John Baumann, Samuel Marshall, Andrew Groneck, Stefan J Hanish, Theodore Choma, Steven DeFroda
Readability of spine-related patient education materials: a standard method for improvement
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
knowledge graph effectively showcase essential characteristics data increasingly emerging significant means integrating information field artificial intelligence coronary artery plaque represents significant etiology cardiovascular events posing diagnostic challenge clinicians confronted multitude nonspecific symptoms visualize hierarchical relationship network graph molecular mechanisms underlying plaque properties symptom phenotypes patient symptomatology extracted electronic health record data realworld clinical settings phenotypic networks constructed utilizing clinical data proteinprotein interaction networks machine learning techniques including convolutional neural networks dijkstras algorithm gene ontology semantic similarity employed quantify clinical biological features within network resulting features utilized train knearest neighbor model yielding 23 symptoms 41 association rules 61 hub genes across three types plaques studied achieving area curve 925 weighted correlation network analysis pathway enrichment subsequently utilized identify lipid statusrelated genes inflammationassociated pathways could help explain differences plaque properties confirm validity network graph model conducted coexpression analysis hub genes evaluate potential diagnostic value additionally investigated immune cell infiltration examined correlations hub genes immune cells validated reliability identified biological pathways integrating clinical data molecular network information biomedical knowledge graph model effectively elucidated potential molecular mechanisms collude symptoms diseases molecules