Virtual Library

Start Your Search

Consuelo Parejo



Author of

  • +

    MA16 - Prioritizing Use of Technology to Improve Survival of Lung Cancer Subgroups and Outcomes with Chemotherapy and Surgery (ID 142)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Now Available
    • +

      MA16.03 - Big Data Analysis for Personalized Medicine in Lung Cancer Patients (Now Available) (ID 2532)

      15:45 - 17:15  |  Author(s): Consuelo Parejo

      • Abstract
      • Presentation
      • Slides

      Background

      The use of Big Data in healthcare is in its early days, and most of the potential for value creation remains unclaimed.

      Electronic Health Records (EHR) contain a large amount of information about the patient's condition, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We report on a first integration of an NLP framework for the analysis of clinical records of lung cancer in Puerta de Hierro University Hospital (HUPHM).

      Method

      A cohort of 1000 patients diagnosed of non-small cell lung cancer (NSCLC) from 2009 to 2018 at HUPHM were included in this observational study. Unstructured clinical data were obtained from the EHR. The semantic indexing and the information analysis was performed by the Politecnica University of Madrid, using Big Data and machine learning techniques. Clinical notes were converted into usable data, and combined with genomic data, images and bibliography, such as PubMed or Drugbank.

      Result

      A total of 251.730 documents were analyzed (240.851 notes and 10.879 reports). These heterogeneous sources of information were analyzed and integrated in an interactive user interface (Figure 1). As a result, all this large amounts of data turns into actionable and exploitable information for clinicians and authorities for planning public health policies and also create new clinical trials.

      The interactive platform will allow the clinician obtain immediate and personalized information of each patient and will elaborate predictive models for long survivors, identify risk patients, reduce overtreatments, etc.

      Conclusion

      By using Big Data we will be able to exploit large amounts of clinical information and combine them with multiple databases developing interactive user interface, increasing lung cancer knowledge and directing medicine towards a more personalized one.

      This work was supported by the EU H2020 programme, under grant agreement NÂș 727658 ( Project iASiS).

      captura de pantalla 2019-04-10 a las 21.07.09.png

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P1.16 - Treatment in the Real World - Support, Survivorship, Systems Research (ID 186)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
    • +

      P1.16-12 - An Analysis of Healthcare Use and the Cost Associated to End-of-Life Care of Lung Cancer Patients in a Spanish Hospital (Now Available) (ID 896)

      09:45 - 18:00  |  Author(s): Consuelo Parejo

      • Abstract
      • Slides

      Background

      Despite various studies have demonstrated that the consumption of healthcare services by cancer patients, and therefore healthcare costs, increases during the end-of-life period, there are insufficient studies which focus specifically on lung cancer. Thus we aim to study the consumption of healthcare services and its cost in end-of-life lung cancer patients attended in the Puerta de Hierro University Hospital, Spain.

      Method

      The Cross Industry Standard Process for Data Mining (CRISP-DM) was applied to integrate and analyze activity data extracted from the Electronic Patient Record (EPR) and clinical data of lung cancer patients from a previous study. A cohort of 224 deceased patients, diagnosed between 01/01/2009 and 31/12/2016, was analyzed to determine the use of healthcare services together with the time-related distribution of such use and associated cost during the end-of-life period.

      Result

      Overall, 82% of the patients received outpatient care, 68% received emergency service care, 53% experienced some period of hospitalization during the last 30 days of their life, and 38% of the patients died during hospitalization. The cost of hospitalization amounted to 88% of the total (excluding the cost of outpatient administered medication and radiotherapy treatment), with the cost of emergency services and consultations being 2.3% and 9.6%, respectively. In total, 58% of the healthcare assistance cost occurred during the last 6 months of life, and 21% in the last 30 days. The costs during the last 30 days represented 36% of the cost during the last 6 months.

      table1.jpgfigure1.jpg

      Conclusion

      Integrating activity data from EPR and clinical structured data from lung cancer patients and applying CRISP-DM has allowed us to describe end-of-life healthcare which could be used to plan resources and improve the quality of care in these patients.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.