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Platform for Medical Information Extraction From Incomplete Data

NCT ID: NCT01813942

Last Updated: 2013-10-28

Study Results

Results pending

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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Recruitment Status

UNKNOWN

Total Enrollment

10000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2013-03-31

Study Completion Date

2016-03-31

Brief Summary

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In order to perform research smoothly, the process of information extraction is required for translating data in clinical text into available format for analysis and statistic. In medical research, the problem of missing data occurs frequently. It is important to develop the method with better imputation performance in the stability and accuracy. The purposes of this project are to provide the data integration and extraction methods for handling the structured and unstructured data sources in more efficient ways, to provide the validation scheme for facilitating the data reviewing of extracted results produced by information extraction modules, to increase the quality of clinical data by comparing the data from different data sources and correcting data errors and inconsistent, to handle the clinical data with the properties of time series and incompleteness, to increase accuracy of data analysis and increase quality of health care by improving the completeness and correctness of clinical data, to provide flexibility of methods in the platform. In the project, the disease topic is focused on the liver cancer patients' clinical data and we hope the methods in the projects can be extended to handle other diseases by replacing these knowledge models in the future.

Detailed Description

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Because of the increasing adoption of Electronic Medical Record (EMR) systems, the data access of EMR is more and more convenient. However, there still have difficulties in analyzing all the clinical data directly due to a large number of records using the narrative format. In order to perform research smoothly, the process of information extraction is required for translating data in clinical text into available format for analysis and statistic. In medical research, the problem of missing data occurs frequently. It is important to develop the method with better imputation performance in the stability and accuracy. The purposes of this project are to provide the data integration and extraction methods for handling the structured and unstructured data sources in more efficient ways, to provide the validation scheme for facilitating the data reviewing of extracted results produced by information extraction modules, to increase the quality of clinical data by comparing the data from different data sources and correcting data errors and inconsistent, to handle the clinical data with the properties of time series and incompleteness, to increase accuracy of data analysis and increase quality of health care by improving the completeness and correctness of clinical data, to provide flexibility of methods in the platform. In the project, the disease topic is focused on the liver cancer patients' clinical data and we hope the methods in the projects can be extended to handle other diseases by replacing these knowledge models in the future.

Conditions

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Liver Cancer

Keywords

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clinical narrative report time series data missing value

Study Design

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Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

Patients with liver cancer
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Science and Technology Council, Taiwan

OTHER_GOV

Sponsor Role collaborator

National Taiwan University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Feipei Lai

Role: PRINCIPAL_INVESTIGATOR

National Taiwan University

Locations

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National Taiwan University Hospital

Taipei, , Taiwan

Site Status RECRUITING

Countries

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Taiwan

Central Contacts

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Feipei Lai

Role: CONTACT

Phone: +886-2-33664924

Email: [email protected]

Facility Contacts

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Feipei Lai

Role: primary

Other Identifiers

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201302013RINC

Identifier Type: -

Identifier Source: org_study_id