Literature DB >> 33373363

Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.

Onintze Zaballa1, Aritz Pérez1, Elisa Gómez Inhiesto2, Teresa Acaiturri Ayesta2, Jose A Lozano1,3.   

Abstract

The aim of this paper is to analyze the sequence of actions in the health system associated with a particular disease. In order to do that, using Electronic Health Records, we define a general methodology that allows us to: (i) identify the actions in the health system associated with a disease; (ii) identify those patients with a complete treatment for the disease; (iii) and discover common treatment pathways followed by the patients with a specific diagnosis. The methodology takes into account the characteristics of the EHRs, such as record heterogeneity and missing information. As an example, we use the proposed methodology to analyze breast cancer disease. For this diagnosis, 5 groups of treatments, which fit in with medical practice guidelines and expert knowledge, were obtained.

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Mesh:

Year:  2020        PMID: 33373363      PMCID: PMC7771666          DOI: 10.1371/journal.pone.0244004

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  11 in total

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2.  Time warp edit distance with stiffness adjustment for time series matching.

Authors:  Pierre-François Marteau
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3.  Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†.

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4.  4th ESO-ESMO International Consensus Guidelines for Advanced Breast Cancer (ABC 4)†.

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Journal:  Ann Oncol       Date:  2018-08-01       Impact factor: 32.976

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Authors:  Zhengxing Huang; Xudong Lu; Huilong Duan
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Review 6.  Process mining in healthcare: A literature review.

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Review 8.  Mining electronic health records: towards better research applications and clinical care.

Authors:  Peter B Jensen; Lars J Jensen; Søren Brunak
Journal:  Nat Rev Genet       Date:  2012-05-02       Impact factor: 53.242

9.  Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

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10.  Paving the COWpath: Learning and visualizing clinical pathways from electronic health record data.

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  1 in total

1.  A machine learning approach to predict healthcare cost of breast cancer patients.

Authors:  Pratyusha Rakshit; Onintze Zaballa; Aritz Pérez; Elisa Gómez-Inhiesto; Maria T Acaiturri-Ayesta; Jose A Lozano
Journal:  Sci Rep       Date:  2021-06-14       Impact factor: 4.379

  1 in total

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