EXPERIMENT WITH ARTIFICIAL LARGE MEDICAL DATA-SETS WITHOUT WORRYING ABOUT PRIVACY.
Click to read how EMRBots was used to create a new type of neural network called Time-Aware LSTM (T-LSTM).
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.
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.
Click here to register to download 100-patient (1.5MB), 10,000-patient (140MB), and 100,000-patient (1.4GB) artificial EMR databases (for free).
Planning to apply complex algorithms known as "best" or "used by millions" to extract features from narrative notes? Before you do that, consider giving a try Text Nailing (read more in Communications of the ACM and in Wikipedia). You may find that both your micro-average and macro-average F-scores get closer to 1.0.