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.
Biostatistics, Electronic Medical Record, Data Ethics, Descriptive Statistics, Data Mining, Predictive Analytics, Clinical Data Management, Interoperability, Patient Flow, Machine Learning, SQL, Database Design, Descriptive Analytics, ICD Coding (ICD-9/ICD-10), Precision Medicine, Health Informatics
Reviews
4.6 (14 ratings)
5 stars
85.71%
3 stars
7.14%
1 star
7.14%
DA
Aug 3, 2022
This course is highly informative and practical-oriented. It has increased my desire in the clinical data analytics field
KD
Jul 20, 2023
This is a great learning curve to properly introduce me into data analysis, and machine learning in healthcare data
From the lesson
Concepts in MIMIC-III and an example of patients inclusion flowchart
This week includes an overview of clinical concepts, which are statistical tools to provide illness scores. They are developed based on expert opinion and subsequently extended based on data-driven methods. These models are the precursor of machine learning models for precision medicine. Finally, the practical exercises of this week provides the opportunity to implement a complex flowchart of patients inclusion.