Platform for Medical Information Extraction From Incomplete Data

October 25, 2013 updated by: National Taiwan University Hospital
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.

Study Overview

Status

Unknown

Conditions

Detailed Description

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.

Study Type

Observational

Enrollment (Anticipated)

10000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Taiwan
      • Taipei, Taiwan
        • Recruiting
        • National Taiwan University Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients with liver cancer

Description

Patients with liver cancer

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The number of patients correctly identified by recurrence predictive model
Time Frame: 3 years
The recurrence predictive model is developed using the incomplete data set, this model is used for predicting the recurrent status of patient who received the specific treatment for liver cancer. The number of patients correctly identified by recurrence predictive model is regarded as the primary outcome measure.
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

National Taiwan University Hospital

Collaborators

National Science Council, Taiwan

Investigators

  • Principal Investigator: Feipei Lai, National Taiwan University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start

March 1, 2013

Primary Completion (Anticipated)

March 1, 2016

Study Completion (Anticipated)

March 1, 2016

Study Registration Dates

First Submitted

March 5, 2013

First Submitted That Met QC Criteria

March 14, 2013

First Posted (Estimate)

March 19, 2013

Study Record Updates

Last Update Posted (Estimate)

October 28, 2013

Last Update Submitted That Met QC Criteria

October 25, 2013

Last Verified

October 1, 2013

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 201302013RINC

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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