Platform for Medical Information Extraction From Incomplete Data

NCT01813942 | Unknown

• 1 other identifier: 201302013RINC
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Liver Cancer

Keywords

clinical narrative report; time series data; missing value

Outcome Measures

Primary Outcomes (1)

• The number of patients correctly identified by recurrence predictive model

  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

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with liver cancer

Patients with liver cancer

Contact the study team to discuss eligibility requirements. They can help determine if this study is right for you.

Sponsors & Collaborators

• National Taiwan University Hospital: lead
• National Science and Technology Council, Taiwan: collaborator

Study Sites (1)

National Taiwan University Hospital

Taipei, Taiwan

RECRUITING

MeSH Terms

Conditions

Liver Neoplasms

Condition Hierarchy (Ancestors)

Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Liver Diseases

Study Officials

• Feipei Lai

  National Taiwan University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Feipei Lai

CONTACT

+886-2-33664924; flai@ntu.edu.tw

Study Design

• Study Type: observational
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 5, 2013
• First Posted: March 19, 2013
• Study Start: March 1, 2013
• Primary Completion: March 1, 2016
• Study Completion: March 1, 2016
• Last Updated: October 28, 2013

Record last verified: 2013-10

Locations

TW (1)