Analysis of free text in electronic health records for identification of cancer patient trajectories

Title: Analysis of free text in electronic health records for identification of cancer patient trajectories
Authors: Jensen, K
Soguero-Ruiz, C
Mikalsen, KO
Lindsetmo, R-O
Kouskoumvekaki, I
Girolami, M
Skrovseth, SO
Augestad, KM
Item Type: Journal Article
Abstract: With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004–2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.
Issue Date: 7-Apr-2017
Date of Acceptance: 13-Mar-2017
URI: http://hdl.handle.net/10044/1/66126
DOI: https://dx.doi.org/10.1038/srep46226
ISSN: 2045-2322
Publisher: Nature Publishing Group
Journal / Book Title: Scientific Reports
Volume: 7
Copyright Statement: © 2017 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
GOODNESS-OF-FIT
SURGICAL COMPLICATIONS
FEATURE-SELECTION
CLINICAL-DATA
RISK-FACTORS
SURGERY
STRATIFICATION
CLASSIFICATION
READMISSIONS
COHORT
Confounding Factors (Epidemiology)
Decision Support Systems, Clinical
Disease Progression
Electronic Health Records
Health Status
Humans
Morbidity
Neoplasms
Norway
Humans
Neoplasms
Disease Progression
Morbidity
Confounding Factors (Epidemiology)
Health Status
Decision Support Systems, Clinical
Norway
Electronic Health Records
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
GOODNESS-OF-FIT
SURGICAL COMPLICATIONS
FEATURE-SELECTION
CLINICAL-DATA
RISK-FACTORS
SURGERY
STRATIFICATION
CLASSIFICATION
READMISSIONS
COHORT
Publication Status: Published
Article Number: 46226
Online Publication Date: 2017-04-07
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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