About this Research Topic
Artificial intelligence and machine learning systems have shown great success in digesting massive amounts of data. Healthcare and clinical practice are areas in which large datasets are often acquired for individual patients in the form of electronic health records. The diversity of data in electronic health records can range from time series of vital parameters of patients, irregularly-sampled information about drug administration, to imaging data of different modalities. The large volume and high dimensionality of this data make this domain a prime target for machine learning research. Methods developed in the context of clinical machine learning ought to, primarily, improve patient welfare by detecting novel biomarkers for complex syndromes such as sepsis or circulatory failure, and assist doctors in their daily routine.
Clinical data presents many idiosyncratic challenges that must be addressed and overcome for machine learning models to perform well. An example of these challenges is the need to consider measurements that are sampled at irregular time intervals, as is the case of data recorded in the intensive care unit. Proper handling of this data requires special choices of the predictive models that will be subsequently used. Other obstacles include the inability to transfer models between different hospital sites due to differences in calibration of acquisition equipment or measurement modalities. Additionally, if one focuses solely on classification tasks, differences in the prevalence of labeled examples may exacerbate the difficulty to compare models.
This collection comprises articles on the topic of applications of machine learning to healthcare, including contributions from speakers and organizers of the “Clinical Machine Learning” track at the Applied Machine Learning Days conference in March of 2021 (AMLD 2021). The track brought together practitioners and researchers to showcase state-of-the-art machine learning models in clinical practice.
Manuscripts in this collection are expected to place particular emphasis on discussions about the use of machine learning for prospective studies. The goal is to describe existing success stories that are already out there and to catalogue the lessons to be learned from successful, and/or failed implementations. Additional aspects to consider comprise ethics, legal discussions, and many more. The aim is to provide a collection of articles that both stimulates the discussion of these aspects and provides insights into the future of the field.
Keywords: machine learning, artificial intelligence, deep learning, representation learning, healthcare, critical care medicine, computational biology, clinical informatics, biomedical data analysis, electronic health records, medical image analysis, computer visio
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