Machine learning in health informatics: making better use of domain experts
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Summary
The immediate aim in this work is to reduce the workload involved in conducting systematic reviews, and it is demonstrated that the developed methods can reduce reviewer workload by more than half, without sacrificing the comprehensiveness of reviews.
- Type
- article
- Published
- 2012-01-01
- Cited by
- 0
- References
- 114
- OpenAlex
- https://openalex.org/W49552497
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:276080453
Keywords
Computer science, Machine learning, Artificial intelligence, Workload, Domain (mathematical analysis)
References
- Identifying and Eliminating Mislabeled Training Instances
- The Foundations of Cost-Sensitive Learning
- Reducing workload in systematic review preparation using automated citation classification.
- Handling imbalanced datasets: A review
- The analysis of case-control studies
- Active Learning and Crowd-Sourcing for Machine Translation
- Selective Sampling Using the Query by Committee Algorithm
- Machine Learning from Imbalanced Data Sets 101
- A Brief Introduction to Boosting
- Text Categorization with Support Vector Machines: Learning with Many Relevant Features
- Criteria for determining disability in infants and children: low birth weight.
- The Comparison and Evaluation of Forecasters.
- Multilevel and Longitudinal Modeling Using Stata
- Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
- Get another label? improving data quality and data mining using multiple, noisy labelers
- The class imbalance problem: A systematic study
- Mining with rarity: a unifying framework
- Learning Regular Sets from Queries and Counterexamples
- Supervised learning from multiple experts: whom to trust when everyone lies a bit
- Transforming classifier scores into accurate multiclass probability estimates
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