Towards Predicting Smoking Events for Just-in-time Interventions.

Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period.

A curious case of retrogenesis in language: Automated analysis of language patterns observed in dementia patients and young children.

Introduction: While linguistic retrogenesis has been extensively investigated in the neuroscientific and behavioral literature, there has been little work on retrogenesis using computerized approaches to language analysis.

Methods: We bridge this gap by introducing a method based on comparing output of a pre-trained neural language model (NLM) with an artificially degraded version of itself to examine the transcripts of speech produced by seniors with and without dementia and healthy children during spontaneous language tasks. We compare a range of linguistic characteristics including language model perplexity, syntactic complexity, lexical frequency and part-of-speech use across these groups.

[The social hygienic aspects of reproductive behavior of women aged 40-45 years].

In the context of forecasted decreasing of natality, actuality of studying its causes for organizational decision-making increases. The purpose of the study was to determine factors affecting reproductive behavior of women aged 40-45 years residing in areas with different natality levels in 2020-2021. The cohort, analytical, sociological methods were applied.

Useful blunders: Can automated speech recognition errors improve downstream dementia classification?

Objectives: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the "Cookie Theft" picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer's disease (AD).

Methods: We conducted experiments using various ASR models, refining their transcripts with post-editing techniques.