ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening
| Autor: | Mihaela van der Schaar, Kyeong H. Moon, William Hsu, Ahmed M. Alaa |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
clinical decision support Computer science multimedia-based healthcare cs.LG 02 engineering and technology computer.software_genre Machine Learning (cs.LG) Breast cancer screening 0302 clinical medicine Breast cancer Engineering 0202 electrical engineering electronic engineering information engineering Artificial Intelligence & Image Processing 030212 general & internal medicine Cancer education.field_of_study screening and diagnosis medicine.diagnostic_test Diagnostic test personalized medicine Health Services Computer Science Applications Detection Networking and Information Technology R&D (NITRD) 020201 artificial intelligence & image processing Patient Safety 4.4 Population screening 4.2 Evaluation of markers and technologies Screening test Population Machine learning Clinical decision support system supervised learning personalized screening policies 03 medical and health sciences Electronic health record Clinical Research Information and Computing Sciences Media Technology medicine Electrical and Electronic Engineering Cluster analysis Set (psychology) education business.industry Supervised learning medicine.disease Computer Science - Learning Good Health and Well Being Signal Processing False positive rate Artificial intelligence business computer |
| Zdroj: | IEEE Transactions on Multimedia, vol 18, iss 10 |
| Popis: | Breast cancer screening policies attempt to achieve timely diagnosis by the regular screening of apparently healthy women. Various clinical decisions are needed to manage the screening process; those include: selecting the screening tests for a woman to take, interpreting the test outcomes, and deciding whether or not a woman should be referred to a diagnostic test. Such decisions are currently guided by clinical practice guidelines (CPGs), which represent a one-size-fits-all approach that are designed to work well on average for a population, without guaranteeing that it will work well uniformly over that population. Since the risks and benefits of screening are functions of each patients features, personalized screening policies that are tailored to the features of individuals are needed in order to ensure that the right tests are recommended to the right woman. In order to address this issue, we present ConfidentCare: a computer-aided clinical decision support system that learns a personalized screening policy from the electronic health record (EHR) data. ConfidentCare operates by recognizing clusters of similar patients, and learning the best screening policy to adopt for each cluster. A cluster of patients is a set of patients with similar features (e.g. age, breast density, family history, etc.), and the screening policy is a set of guidelines on what actions to recommend for a woman given her features and screening test scores. ConfidentCare algorithm ensures that the policy adopted for every cluster of patients satisfies a predefined accuracy requirement with a high level of confidence. We show that our algorithm outperforms the current CPGs in terms of cost-efficiency and false positive rates. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|