The Development of a Medical Ontology for Data Mining to classify, predict, and treat cancer subtypes in the PLCO population
Principal Investigator
Name
Jinglu Wang
Degrees
B.A.
Institution
Weill Cornell Medical College
Position Title
Graduate Student
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-233
Initial CDAS Request Approval
Oct 7, 2016
Title
The Development of a Medical Ontology for Data Mining to classify, predict, and treat cancer subtypes in the PLCO population
Summary
Working with my clinical collaborators, I would like to develop data mining techniques to detect, classify, and predict the typology of patients, given inputs of screening and clinical data.
Using this data, I would like to develop clinical decision support tools to help clinicians decide when a patient has early stage cancer, and help these clinicians develop an appropriate care plan specific to the patient's cancer phenotype and other data points commonly available within a large EHR dataset.
Using this data, I would like to develop clinical decision support tools to help clinicians decide when a patient has early stage cancer, and help these clinicians develop an appropriate care plan specific to the patient's cancer phenotype and other data points commonly available within a large EHR dataset.
Aims
We have two use-cases or specific aims:
1. Develop a tool to correctly classify and group cancer types and subtypes (including accurately classifying patients as having no cancer on evaluation)
2. Find a way to match those cancer phenotypes to other information in other large datasets (e.g. match the cancer phenotype, and use other data points available to researchers to automatically connect the patient with a matching clinical trial)
Collaborators
Joseph Kabariti (Weill Cornell Medical College)
Evan Sholle (Weill Cornell Medical College)
Steven E. Flores (Weill Cornell Medical College)
Philip Jeng (Weill Cornell Medical College)