Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations

Introduction Clinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation...

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Autores: Baykova, E, Raya, I, Lombardía, C, Gonzalvo, B, Andreu, I, Losada, D, Falkenhain, T, Cunill, R, Serrano, D, Rigau, D, Ramírez-Saco, D, López, B, Castells, X
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p29321
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=29321
Access Level:acceso abierto
Palabra clave:Attention defcit hyperactivity disorder (ADHD)
recommendation systems
evidence base for decision making
shared decision making
Artificial intelligence (AI)
patient empowerment
clinical practice guidelines
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spelling Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendationsBaykova, ERaya, ILombardía, CGonzalvo, BAndreu, ILosada, DFalkenhain, TCunill, RSerrano, DRigau, DRamírez-Saco, DLópez, BCastells, XAttention defcit hyperactivity disorder (ADHD)recommendation systemsevidence base for decision makingshared decision makingArtificial intelligence (AI)patient empowermentclinical practice guidelinesIntroduction Clinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation of the GRADE heuristic designed to generate CPG-like treatment recommendations that are automated, updated, personalized, participatory, and explanatory using a symbolic AI approach. TDApp is a clinical decision support system (CDSS) that implements APPRAISE-RS for ADHD.Methods Two clinical trials were conducted. In both studies a total of 33 and 32 ADHD patients, respectively, requiring treatment initiation or a major treatment change were enrolled. TDApp recommendations were compared to those of selected CPGs (American Academy of Pediatrics, National Institute for Health and Care Excellence, Spanish Health System, Canadian ADHD Resource Alliance, and the Australasian ADHD Professionals Association) CPGs. The diversity of treatment recommendations was analyzed using Blau's index. Concordance between TDApp and CPGs recommendations was assessed by calculating the proportion of patients for whom TDApp recommended one drug that was also endorsed by CPGs. Dendrograms were plotted to compare the distance between treatment recommendations as calculated using the NbN nomenclature.Results The first study investigated eight methods that differed in how patient and clinician preferred outcomes were handled and the extent to which TDApp tailored the analysis of evidence. The method deemed most suitable was examined in the second study, which found that 50-75% of the patients received at least one favorable treatment recommendation. TDApp evaluated over 10 drugs, including recently marketed ones, with amphetamine derivatives emerging as the most frequently recommended interventions. TDApp generated 8-12 distinct treatment recommendations with a diversity index of 0.70-0.88, which was higher than those of CPGs. The proportion of patients for whom TDApp recommendations overlapped with at least one drug endorsed by CPGs ranged from 21.9% to 100%. Dendrogram analysis revealed that TDApp was positioned on one side of the tree, while CPGs clustered together on the opposite side.Conclusions TDApp is an advanced prototype of an CDSS offering automated, participatory, personalized, and explanatory treatment recommendations for ADHD. It represents a promising alternative to CPGs for aiding clinicians and patients in shared treatment decision-making.FRONTIERS MEDIA SA2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=29321Frontiers in PsychiatryISSN: 16640640reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p293212026-05-27T12:37:41Z
dc.title.none.fl_str_mv Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
title Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
spellingShingle Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
Baykova, E
Attention defcit hyperactivity disorder (ADHD)
recommendation systems
evidence base for decision making
shared decision making
Artificial intelligence (AI)
patient empowerment
clinical practice guidelines
title_short Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
title_full Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
title_fullStr Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
title_full_unstemmed Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
title_sort Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
dc.creator.none.fl_str_mv Baykova, E
Raya, I
Lombardía, C
Gonzalvo, B
Andreu, I
Losada, D
Falkenhain, T
Cunill, R
Serrano, D
Rigau, D
Ramírez-Saco, D
López, B
Castells, X
author Baykova, E
author_facet Baykova, E
Raya, I
Lombardía, C
Gonzalvo, B
Andreu, I
Losada, D
Falkenhain, T
Cunill, R
Serrano, D
Rigau, D
Ramírez-Saco, D
López, B
Castells, X
author_role author
author2 Raya, I
Lombardía, C
Gonzalvo, B
Andreu, I
Losada, D
Falkenhain, T
Cunill, R
Serrano, D
Rigau, D
Ramírez-Saco, D
López, B
Castells, X
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Attention defcit hyperactivity disorder (ADHD)
recommendation systems
evidence base for decision making
shared decision making
Artificial intelligence (AI)
patient empowerment
clinical practice guidelines
topic Attention defcit hyperactivity disorder (ADHD)
recommendation systems
evidence base for decision making
shared decision making
Artificial intelligence (AI)
patient empowerment
clinical practice guidelines
description Introduction Clinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation of the GRADE heuristic designed to generate CPG-like treatment recommendations that are automated, updated, personalized, participatory, and explanatory using a symbolic AI approach. TDApp is a clinical decision support system (CDSS) that implements APPRAISE-RS for ADHD.Methods Two clinical trials were conducted. In both studies a total of 33 and 32 ADHD patients, respectively, requiring treatment initiation or a major treatment change were enrolled. TDApp recommendations were compared to those of selected CPGs (American Academy of Pediatrics, National Institute for Health and Care Excellence, Spanish Health System, Canadian ADHD Resource Alliance, and the Australasian ADHD Professionals Association) CPGs. The diversity of treatment recommendations was analyzed using Blau's index. Concordance between TDApp and CPGs recommendations was assessed by calculating the proportion of patients for whom TDApp recommended one drug that was also endorsed by CPGs. Dendrograms were plotted to compare the distance between treatment recommendations as calculated using the NbN nomenclature.Results The first study investigated eight methods that differed in how patient and clinician preferred outcomes were handled and the extent to which TDApp tailored the analysis of evidence. The method deemed most suitable was examined in the second study, which found that 50-75% of the patients received at least one favorable treatment recommendation. TDApp evaluated over 10 drugs, including recently marketed ones, with amphetamine derivatives emerging as the most frequently recommended interventions. TDApp generated 8-12 distinct treatment recommendations with a diversity index of 0.70-0.88, which was higher than those of CPGs. The proportion of patients for whom TDApp recommendations overlapped with at least one drug endorsed by CPGs ranged from 21.9% to 100%. Dendrogram analysis revealed that TDApp was positioned on one side of the tree, while CPGs clustered together on the opposite side.Conclusions TDApp is an advanced prototype of an CDSS offering automated, participatory, personalized, and explanatory treatment recommendations for ADHD. It represents a promising alternative to CPGs for aiding clinicians and patients in shared treatment decision-making.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=29321
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=29321
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv FRONTIERS MEDIA SA
publisher.none.fl_str_mv FRONTIERS MEDIA SA
dc.source.none.fl_str_mv Frontiers in Psychiatry
ISSN: 16640640
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
repository.name.fl_str_mv
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