Casual Analytics with Machine Learning
Casual analytics is becoming increasingly popular in healthcare. Aside from the intrinsic possibilities of incorporating domain knowledge into learning systems, casual machine learning offers a comprehensive set of tools for evaluating how a system might respond to an intervention.
We think that leveraging current breakthroughs in machine learning, we may include causal analytics into many elements of clinical decision support systems. Following the introduction of strong machine learning algorithms such as deep learning, significant progress has been achieved in predicting systems for healthcare.
Clinical decision support (CDS) technologies in healthcare produce predictions from electronic health record (EHR) data such as medical imaging, clinical free-text notes, blood tests, and genetic data for tasks such as detection, classification, and/or segmentation.
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