Publicaties

Op deze pagina is een overzicht te vinden van (wetenschappelijke) publicaties die zijn voortgekomen uit het Living Lab Data Supported Healthcare & Innovation (DSHI). De publicaties hieronder weerspiegelen deze samenwerking en variëren van onderzoek naar AI-geletterdheid tot de praktische inzet van AI-toepassingen binnen zorgorganisaties.

Wetenschappelijke publicaties

2026

Hulsen, Tim; Scheper, Mark C.; Pauws, Steffen C.

GenAI in Healthcare: Technologies, Applications and Evaluation Tijdschriftartikel

In: Frontiers in Artificial Intelligence, vol. 9, nr. 1878137, 2026.

Abstract | Links | BibTeX

Scheper, Mark C

Rethinking treatment effectiveness in the context of personalized care and environmental health Tijdschriftartikel

In: vol. 65, nr. 5, 2026, ISSN: 1462-0332.

Links | BibTeX

Steijger, Dirk; Scheper, Mark C; van der Willigen, Robert Frans; Christie, Hannah; de Vugt, Marjolein E; Verbeek, Hilde; Aarts, Sil

Quality of Life of People Living With Dementia Residing in Nursing Homes: Secondary Analysis of Observational Data Tijdschriftartikel

In: J Med Internet Res, vol. 28, pp. e91968–e91968, 2026, ISSN: 1438-8871.

Abstract | Links | BibTeX

@article{Steijger2026,

title = {Quality of Life of People Living With Dementia Residing in Nursing Homes: Secondary Analysis of Observational Data},

author = {Dirk Steijger and Mark C Scheper and Robert Frans van der Willigen and Hannah Christie and Marjolein E de Vugt and Hilde Verbeek and Sil Aarts},

doi = {10.2196/91968},

issn = {1438-8871},

year  = {2026},

date = {2026-09-15},

journal = {J Med Internet Res},

volume = {28},

pages = {e91968--e91968},

publisher = {JMIR Publications Inc.},

abstract = {Abstract

                  

                    Background

                    Quality of life (QoL) plays a crucial role in dementia care; however, QoL and its dynamic, context-dependent nature can be difficult to capture among people living with dementia due to challenges in memory and communication, and limitations of self-reported QoL instruments. Observational tools such as the Maastricht Electronic Daily Life Observation (MEDLO) provide narrative descriptions of the daily life of people living with dementia in nursing homes. However, the MEDLO tool was not developed to assess QoL specifically, and it remains unclear to what extent its narrative descriptions reflect aspects of QoL. Analyzing these narrative descriptions is labor-intensive and time-consuming. Recent advances in natural language processing, including large language models (LLMs), offer the potential to analyze these narrative descriptions at scale.

                  

                  

                    Objective

                    The study aims to explore whether an LLM can be used to structure existing MEDLO narrative data into interpretable QoL-relevant patterns in people living with dementia. Specifically, this study examines whether N-gram analysis, sentiment analysis, and LLM-based topic modeling can identify recurring language patterns, emotional tone, and semantic clusters that can be mapped to Lawton QoL domains.

                  

                  

                    Methods

                    This study conducted a secondary analysis of existing MEDLO observational data from 151 people living with dementia residing in Dutch long-term care. Narrative data had been documented by trained observers, describing activities, interactions, settings, and emotional expressions. For analysis, a local secure pipeline was developed in which GPT-4o-mini was deployed. The pipeline comprised three analytical steps: (1) N-gram frequency analysis, (2) sentiment analysis, and (3) topic modeling. Prompts were iteratively refined through prompt engineering. Coauthors and domain experts reviewed outputs for coherence, contextual plausibility, and relevance to long-term care practice.

                  

                  

                    Results

                    

                      A total of 5622 narratives (50,106 words) from 151 people living with dementia were analyzed. The narratives were short, averaging 10.5 (SD 5.80) words per narrative. N-gram frequency analysis identified the frequent documentation of passive activities (

                      sits at the table

                      ) in limited indoor settings (

                      living room

                      ). Emotional well-being was often described in positive terms (

                      smiles

                      and

                      laughs

                      ), whereas explicitly negative expressions (

                      cries

                      and

                      distress

                      ) occurred less frequently. Weighted sentiment analysis showed that, although fewer in number, negative expressions carried a stronger intensity, resulting in an overall predominance of negative sentiment across all QoL domains. Topic modeling identified 8 coherent clusters, most of which mapped onto multiple QoL domains, underscoring QoL’s multidimensionality.

