Predicting morbidity by local similarities in multi-scale patient trajectories
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| dc.contributor.author | Carrasco-Ribelles, Lucía Amalia
|
es_ES |
| dc.contributor.author | Pardo-Más, Jose Ramón
|
es_ES |
| dc.contributor.author | Tortajada, Salvador
|
es_ES |
| dc.contributor.author | Sáez Silvestre, Carlos
|
es_ES |
| dc.contributor.author | Valdivieso, Bernardo
|
es_ES |
| dc.contributor.author | Garcia-Gomez, Juan M
|
es_ES |
| dc.date.accessioned | 2022-09-29T18:04:52Z | |
| dc.date.available | 2022-09-29T18:04:52Z | |
| dc.date.issued | 2021-08 | es_ES |
| dc.identifier.issn | 1532-0464 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10251/186757 | |
| dc.description.abstract | [EN] 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. | es_ES |
| dc.description.sponsorship | This work was supported by the CrowdHealth project (COLLECTIVE WISDOM DRIVING PUBLIC HEALTH POLICIES (727560)) and the MTS4up project (DPI2016-80054-R). | es_ES |
| dc.language | Inglés | es_ES |
| dc.publisher | Elsevier | es_ES |
| dc.relation.ispartof | Journal of Biomedical Informatics | es_ES |
| dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
| dc.subject | Patient trajectory | es_ES |
| dc.subject | Risk prediction | es_ES |
| dc.subject | Local alignment | es_ES |
| dc.subject | Dynamic programming | es_ES |
| dc.subject | Diabetes | es_ES |
| dc.subject | Cardiovascular disease | es_ES |
| dc.subject.classification | FISICA APLICADA | es_ES |
| dc.title | Predicting morbidity by local similarities in multi-scale patient trajectories | es_ES |
| dc.type | Artículo | es_ES |
| dc.identifier.doi | 10.1016/j.jbi.2021.103837 | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/727560/EU | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI//DPI2016-80054-R//BIOMARCADORES DINAMICOS BASADOS EN FIRMAS TISULARES MULTIPARAMETRICAS PARA EL SEGUIMIENTO Y EVALUACION DE LA RESPUESTA A TRATAMIENTO DE PACIENTES CON GLIOBLASTOMA Y CANCER DE PROSTATA/ | es_ES |
| dc.rights.accessRights | Abierto | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada | es_ES |
| dc.description.bibliographicCitation | Carrasco-Ribelles, LA.; Pardo-Más, JR.; Tortajada, S.; Sáez Silvestre, C.; Valdivieso, B.; Garcia-Gomez, JM. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. Journal of Biomedical Informatics. 120:1-9. https://doi.org/10.1016/j.jbi.2021.103837 | es_ES |
| dc.description.accrualMethod | S | es_ES |
| dc.relation.publisherversion | https://doi.org/10.1016/j.jbi.2021.103837 | es_ES |
| dc.description.upvformatpinicio | 1 | es_ES |
| dc.description.upvformatpfin | 9 | es_ES |
| dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
| dc.description.volume | 120 | es_ES |
| dc.identifier.pmid | 34119690 | es_ES |
| dc.relation.pasarela | S\444661 | es_ES |
| dc.contributor.funder | European Commission | es_ES |
| dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
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