Opportunities in Machine Learning for Healthcare
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Summary
This article serves as a primer to illuminate challenges of learning in a clinical setting and highlights opportunities for members of the machine learning community to contribute to healthcare.
- Type
- preprint
- Published
- 2018-06-01
- Cited by
- 112
- References
- 98
- Access
- Open access
- OpenAlex
- https://openalex.org/W2806535995
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:44139346
Keywords
Health care, Health records, Artificial intelligence, Data science, Meaningful use
References
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- The problem of concept drift: definitions and related work
- Best Care at Lower Cost: The Path to Continuously Learning Health Care in America
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- Apprenticeship learning via inverse reinforcement learning
- Redundancy-Aware Topic Modeling for Patient Record Notes
- Clinician blood pressure documentation of stable intensive care patients: an intelligent archiving agent has a higher association with future hypotension
- External validity of randomised controlled trials in asthma: to whom do the results of the trials apply?
- Demystifying trial networks and network meta-analysis
- Unusual brain growth patterns in early life in patients with autistic disorder
- KDIGO Clinical Practice Guidelines for Acute Kidney Injury
- Mortality in Multicenter Critical Care Trials: An Analysis of Interventions With a Significant Effect*
- Biomarkers of AKI: a review of mechanistic relevance and potential therapeutic implications.
- The RIFLE criteria and mortality in acute kidney injury: A systematic review.
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- Semi-supervised Rare Disease Detection Using Generative Adversarial Network
- Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
- Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series
- Deep Survival Analysis: Nonparametrics and Missingness
- How Should AI Be Developed, Validated, and Implemented in Patient Care?
- Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review
- What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
- Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams
- Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
- Large-scale Multi-output Gaussian Processes for Clinical Decision Support
- Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
- Predictive analytics in health care: how can we know it works?
- Representation Learning for Electronic Health Records
- Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health KnowledgeGraph
- AI-Assisted Annotator Using Reinforcement Learning
- Counterfactual diagnosis
- On the Morality of Artificial Intelligence
- Axes of a revolution: challenges and promises of big data in healthcare
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