Volume 3: How Machine Learning
is Being Leveraged to Analyze
Real-World Data
A Collection of Recent Research and Use Cases
The growth of machine learning in healthcare, clinical practice, and research has been rapid and exponential in recent years from under one hundred published articles in each area as recently as 2012 to a total of several thousand by 2019 – and that number continues to grow.
In Volume #1 and Volume #2 of this series, we offered examples of the ways in which researchers are using the power of machine learning and real-world data to enhance their study efforts.
In Volume #3, we examine another 12 examples of the use of machine learning to generate RWE that helps further research in several clinical areas, including:
- Predicting HIV, lung cancer, and delirium risk
- Detecting COVID-19, malaria, and risk of stroke
- Screening for disease in non-invasive ways
- Predicting time to treatment response, treatment outcomes, and medical adherence
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