Given the frequent use of MIMIC for research, references to some of these studies is recommended

[tompollard]

In this paper, the authors report on the MIMIC-III database, providing characteristics of its generation and content. As MIMIC has served as an important and frequently used research database, the authors' ongoing work is appreciated and of great value to the community. I do have some suggestions however regarding the paper and the database.

Page 2 - -- Given the frequent use of MIMIC for research, references to some of these studies is recommended in the background section to convey the value of this dataset

[tompollard]

MIMIC has been used as a basis for coursework in numerous educational institutions, for example in medical analytics courses at Stanford University, Massachusetts Institute of Technology, Georgia Institute of Technology, and Columbia University, amongst others. MIMIC has also provided the data that underpins a broad range of research studies, which have explored topics such as machine learning approaches for prediction of patient outcomes, clinical implications of blood pressure monitoring techniques, and semantic analysis of unstructured patient notes.

A series of 'datathons' have been held alongside development of the MIMIC database. These events assemble caregivers, data scientists, and those with domain-specific knowledge with the aim of creating ideas and producing clinically relevant, reproducible research \cite{cite7}. In parallel the events introduce new researchers to MIMIC and provide a platform for continuous review and development of code and research.

Documentation for the MIMIC database is available online \cite{cite-mimic-website}. The content is under continuous development and includes a list of studies that have been carried out using MIMIC. The website includes functionality that enables the research community to directly submit updates and improvements via Github "Pull requests".

Refs:

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724452/
• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3609896/
• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587060/
• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147606/

Course examples:

% - CSE8803 Big Data Analytics for Healthcare, Spring 2016, at Georgia Tech, taught by Jimeng Sun. http://www.sunlab.org/teaching/cse8803/
% - BIOMEDIN 215 DATA DRIVEN MEDICINE, Fall semester (several years now), Stanford, taught by Nigam Shah. http://shahlab.stanford.edu/biomedin215
% - Xiaopeng Zhao is teaching a course at University of Tennessee, Knoxville
% Columbia course: computational methods in biomedical informatics (G4002)
% taught in the columbia university biomedical informatics department
% Noemie Elhadad is the professor.
% from https://www.dbmi.columbia.edu/for-current-staff-students/courses/ (spring term courses):
% BINF G4002 Methods I

[tompollard]

• Joydeep Ghosh, University of Texas at Austin. Fall 2015, approx. 26 students. EE 381V: (ADVANCED DATA MINING)
  Theme: Big Data Analytics for Healthcare (BDAH)
  http://hercules.ece.utexas.edu/ghosh/bdah-f15.htm
• Peter Szolovits, (Tristan Nauman):
  6.872J Biomedical Computing

[rgmark]

Looks good Tom. I marked one typo in the first line (5th word should go).
Roger

Roger G. Mark, M.D., Ph.D.
Professor of Health Sciences and Technology
and of Electrical Engineering
Room E25-505
MIT
Cambridge, MA 02139
Tel 617-253-7818
Fax 617-258-7859

On 4/1/2016 4:23 PM, Tom Pollard wrote:

MIMIC has been used /to/ as a basis for coursework in numerous
educational institutions, for example in medical analytics courses at
Stanford University, Massachusetts Institute of Technology, Georgia
Institute of Technology, and Columbia University, amongst others.
MIMIC has also provided the data that underpins a broad range of
research studies, which have explored topics such as machine learning
approaches for prediction of patient outcomes, clinical implications
of blood pressure monitoring techniques, and semantic analysis of
unstructured patient notes.

A series of 'datathons' have been held alongside development of the
MIMIC database. These events assemble caregivers, data scientists, and
those with domain-specific knowledge with the aim of creating ideas
and producing clinically relevant, reproducible research \cite{cite7}.
In parallel the events introduce new researchers to MIMIC and provide
a platform for continuous review and development of code and research.

Documentation for the MIMIC database is available online
\cite{cite-mimic-website}. The content is under continuous development
and includes a list of studies that have been carried out using MIMIC.
The website includes functionality that enables the research community
to directly submit updates and improvements via Github "Pull requests".

Refs:

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724452/

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3609896/

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587060/

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147606/

  Course examples:

% - CSE8803 Big Data Analytics for Healthcare, Spring 2016, at Georgia
Tech, taught by Jimeng Sun. http://www.sunlab.org/teaching/cse8803/
% - BIOMEDIN 215 DATA DRIVEN MEDICINE, Fall semester (several years
now), Stanford, taught by Nigam Shah.
http://shahlab.stanford.edu/biomedin215
% - Xiaopeng Zhao is teaching a course at University of Tennessee,
Knoxville
% Columbia course: computational methods in biomedical informatics (G4002)
% taught in the columbia university biomedical informatics department
% Noemie Elhadad is the professor.
% from
https://www.dbmi.columbia.edu/for-current-staff-students/courses/
(spring term courses):
% BINF G4002 Methods I

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