                    

                  

                  

                    Conclusions

                    LLM-based analyses identified predominantly passive activities with little variation in indoor settings, while people living with dementia were often described as having positive affect. This exploratory study suggests that LLM-based analyses may help structure observational narratives into QoL-relevant patterns, but further validation is needed before such outputs can inform person-centered care practice.

                  },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Sluiten

Abstract

Background
Quality of life (QoL) plays a crucial role in dementia care; however, QoL and its dynamic, context-dependent nature can be difficult to capture among people living with dementia due to challenges in memory and communication, and limitations of self-reported QoL instruments. Observational tools such as the Maastricht Electronic Daily Life Observation (MEDLO) provide narrative descriptions of the daily life of people living with dementia in nursing homes. However, the MEDLO tool was not developed to assess QoL specifically, and it remains unclear to what extent its narrative descriptions reflect aspects of QoL. Analyzing these narrative descriptions is labor-intensive and time-consuming. Recent advances in natural language processing, including large language models (LLMs), offer the potential to analyze these narrative descriptions at scale.

Objective
The study aims to explore whether an LLM can be used to structure existing MEDLO narrative data into interpretable QoL-relevant patterns in people living with dementia. Specifically, this study examines whether N-gram analysis, sentiment analysis, and LLM-based topic modeling can identify recurring language patterns, emotional tone, and semantic clusters that can be mapped to Lawton QoL domains.

Methods
This study conducted a secondary analysis of existing MEDLO observational data from 151 people living with dementia residing in Dutch long-term care. Narrative data had been documented by trained observers, describing activities, interactions, settings, and emotional expressions. For analysis, a local secure pipeline was developed in which GPT-4o-mini was deployed. The pipeline comprised three analytical steps: (1) N-gram frequency analysis, (2) sentiment analysis, and (3) topic modeling. Prompts were iteratively refined through prompt engineering. Coauthors and domain experts reviewed outputs for coherence, contextual plausibility, and relevance to long-term care practice.

Results

A total of 5622 narratives (50,106 words) from 151 people living with dementia were analyzed. The narratives were short, averaging 10.5 (SD 5.80) words per narrative. N-gram frequency analysis identified the frequent documentation of passive activities (
sits at the table
) in limited indoor settings (
living room
). Emotional well-being was often described in positive terms (
smiles
and
laughs
), whereas explicitly negative expressions (
cries
and
distress
) occurred less frequently. Weighted sentiment analysis showed that, although fewer in number, negative expressions carried a stronger intensity, resulting in an overall predominance of negative sentiment across all QoL domains. Topic modeling identified 8 coherent clusters, most of which mapped onto multiple QoL domains, underscoring QoL’s multidimensionality.

Conclusions
LLM-based analyses identified predominantly passive activities with little variation in indoor settings, while people living with dementia were often described as having positive affect. This exploratory study suggests that LLM-based analyses may help structure observational narratives into QoL-relevant patterns, but further validation is needed before such outputs can inform person-centered care practice.

Sluiten

2025

van Velzen, Mark; van der Willigen, Robert F.; de Beer, Vincent J.; de Graaf-Waar, Helen I.; Janssen, Esther R. C.; van Leeuwen, Sjemaine; van der Willigen, Micha F.; van der Willigen, Martijn J.; Renardus, Gavin; Maaroufi, Rayan El; Satimin, Sven J.; Hartog, Larissa M.; Hulsen, Tim; van Meeteren, Nico L. U.; Scheper, Mark C.

Privacy-, linguistic-, and information-preserving synthesis of clinical documentation through generative agents Tijdschriftartikel

In: Frontiers in Artificial Intelligence, vol. 8, nr. 1644084, 2025, ISSN: 2624-8212.

Abstract | Links | BibTeX

Hulsen, Tim; Scheper, Mark

AI in Healthcare: Do Not Forget About Allied Healthcare Tijdschriftartikel

In: AI, vol. 6, nr. 6, 2025, ISSN: 2673-2688.

Abstract | Links | BibTeX

Wetenschappelijke publicaties

2026

2025

Online publicaties