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Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data.

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Chronic disease prediction using administrative data and graph theory: The case of type 2 diabetes., , and . Expert Syst. Appl., (2019)Quantifying encircling behaviour in complex networks., , , and . CICS, page 9-14. IEEE, (2013)Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities., , , , , , and . IEEE Access, (2019)Measuring Topological Robustness of Networks under Sustained Targeted Attacks., , and . ASONAM, page 38-45. IEEE Computer Society, (2012)Understanding the Progression of Congestive Heart Failure of Type 2 Diabetes Patient Using Disease Network and Hospital Claim Data., , and . COMPLEX NETWORKS (2), volume 882 of Studies in Computational Intelligence, page 774-788. Springer, (2019)Mining adverse drug events from patients' disease histories via a GNN-based subgraph prediction method., and . ACSW, page 227-230. ACM, (2023)A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus., , , , and . Appl. Intell., 52 (3): 2411-2422 (2022)Fusion of Graph and Natural Language Processing in Predictive Analytics for Adverse Drug Reactions., and . ACSW, page 65-68. ACM, (2024)New Direction in Degree Centrality Measure: Towards a Time-Variant Approach., , and . Int. J. Inf. Technol. Decis. Mak., 13 (4): 865- (2014)Few research fields play major role in interdisciplinary grant success., , and . Scientometrics, 119 (1): 237-246 (2019)