Accelerating automation of digital health applications via cloud native approach

Wen, Bo; Koyfman, Yan; Tian, Hongfei; Lublinsky, B.; Norel, Raquel; Agurto, Carla; Wampler, Dean; Albarracin, Narciso

doi:10.1145/3565384.3565888

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Participants performed the ECAS test and the picture description tasks using the modality.ai platform. Recordings from the participants performing the picture description task were saved in WAV format with a sampling rate of 44.1kHz, and were processed to extract different types of features using the protocol described in [12]. To perform a thorough characterization of the description, we summarized the acoustic information of the recording using features that focus on prosody, voice quality, noise measurements, and other aspects of the voice using Python [13] and Praat [14], [15].…”

Section: B Approachmentioning

confidence: 99%

Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description

Agurto,

Cecchi,

Wen

et al. 2023

2023 IEEE International Conference on Digital Health (ICDH)

Self Cite

View full text Add to dashboard Cite

Amyotrophic lateral sclerosis (ALS) is a fatal disease that affects not only movement, speech, and breathing but also cognition. Recent studies have focused on the use of language analysis techniques to detect ALS and infer scales for monitoring functional progression. This paper focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population. In an effort to reach the ALS population, which frequently exhibits mobility limitations, we implemented the digital version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) test for the first time. This test, designed to measure cognitive impairment, was remotely performed by 56 participants from the EverythingALS Speech Study 1 . As part of the study, participants (ALS and non-ALS) were asked to describe weekly one picture from a pool of many pictures with complex scenes displayed on their computer at home. We analyze the descriptions performed within +/-60 days from the day the ECAS test was administered and extract different types of linguistic and acoustic features. We input those features into linear regression models to infer 5 ECAS sub-scores and the total score. Speech samples from the picture description are reliable enough to predict the ECAS subs-scores, achieving statistically significant Spearman correlation values between 0.32 and 0.51 for the model's performance using 10-fold cross-validation.

show abstract

Section: B Approachmentioning

confidence: 99%