Platform for Medical Information Extraction From Incomplete Data
Conditions
Liver Cancer
Conditions: Keywords
clinical narrative report, time series data, missing value
Study Type
Observational
Study Phase
N/A
Study Design
Time Perspective: Retrospective
Overall Status
Recruiting
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.
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.
Criteria for eligibility
Healthy Volunteers: No
Maximum Age: N/A
Minimum Age: N/A
Gender: Both
Criteria: Patients with liver cancer
Location
National Taiwan University Hospital
Taipei, Taiwan
Status: Recruiting
Contact: Feipei Lai - +886-2-33664924 - flai@ntu.edu.tw
Start Date
March 2013
Completion Date
March 2016
Sponsors
National Taiwan University Hospital
Source
National Taiwan University Hospital
Record processing date
ClinicalTrials.gov processed this data on July 28, 2015
ClinicalTrials.gov page