Search Articles

View query in Help articles search

Search Results (1 to 2 of 2 Results)

Download search results: CSV END BibTex RIS


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

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

Similarly, Yordanova et al [60] used RF algorithms to detect social behavior from transcripts of daily conversations. We provide information on the experimental setting by describing the (1) machine learning runs (R, when reported with run number), (2) repeated cross-validation routine, (3) recursive feature elimination (RFE) algorithm, (4) hyperparameters in the cross-validation, and (5) the evaluation metrics of the machine learning models.

Andrea Ferrario, Minxia Luo, Angelina J Polsinelli, Suzanne A Moseley, Matthias R Mehl, Kristina Yordanova, Mike Martin, Burcu Demiray

JMIR Aging 2022;5(1):e28333

Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Yordanova et al [24] were the first to investigate the applicability of NLP and machine learning methodologies on data from a naturalistic observation study by Demiray et al [6]; they introduced an NLP pipeline and machine learning routines to automatically code the social behaviors and interactions (eg, talking to a partner or daughter/son, giving advice, receive support, etc) in the transcripts of recorded conversations.

Andrea Ferrario, Burcu Demiray, Kristina Yordanova, Minxia Luo, Mike Martin

J Med Internet Res 2020;22(9):e19133