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Author (up) Carrasco-Ribelles, L.A.; Pardo-Mas, J.R.; Tortajada, S.; Saez, C.; Valdivieso, B.; Garcia-Gomez, J.M. doi  openurl
  Title Predicting morbidity by local similarities in multi-scale patient trajectories Type Journal Article
  Year 2021 Publication Journal of Biomedical Informatics Abbreviated Journal J. Biomed. Inform.  
  Volume 120 Issue Pages 103837 - 9pp  
  Keywords Patient trajectory; Risk prediction; Local alignment; Dynamic programming; Diabetes; Cardiovascular disease  
  Abstract Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.  
  Address [Carrasco-Ribelles, Lucia A.; Pardo-Mas, Jose Ramon; Saez, Carlos; Garcia-Gomez, Juan M.] Univ Politecn Valencia, Biomed Data Sci Lab BDSLAB, Inst Tecnol Informat & Comunicac ITACA, Camino Vera S-N, Valencia 46022, Spain, Email: lucarri@etsii.upv.es;  
  Corporate Author Thesis  
  Publisher Academic Press Inc Elsevier Science Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1532-0464 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000683527500003 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 4934  
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