5/2/2023 0 Comments Health data generator![]() Our goal is to be able to capture a variety of types of data present in health, healthcare, and medical domains:ĭemographics – includes information that may or may not change such as: name, MRN, DOB, SSN, race, ethnicity, and place of birthįamily History – used as an additional factor as a part of the patient’s medical history which may or may not typically coded The project’s two min goals are to create machine learning methods capable of effectively capturing longitudinal patient data, and by doing so to create a set of models that can be used to generate synthetic longitudinal data. Simulation – this type of testing is usually done within the healthcare setting where training incorporates using a “dummy” patient, large scare discrete-event, and/or agent-based simulation used to model large populations of patients like the Monte Carlo simulation used to test decision-making modelsEpidemiology – the model’s accuracy depends on the use of accurate realistic data Some of the applications include:ĮHR Software Testing – used in testing functionality of newly developed EHR systemsĪlgorithm Development – methods used in data mining, health services research, statistics, and other areas that rely on data that resemble real patient data.Įducation – synthetic data is useful in training students in areas that require access to large amounts of realistic data While many research projects in healthcare and medicine focus on analyzing de-identified and limited datasets, there are important applications that require data that is not limited. However, patient data is also regarded as sensitive and protected information that is only available to those who have been given the privilege to access such information. Patient data is also an essential tool in providing a better quality of care through preventative measures and addressing current medical conditions. Patient data is an important factor in managing a patient’s overall health and equips providers a bigger picture and better understanding of their patient. We use machine learning to learn models from real data, combine these models with expert knowledge, and together apply to generate new synthetic data. ![]() Our mission is to go beyond what is often seen in synthetic data (i.e., demographics or claims), but be able to additionally generate clinical data and consumer-generated data. This project focuses on large scale generation of realistic synthetic patient data. Intelligent patient data generator (iPDG)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |