Clinical trial NCT01813942: Platform for Medical Information Extraction From Incomplete Data
Official title: Platform for Medical Information Extraction From Incomplete Data
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Liver Cancer
Keywords: clinical narrative report
time series data
missing value
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
Collaborators: name: National Science and Technology Council, Taiwan
First posted: 2013-03-19
Enrollment: 10000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with liver cancer

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.

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.

Eligibility criteria: Patients with liver cancer

Primary outcomes:
measure: The number of patients correctly identified by recurrence predictive model
timeFrame: 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: National Taiwan University Hospital
Study officials:
affiliation: National Taiwan University
name: Feipei Lai
role: PRINCIPAL_INVESTIGATOR
References: