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Found 11 papers, 4 papers with code
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains
Medical imaging data is often siloed within hospitals, limiting the amount of data available for specialized model development.
Business Metric-Aware Forecasting for Inventory Management
We explore a wide range of plausible cost trade-off scenarios, and empirically demonstrate that end-to-end optimization often outperforms optimization of standard business-agnostic forecasting metrics (by up to 45. 7% for a simple scaling model, and up to 54. 0% for an LSTM encoder-decoder model).
Evaluating Model Performance in Medical Datasets Over Time
Machine learning (ML) models deployed in healthcare systems must face data drawn from continually evolving environments.
Model Evaluation in Medical Datasets Over Time
Machine learning models deployed in healthcare systems face data drawn from continually evolving environments.
Domain Adaptation under Missingness Shift
Rates of missing data often depend on record-keeping policies and thus may change across times and locations, even when the underlying features are comparatively stable.
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
With COVID-19 now pervasive, identification of high-risk individuals is crucial.
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatment options.
Digital Stylometry: Linking Profiles Across Social Networks
There is an ever growing number of users with accounts on multiple social media and networking sites.
Enhanced Twitter Sentiment Classification Using Contextual Information
This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.
