Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors.
In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 12; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153929239&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153929239 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2021 vid: 45 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153929239 153929239 153929239 10.1007/s10916-021-01770-3 153929239 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors. aug: au: Manzo, Gaetano Calvaresi, Davide Jimenez-del-Toro, Oscar Calbimonte, Jean-Paul Schumacher, Michael affil: University of Applied Sciences and Arts Western Switzerland (HES-SO), Institut Informatique de Gestion, HES-SO Valais-Wallis, Sierre, Switzerland sug: subj: Machine Learning Decision Support Systems, Clinical User-Computer Interface Cancer Survivors Breast Neoplasms Prognosis Neoplasm Recurrence, Local Risk Factors Risk Assessment Breast Neoplasms Therapy Individualized Medicine Human Deep Learning Prediction Models Electronic Health Records Female Cancer Patients Health Behavior Cox Proportional Hazards Model Kaplan-Meier Estimator Descriptive Statistics Support Vector Machine Decision Trees Neural Networks (Computer) Adult Middle Age Aged Aged, 80 and Over Confidence Intervals Logistic Regression Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female ab: In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient's high-risk markers, and support treatment decisions. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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