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README.md

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 **The Problem:** It is difficult and expensive to access Electronic Medical Records (EMRs) due to privacy concerns and technical problems. 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. 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.
 
-**The Solution:** A database of artificial 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.
+**The Solution:** A database of artificial 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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+**Check also:** [Leveraging Large Language Models for Enhanced Clinical Narrative Analysis: An Application in Alcohol Use Detection](https://www.linkedin.com/pulse/leveraging-large-language-models-enhanced-clinical-uri-w6qye/?trackingId=06CMNcZa95lObWau2Ha%2FEg%3D%3D)