ABSTRACT
Objective: Although studies have shown that digital measures of speech detected ALS speech impairment and correlated with the ALSFRS-R speech item, no study has yet compared their performance in detecting speech changes. In this study, we compared the performances of the ALSFRS-R speech item and an algorithmic speech measure in detecting clinically important changes in speech. Importantly, the study was part of a FDA submission which received the breakthrough device designation for monitoring ALS; we provide this paper as a roadmap for validating other speech measures for monitoring disease progression. Methods: We obtained ALSFRS-R speech subscores and speech samples from participants with ALS. We computed the minimum detectable change (MDC) of both measures; using clinician-reported listener effort and a perceptual ratings of severity, we calculated the minimal clinically important difference (MCID) of each measure with respect to both sets of clinical ratings. Results: For articulatory precision, the MDC (.85) was lower than both MCID measures (2.74 and 2.28), and for the ALSFRS-R speech item, MDC (.86) was greater than both MCID measures (.82 and .72), indicating that while the articulatory precision measure detected minimal clinically important differences in speech, the ALSFRS-R speech item did not. Conclusion: The results demonstrate that the digital measure of articulatory precision effectively detects clinically important differences in speech ratings, outperforming the ALSFRS-R speech item. Taken together, the results herein suggest that this speech outcome is a clinically meaningful measure of speech change.
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.
