GitHub - kartoun/emrbots: EMRBots are experimental artificially generated electronic medical records (EMRs). The aim of EMRBots is to allow non-commercial entities (such as universities) to use the artificial patient repositories to practice statistical and machine-learning algorithms. Commercial entities can also use the repositories for any purpose, as long as they do not create software products using the repositories.

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EMRBots are experimental artificially generated electronic medical records (EMRs). The aim of EMRBots is to allow non-commercial entities (such as universities) to use the artificial patient repositories to practice statistical and machine-learning algorithms. Commercial entities can also use the repositories for any purpose, as long as they do …

en.wikipedia.org/wiki/EMRBots

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Name		Name	Last commit message	Last commit date
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Configuration		Configuration
Docs		Docs
ICD10_Parser		ICD10_Parser
Simulator		Simulator
.gitattributes		.gitattributes
ReadMe.txt		ReadMe.txt

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EXPERIMENT WITH ARTIFICIAL LARGE MEDICAL DATA-SETS WITHOUT WORRYING ABOUT PRIVACY.

Created by Uri Kartoun, PhD

https://en.wikipedia.org/wiki/EMRBots

http://www.emrbots.org/

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Download 100-patient (1.5MB), 10,000-patient (140MB), and 100,000-patient (1.4GB) artificial EMR databases (for free).

https://figshare.com/search?q=emrbots

https://data.niaid.nih.gov/search?q=emrbots

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It is difficult to get access to Electronic Medical Records (EMRs) due to privacy concerns and technical burdens.
I am in a process of founding a company focused on developing a new EMR management platform and I want to demonstrate to a venture capital company and to potential customers the ability of my product to handle big data. Current simulated medical databases are limited and are hard to configure.

I am a student or a researcher working at a university that does not have yet an access to EMR system and I am interested in evaluating machine learning algorithms. Tedious bureaucracy.

I am teaching a computer science course and I wish to let my 150 students to experiment with electronic medical records. Not possible due to privacy issues.

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Solution: a database of artifical patients.
The data is generated according to pre-defined criteria and is not based on any human data.

The database contains the same characteristics that exist in a real medical database such as patients’ admission details, demographics, socioeconomic details, labs, medications, etc.

The database is customizable. For example, it is possible to generate a population of 100,000 patients of which 60% are male, 40% are African American, 15% are diabetic, specific lab range distributions can be set, etc.

The number of records can range from several thousands to millions, depending on the desired configuration.

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