Machine learning for clinical outcome prediction
Clinical decision-making in healthcare is already being influenced by predictions or
recommendations made by data-driven machines. Numerous machine learning applications …
recommendations made by data-driven machines. Numerous machine learning applications …
[HTML][HTML] Evaluating pointwise reliability of machine learning prediction
Abstract Interest in Machine Learning applications to tackle clinical and biological problems
is increasing. This is driven by promising results reported in many research papers, the …
is increasing. This is driven by promising results reported in many research papers, the …
Artificial intelligence, machine learning, and deep learning for clinical outcome prediction
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would
usually require a human to complete. AI methods are well suited to predict clinical outcomes …
usually require a human to complete. AI methods are well suited to predict clinical outcomes …
Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
Abstract Machine learning can help clinicians to make individualized patient predictions only
if researchers demonstrate models that contribute novel insights, rather than learning the …
if researchers demonstrate models that contribute novel insights, rather than learning the …
Personalized mortality prediction driven by electronic medical data and a patient similarity metric
Background Clinical outcome prediction normally employs static, one-size-fits-all models
that perform well for the average patient but are sub-optimal for individual patients with …
that perform well for the average patient but are sub-optimal for individual patients with …
Performance metrics for the comparative analysis of clinical risk prediction models employing machine learning
Background: New methods such as machine learning techniques have been increasingly
used to enhance the performance of risk predictions for clinical decision-making. However …
used to enhance the performance of risk predictions for clinical decision-making. However …
Accessing artificial intelligence for clinical decision-making
C Giordano, M Brennan, B Mohamed… - Frontiers in digital …, 2021 - frontiersin.org
Advancements in computing and data from the near universal acceptance and
implementation of electronic health records has been formative for the growth of …
implementation of electronic health records has been formative for the growth of …
[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 …
Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning.
Successful adoption of electronic health records (EHRs) created an explosion in digital …
Successful adoption of electronic health records (EHRs) created an explosion in digital …
[HTML][HTML] Machine learning and prediction in medicine—beyond the peak of inflated expectations
Big data, we have all heard, promise to transform health care with the widespread capture of
electronic health records and high-volume data streams from sources ranging from …
electronic health records and high-volume data streams from sources ranging from …