Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach (Preprint)

Understanding reminiscence and its negative functions in the everyday conversations of young adults: A machine learning approach

Reminiscence is the act of recalling or telling others about relevant personal past experiences. It is an important activity for all individuals, young and old alike. In fact, reminiscence can serve different functions that can support or be detrimental to one's well-being. Although previous studies have extensively investigated older adults' recalling of autobiographical memories, the evidence for young adults remains scarce. Therefore, in this work, we analyze young adults' production of reminiscence and their functions with a naturalistic observation method. Furthermore, we demonstrate that natural language processing and machine learning can automatically detect reminiscence and its negative functions in young adults' everyday conversations. We interpret machine learning model results using Shapley explanations. Our results indicate that young adults reminisce in everyday life mostly to connect with others through conversation, to compensate for a lack of stimulation or to recall difficult past experiences. Moreover, our models improve existing benchmarks from the literature on the automated detection of older adults' reminiscence in everyday life. Finally, our results may support the development of digital health intervention programs that detect reminiscence and its functions in young adults to support their well-being.

Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings

OBJECTIVES

Patient-clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients' and nurses' speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language.

MATERIALS AND METHODS

Pilot studies were conducted at VNS Health, the largest not-for-profit home healthcare agency in the United States, to optimize audio-recording patient-nurse interactions. We recorded and transcribed 46 interactions, resulting in 3494 "utterances" that were annotated to identify the speaker. We employed natural language processing techniques to generate linguistic features and built various ML classifiers to distinguish between patient and nurse language at both individual and encounter levels.

RESULTS

A support vector machine classifier trained on selected linguistic features from term frequency-inverse document frequency, Linguistic Inquiry and Word Count, Word2Vec, and Medical Concepts in the Unified Medical Language System achieved the highest performance with an AUC-ROC = 99.01 ± 1.97 and an F1-score = 96.82 ± 4.1. The analysis revealed patients' tendency to use informal language and keywords related to "religion," "home," and "money," while nurses utilized more complex sentences focusing on health-related matters and medical issues and were more likely to ask questions.

CONCLUSION

The methods and analytical approach we developed to differentiate patient and nurse language is an important precursor for downstream tasks that aim to analyze patient speech to identify patients at risk of disease and negative health outcomes.

Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS)

Prominent theories of aging emphasize the importance of resource allocation processes as a means to maintain functional ability, well-being and quality of life. Little is known about which activities and what activity patterns actually characterize the daily lives of healthy older adults in key domains of functioning, including the spatial, physical, social, and cognitive domains. This study aims to gain a comprehensive understanding of daily activities of community-dwelling older adults over an extended period of time and across a diverse range of activity domains, and to examine associations between daily activities, health and well-being at the within- and between-person levels. It also aims to examine contextual correlates of the relations between daily activities, health, and well-being. At its core, this ambulatory assessment (AA) study with a sample of 150 community-dwelling older adults aged 65 to 91 years measured spatial, physical, social, and cognitive activities across 30 days using a custom-built mobile sensor ("uTrail"), including GPS, accelerometer, and audio recording. In addition, during the first 15 days, self-reports of daily activities, psychological correlates, contexts, and cognitive performance in an ambulatory working memory task were assessed 7 times per day using smartphones. Surrounding the ambulatory assessment period, participants completed an initial baseline assessment including a telephone survey, web-based questionnaires, and a laboratory-based cognitive and physical testing session. They also participated in an intermediate laboratory session in the laboratory at half-time of the 30-day ambulatory assessment period, and finally returned to the laboratory for a posttest assessment. In sum, this is the first study which combines multi-domain activity sensing and self-report ambulatory assessment methods to observe daily life activities as indicators of functional ability in healthy older adults unfolding over an extended period (i.e., 1 month). It offers a unique opportunity to describe and understand the diverse individual real-life functional ability profiles characterizing later life.