrastructure for predicting univariate clinical outcomes (e.g., cancer staging), processing unstructured clinical notes and medication data, and generating machine-learning models of association.
+We developed computational statistics and machine learning methods for H&N Cancer analytics. Three applications of the new methods on the H&N cancer dataset (n~350) include: (1) predicting univariate clinical outcomes (e.g., cancer staging), (2) processing unstructured clinical notes and medication data, and, (3) generating machine-learning models of association.
 ==Slidedeck==

@@ Line 2: / Line 2: @@
 ==Logistics==
 * '''Event''': 2018 [https://www.mcancer.org/head-and-neck-cancer Head and Neck Oncology Program Retreat]
 * '''Place/Time''': Friday May 11, 2018, 7:30AM-4:00PM, University of Michigan, Pierpont Commons (East Room), 2101 Bonisteel Blvd, Ann Arbor, MI 48109.
 * '''Presenter''': [http://www.umich.edu/~dinov/ Ivo D Dinov]
 ==Outline==
 We explore computational statistics and machine learning methods, specifically for H&N Cancer analytics. Specifically, we developed new methods and tested them on the H&N cancer dataset (n~350). Specifically, we will demonstrate the infrastructure for predicting univariate clinical outcomes (e.g., cancer staging), processing unstructured clinical notes and medication data, and generating machine-learning models of association.
 ==Slidedeck==
 [http://socr.umich.edu/docs/uploads/2018/Dinov_Predictive_Cancer_HnN_Analytics_2018_Retreat.pdf Dr. Dinov's slides are available here].
 ==References==