Automl to date and beyond: Challenges and opportunities
As big data becomes ubiquitous across domains, and more and more stakeholders aspire to
make the most of their data, demand for machine learning tools has spurred researchers to …
make the most of their data, demand for machine learning tools has spurred researchers to …
Machine learning for real-time aggregated prediction of hospital admission for emergency patients
Abstract Machine learning for hospital operations is under-studied. We present a prediction
pipeline that uses live electronic health-records for patients in a UK teaching hospital's …
pipeline that uses live electronic health-records for patients in a UK teaching hospital's …
Recent advances in domain-driven data mining
Data mining research has been significantly motivated by and benefited from real-world
applications in novel domains. This special issue was proposed and edited to draw attention …
applications in novel domains. This special issue was proposed and edited to draw attention …
The machine learning bazaar: Harnessing the ml ecosystem for effective system development
As machine learning is applied more widely, data scientists often struggle to find or create
end-to-end machine learning systems for specific tasks. The proliferation of libraries and …
end-to-end machine learning systems for specific tasks. The proliferation of libraries and …
The technological emergence of automl: A survey of performant software and applications in the context of industry
With most technical fields, there exists a delay between fundamental academic research and
practical industrial uptake. Whilst some sciences have robust and well-established …
practical industrial uptake. Whilst some sciences have robust and well-established …
Diving into AutoML in Medical Imaging: Solution for Non-ML Practitioners
Within the healthcare sector, deploying Machine Learning (ML) models involves trial-and-
error approaches, considerable time to create task-specific models, and collaboration …
error approaches, considerable time to create task-specific models, and collaboration …
Cardea: An open automated machine learning framework for electronic health records
An estimated 180 papers focusing on deep learning and EHR were published between
2010 and 2018. Despite the common workflow structure appearing in these publications, no …
2010 and 2018. Despite the common workflow structure appearing in these publications, no …
Toward a progress indicator for machine learning model building and data mining algorithm execution: a position paper
G Luo - Acm Sigkdd Explorations Newsletter, 2017 - dl.acm.org
For user-friendliness, many software systems offer progress indicators for long-duration
tasks. A typical progress indicator continuously estimates the remaining task execution time …
tasks. A typical progress indicator continuously estimates the remaining task execution time …
AutoML for shape-writing biometrics
L Weber, LA Leiva - Neural Computing and Applications, 2025 - Springer
Shape-writing is a text entry method that allows users to type words on mobile devices by
gliding their finger across the keyboard from one character to the next. This creates a …
gliding their finger across the keyboard from one character to the next. This creates a …
[HTML][HTML] A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling
G Luo - Global transitions, 2019 - Elsevier
Predictive modeling based on machine learning with medical data has great potential to
improve healthcare and reduce costs. However, two hurdles, among others, impede its …
improve healthcare and reduce costs. However, two hurdles, among others, impede its …