KD
This is a great learning curve to properly introduce me into data analysis, and machine learning in healthcare data
This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems. The main areas that would explore are: Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes. Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics. Explainable deep learning models for healthcare applications: What it is and why it is needed. Clinical Decision Support Systems: Generalisation, bias, ‘fairness’, clinical usefulness and privacy of artificial intelligence algorithms.
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