Mining electronic health records (EHRs) A survey
The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review
Abstract Functional Magnetic Resonance Imaging (fMRI) is presently one of the most
popular techniques for analysing the dynamic states in brain images using various kinds of …
popular techniques for analysing the dynamic states in brain images using various kinds of …
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform
medical care. Although machine learning classifiers (MLCs) have already demonstrated …
medical care. Although machine learning classifiers (MLCs) have already demonstrated …
Risk prediction with electronic health records: A deep learning approach
The recent years have witnessed a surge of interests in data analytics with patient Electronic
Health Records (EHR). Data-driven healthcare, which aims at effective utilization of big …
Health Records (EHR). Data-driven healthcare, which aims at effective utilization of big …
Population-level prediction of type 2 diabetes from claims data and analysis of risk factors
N Razavian, S Blecker, AM Schmidt, A Smith-McLallen… - Big Data, 2015 - liebertpub.com
We present a new approach to population health, in which data-driven predictive models are
learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from …
learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from …
Constructing disease network and temporal progression model via context-sensitive hawkes process
Modeling disease relationships and temporal progression are two key problems in health
analytics, which have not been studied together due to data and technical challenges …
analytics, which have not been studied together due to data and technical challenges …
Health-atm: A deep architecture for multifaceted patient health record representation and risk prediction
Leveraging massive electronic health records (EHR) brings tremendous promises to
advance clinical and precision medicine informatics research. However, it is very …
advance clinical and precision medicine informatics research. However, it is very …
Tensor factorization toward precision medicine
Precision medicine initiatives come amid the rapid growth in quantity and variety of
biomedical data, which exceeds the capacity of matrix-oriented data representations and …
biomedical data, which exceeds the capacity of matrix-oriented data representations and …
[HTML][HTML] A predictive model for medical events based on contextual embedding of temporal sequences
Background: Medical concepts are inherently ambiguous and error-prone due to human
fallibility, which makes it hard for them to be fully used by classical machine learning …
fallibility, which makes it hard for them to be fully used by classical machine learning …
Auxiliary diagnosis of developmental dysplasia of the hip by automated detection of Sharp's angle on standardized anteroposterior pelvic radiographs
Q Li, L Zhong, H Huang, H Liu, Y Qin, Y Wang, Z Zhou… - Medicine, 2019 - journals.lww.com
Developmental dysplasia of the hip (DDH) is common, and features a widened Sharp's
angle as observed on pelvic x-ray images. Determination of Sharp's angle, essential for …
angle as observed on pelvic x-ray images. Determination of Sharp's angle, essential for …