Causal inference and counterfactual prediction in machine learning for actionable healthcare
Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …
becoming key to precision medicine—from identifying disease risks and taking preventive …
The myth of generalisability in clinical research and machine learning in health care
An emphasis on overly broad notions of generalisability as it pertains to applications of
machine learning in health care can overlook situations in which machine learning might …
machine learning in health care can overlook situations in which machine learning might …
Comparison of deep learning approaches to predict COVID-19 infection
TB Alakus, I Turkoglu - Chaos, Solitons & Fractals, 2020 - Elsevier
The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a
pandemic and has expanded all over the world. Because of increasing number of cases day …
pandemic and has expanded all over the world. Because of increasing number of cases day …
[HTML][HTML] A systematic review of the prediction of hospital length of stay: Towards a unified framework
Hospital length of stay of patients is a crucial factor for the effective planning and
management of hospital resources. There is considerable interest in predicting the LoS of …
management of hospital resources. There is considerable interest in predicting the LoS of …
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
In machine learning often a tradeoff must be made between accuracy and intelligibility. More
accurate models such as boosted trees, random forests, and neural nets usually are not …
accurate models such as boosted trees, random forests, and neural nets usually are not …
[HTML][HTML] Benchmarking deep learning models on large healthcare datasets
Deep learning models (aka Deep Neural Networks) have revolutionized many fields
including computer vision, natural language processing, speech recognition, and is being …
including computer vision, natural language processing, speech recognition, and is being …
Improving palliative care with deep learning
Background Access to palliative care is a key quality metric which most healthcare
organizations strive to improve. The primary challenges to increasing palliative care access …
organizations strive to improve. The primary challenges to increasing palliative care access …
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
In secondary analysis of electronic health records, a crucial task consists in correctly
identifying the patient cohort under investigation. In many cases, the most valuable and …
identifying the patient cohort under investigation. In many cases, the most valuable and …
Multitask learning
R Caruana - Machine learning, 1997 - Springer
Multitask Learning is an approach to inductive transfer that improves generalization by using
the domain information contained in the training signals of related tasks as an inductive bias …
the domain information contained in the training signals of related tasks as an inductive bias …
An empirical comparison of supervised learning algorithms
R Caruana, A Niculescu-Mizil - … of the 23rd international conference on …, 2006 - dl.acm.org
A number of supervised learning methods have been introduced in the last decade.
Unfortunately, the last comprehensive empirical evaluation of supervised learning was the …
Unfortunately, the last comprehensive empirical evaluation of supervised learning was the …