Platform for Medical Information Extraction From Incomplete Data

Liver Cancer Clinical Trial

Official title:

Platform for Medical Information Extraction From Incomplete Data

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT01813942
• Other study ID #: 201302013RINC
• Status: Recruiting
• Phase: N/A
• First received: March 5, 2013
• Last updated: October 25, 2013
• Start date: March 2013
• Est. completion date: March 2016

Study information

• Verified date: October 2013
• Source: National Taiwan University Hospital
• Contact: Feipei Lai
• Phone: +886-2-33664924
• Email: flai[@]ntu.edu.tw
• Is FDA regulated: No
• Health authority: Taiwan: Department of Health
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: March 2016
• Est. primary completion date: March 2016
• Accepts healthy volunteers: No
• Gender: Both
• Age group: N/A and older
• Eligibility: Patients with liver cancer

Study Design

Time Perspective: Retrospective

Related Conditions & MeSH terms

• Liver Cancer

Locations

Country	Name	City	State
Taiwan	National Taiwan University Hospital	Taipei

Sponsors (2)

Lead Sponsor	Collaborator
National Taiwan University Hospital	National Science Council, Taiwan

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	No