Big Data and Text-mining Technologies Applied for Breast Cancer Medical Data Analysis
NCT ID: NCT02810093
Last Updated: 2016-06-22
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
10000 participants
OBSERVATIONAL
2016-05-31
2016-12-31
Brief Summary
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To develop a method to automatically extract and structure the information included in numerous medical records from breast cancer patients.
Secondary purpose :
With this procedure we can analyze the content of ten thousand anonymized textual medical records.
This information should enable us to explore many subjects, such as:
* The impact of certain therapeutic procedures
* The characteristics of sub-groups of patients
* Pregnancy associated breast cancers
* Risk factors
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Breast cancer patients between 2000 and 2016
Patients treated for a breast cancer between 2000 and 2016 in the Hospital of Strasbourg (France).
retrospective medical records analyze
Ten thousand medical records (between years 2000 and 2016) will be analyzed
Interventions
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retrospective medical records analyze
Ten thousand medical records (between years 2000 and 2016) will be analyzed
Eligibility Criteria
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Inclusion Criteria
* Malignant breast tumors
* signed informed consent
Exclusion Criteria
* Patients not initially treated at the Hôpitaux Universitaires de Strasbourg
18 Years
FEMALE
No
Sponsors
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Quantmetry
UNKNOWN
University Hospital, Strasbourg, France
OTHER
Responsible Party
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Principal Investigators
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Carole Mathelin, MD
Role: PRINCIPAL_INVESTIGATOR
Strasbourg's University Hospitals
Locations
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University Strasbourg Hospital
Strasbourg, , France
Countries
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Central Contacts
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Facility Contacts
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References
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Simoulin A, Thiebaut N, Neuberger K, Ibnouhsein I, Brunel N, Vine R, Bousquet N, Latapy J, Reix N, Moliere S, Lodi M, Mathelin C. From free-text electronic health records to structured cohorts: Onconum, an innovative methodology for real-world data mining in breast cancer. Comput Methods Programs Biomed. 2023 Oct;240:107693. doi: 10.1016/j.cmpb.2023.107693. Epub 2023 Jun 25.
Other Identifiers
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6373
Identifier Type: -
Identifier Source: org_study_id
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