This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics.
The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.
DA
This course is highly informative and practical-oriented. It has increased my desire in the clinical data analytics field
KD
This is a great learning curve to properly introduce me into data analysis, and machine learning in healthcare data
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The content and syllabus are great. The difficulty for most of the course is low but it ramps up drastically at the end of week 4 where I had to refer to the implementation given by the instructor as my SQL skills were not good enough. You can finish this first course without having access to the complete MIMIC-III database, and you can use the demo for all the exercises, however, it took me a weekend to complete the training and process for requesting access so if you are new to clinical databases consider an extra week of work for the MIMIC-III process. It is a very academic field so there is a lot to read and study but it is accessible enough for a layman engineer like me.
This course is highly informative and practical-oriented. It has increased my desire in the clinical data analytics field
This is a great learning curve to properly introduce me into data analysis, and machine learning in healthcare data
Requires you to pay for another course to access the data