Liver CancerNCT01813942
Platform for Medical Information Extraction From Incomplete Data
An observational study of Liver Cancer, run by National Taiwan University Hospital at 1 site in Taiwan.
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Liver Cancer
- How many people
- 10,000 participants
- Where
- 1 siteTaiwan
- Study dates
- Mar 2013to Mar 2016
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with liver cancer
Patients with liver cancer
Trial locations and contacts
1 site in 1 country.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Hospital
Taipei
Feipei Lai, +886-2-33664924, flai@ntu.edu.tw
About this study
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.
Read the 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.
- Conditions
- Liver Cancer
- Keywords
- clinical narrative report, time series data, missing value
- Medical subject headings
- Liver Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Mar 2013 (estimated)
- Study startMar 2013
- Primary completionMar 2016 (estimated)
- Study completionMar 2016 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver CancerStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Time perspective
- Retrospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Science and Technology Council, Taiwan
- Responsible party
- Sponsor
- Investigators
- Feipei Lai, Principal investigator, National Taiwan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT01813942
- Sponsor's protocol ID
- 201302013RINC
- Status last verified
- Oct 2013
- First submitted
- 5 Mar 2013
- Last updated
- 28 Oct 2013
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Oct 2013. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Platform for Medical Information Extraction From Incomplete Data
- ClinicalTrials.gov ID
- NCT01813942
- Acronym / sponsor ID
- 201302013RINC
- Conditions
- Liver Cancer
- Keywords
- clinical narrative report, time series data, missing value
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- National Science and Technology Council, Taiwan
- Study start
- Mar 2013
- Primary completion
- Mar 2016 (estimated)
- Study completion
- Mar 2016 (estimated)
- First posted
- 19 Mar 2013 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with liver cancer
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Oct 2013.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Feipei Lai principal investigator
National Taiwan University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Liver Cancer, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Oct 2013. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Feipei Lai (+886-2-33664924, flai@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The number of patients correctly identified by recurrence predictive model (time frame: 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Oct 2013.