Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Early prediction of patient outcomes is important for targeting preventive care. This protocol
describes a practical workflow for developing deep-learning risk models that can predict …
describes a practical workflow for developing deep-learning risk models that can predict …
A clinically applicable approach to continuous prediction of future acute kidney injury
The early prediction of deterioration could have an important role in supporting healthcare
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …
[HTML][HTML] Scalable and accurate deep learning with electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive
personalized medicine and improve healthcare quality. Constructing predictive statistical …
personalized medicine and improve healthcare quality. Constructing predictive statistical …
Utilizing Electronic Health Records to Predict Acute Kidney Injury Risk and Outcomes: Workgroup Statements from the 15th ADQI Consensus Conference
SM Sutherland, LS Chawla… - Canadian journal of …, 2016 - journals.sagepub.com
The data contained within the electronic health record (EHR) is “big” from the standpoint of
volume, velocity, and variety. These circumstances and the pervasive trend towards EHR …
volume, velocity, and variety. These circumstances and the pervasive trend towards EHR …
[HTML][HTML] Machine learning–based prediction models for different clinical risks in different hospitals: evaluation of live performance
H Sun, K Depraetere, L Meesseman… - Journal of Medical …, 2022 - jmir.org
Background Machine learning algorithms are currently used in a wide array of clinical
domains to produce models that can predict clinical risk events. Most models are developed …
domains to produce models that can predict clinical risk events. Most models are developed …
Metapred: Meta-learning for clinical risk prediction with limited patient electronic health records
In recent years, large amounts of health data, such as patient Electronic Health Records
(EHR), are becoming readily available. This provides an unprecedented opportunity for …
(EHR), are becoming readily available. This provides an unprecedented opportunity for …
[HTML][HTML] Deep learning for electronic health records: A comparative review of multiple deep neural architectures
Despite the recent developments in deep learning models, their applications in clinical
decision-support systems have been very limited. Recent digitalisation of health records …
decision-support systems have been very limited. Recent digitalisation of health records …
The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review
Abstract Introduction: Electronic Health Record (EHR) is a significant source of medical data
that can be used to develop predictive modelling with therapeutically useful outcomes …
that can be used to develop predictive modelling with therapeutically useful outcomes …
Risk prediction on electronic health records with prior medical knowledge
Predicting the risk of potential diseases from Electronic Health Records (EHR) has attracted
considerable attention in recent years, especially with the development of deep learning …
considerable attention in recent years, especially with the development of deep learning …
Carepre: An intelligent clinical decision assistance system
Clinical decision support systems are widely used to assist with medical decision making.
However, clinical decision support systems typically require manually curated rules and …
However, clinical decision support systems typically require manually curated rules and …