Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study

<b>Objective:</b> Human immunodeficiency virus (HIV) often affects episodic memory. Yet, standard measures of this domain are derived from clinicians' simple counts of recalled and omitted pieces of information, undermining robustness, informativeness, and scalability. Here, we present an automated natural language processing (NLP) approach that tackles such limitations. <b>Methods:</b> We recruited 92 participants (50 people living with HIV and 42 controls), who performed a story retelling task. Using NLP tools, we compared the retellings and the original story in terms of verbosity, semantic acuity, and organizational structure. <b>Results:</b> We found that people living with HIV produced fewer nouns and had poorer semantic acuity and organizational similarity. Moreover, machine learning classifiers robustly differentiated between the two groups. <b>Conclusion:</b> These results suggest that our digital approach can reveal fine-grained episodic memory alterations in people living with HIV, offering an objective, scalable, and cost-effective complement to standard cognitive testing.

Authors

Institutions

Publication Details

Journal
The Clinical Neuropsychologist
Published
2025-08-18
DOI
https://doi.org/10.1080/13854046.2025.2545943
Primary Topic
HIV Research and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study

Agustina Birba, Carolina Gattei, Lucía Amoruso, Lorena Abusamra et al.
The Clinical Neuropsychologist
HIV Research and Treatment
article

Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study

Agustina Birba, Carolina Gattei, Lucía Amoruso, Lorena Abusamra, Valeria Abusamra, Franco Ferrante, Bárbara Sampedro, Gonzalo Nicolás Pérez, Lucas Federico Sterpin, Adolfo M. García, Jeremías Inchauspe, Camilo Avendaño Avello
article en

Abstract

<b>Objective:</b> Human immunodeficiency virus (HIV) often affects episodic memory. Yet, standard measures of this domain are derived from clinicians' simple counts of recalled and omitted pieces of information, undermining robustness, informativeness, and scalability. Here, we present an automated natural language processing (NLP) approach that tackles such limitations. <b>Methods:</b> We recruited 92 participants (50 people living with HIV and 42 controls), who performed a story retelling task. Using NLP tools, we compared the retellings and the original story in terms of verbosity, semantic acuity, and organizational structure. <b>Results:</b> We found that people living with HIV produced fewer nouns and had poorer semantic acuity and organizational similarity. Moreover, machine learning classifiers robustly differentiated between the two groups. <b>Conclusion:</b> These results suggest that our digital approach can reveal fine-grained episodic memory alterations in people living with HIV, offering an objective, scalable, and cost-effective complement to standard cognitive testing.

The Clinical Neuropsychologist
Universidad de Santiago de Chile (CL), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Universidad Torcuato Di Tella (AR), Universidad de Buenos Aires (AR), Centro de Investigación de Métodos Computacionales (AR), Centro Científico Tecnológico - San Juan (AR), Basque Center on Cognition, Brain and Language (ES), Institut des Sciences Cognitives Marc Jeannerod (FR), St. Thomas Hospital (CA), Instituto de Filosofía (ES), Hospital Británico de Buenos Aires (AR), Global Brain Health Institute (US)
Alzheimer's Association, Agencia Nacional de Promoción Científica y Tecnológica
Quality Education
Openalex Percentile: Top 77%
HIV Research and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.