Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics

Representation learning methods that transform encoded data into continuous vector spaces are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for medical concepts from electronic health records or medical claims datasets.

We propose learning embeddings for medical concepts by using graph-based representation learning methods on SNOMED-CT, a widely popular knowledge graph in the healthcare domain with numerous operational and research applications. Current work presents an empirical analysis of various embedding methods, including the evaluation of their performance on multiple tasks of biomedical relevance .

PNNLab SciReports said it years ago when I implemented an EMR system KNOWING NOTHING about ANY OF IT yet, learned HL7 and how powerful YET LOCKED UP the data was and how you could NOT USE computers to come to a diagnosis - that has CHANGED now that it is somebody else's IDEA? had so many ideas..

PNNLab SciReports . Smart use of AI in primary healthcare analyses 24-hour heart rate variability, 24-hour cortisol-melatonin rhythm, serum micronutrients, circulating gut microbiota metabolites. .

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