Year: 2021
Type: review
Abstract: Digital health data are multimodal and high-dimensional. A patient's health state can be characterized by a multitude of signals including medical imaging, clinical variables, genome sequencing, conversations between clinicians and patients, and continuous signals from wearables, among others. This high volume, personalized data stream aggregated over patients' lives has spurred interest in developing new artificial intelligence (AI) models for higher-precision diagnosis, prognosis, and tracking. While the promise of these algorithms is undeniable, their dissemination and adoption have been slow, owing partially to unpredictable AI model performance once deployed in the real world. We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting-their high-dimensional nature. This paper considers how the large number of features in vast digital health data can challenge the development of robust AI models-a phenomenon known as "the curse of dimensionality" in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algorithm designers.
Source: npj Digital Medicine
Institution: Arizona State University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,060
Awards: AN INFORMATION-THEORETIC APPROACH TO IMPROVING THE ROBUSTNESS OF DEEP LEARNING ARCHITECTURES, G4111788909, G4504108201, CAREER: Learning and Leveraging the Structure of Large Graphs: Novel Theory and Algorithms, G6500814593, Perception of dysarthric speech: An objective model of dysarthric speech evaluation with actionable outcomes, Graphical Models from Partially Observed Interactions with Biomedical Applications, G8394787047, G8876996369, G8929337198, G9000093548
OpenAlex ID: W3208504364