C
Cindy Marling
Researcher at Ohio University
Publications - 57
Citations - 1744
Cindy Marling is an academic researcher from Ohio University. The author has contributed to research in topics: Case-based reasoning & Diabetes management. The author has an hindex of 20, co-authored 57 publications receiving 1522 citations. Previous affiliations of Cindy Marling include Case Western Reserve University.
Papers
More filters
Journal ArticleDOI
Case-based reasoning in the health sciences: What's next?
TL;DR: Current research in CBR in the health sciences is marked by its richness, and CBR systems are being better designed to account for the complexity of biomedicine, to integrate into clinical settings and to communicate and interact with diverse systems and methods.
Proceedings Article
The OhioT1DM Dataset For Blood Glucose Level Prediction.
Cindy Marling,Razvan Bunescu +1 more
TL;DR: The OhioT1DM Dataset is developed to promote and facilitate research in blood glucose level prediction and contains eight weeks' worth of continuous glucose monitoring, insulin, physiological sensor, and self-reported life-event data for each of 12 people with type 1 diabetes.
Proceedings Article
A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management
TL;DR: A generic physiological model of blood glucose dynamics is used to generate informative features for a Support Vector Regression model that is trained on patient specific data and could be used to anticipate almost a quarter of hypoglycemic events 30 minutes in advance.
Book ChapterDOI
Case-Based Reasoning in the Care of Alzheimer's Disease Patients
TL;DR: The Auguste Project is an effort to provide decision support for planning the ongoing care of AD patients, using CBR and other thought processes natural to members of geriatric interdisciplinary teams, and the first system prototype has just been completed.
Journal ArticleDOI
Hypoglycemia in Type 2 Diabetes - More Common Than You Think A Continuous Glucose Monitoring Study
TL;DR: CGMS can provide rich data that show glucose excursions in diabetes patients throughout the day, Consequently, unwarranted onset of hypo- and hyperglycemic events can be detected, intervened, and prevented by using CGMS.