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Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data
INTRODUCTION: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA). Mining these complex data for clinically relevant patt...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096320/ https://www.ncbi.nlm.nih.gov/pubmed/30135781 http://dx.doi.org/10.1016/j.invent.2018.03.003 |
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author | Rocha, Artur Camacho, Rui Ruwaard, Jeroen Riper, Heleen |
author_facet | Rocha, Artur Camacho, Rui Ruwaard, Jeroen Riper, Heleen |
author_sort | Rocha, Artur |
collection | PubMed |
description | INTRODUCTION: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA). Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP) data mining approach, using combined trial data of the EU E-COMPARED depression trial. METHODS: We explored the capability of ILP to handle and combine (implicit) multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1) Time reasoning is involved; 2) there is a reasonable amount of explicit useful relations to be analyzed; 3) ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4) both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity), and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks. RESULTS: E-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much) slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA), PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories. DISCUSSION: This work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically meaningful predictors of system use and treatment outcome. Strengths and limitations of the ILP approach given this objective will be discussed. |
format | Online Article Text |
id | pubmed-6096320 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-60963202018-08-22 Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data Rocha, Artur Camacho, Rui Ruwaard, Jeroen Riper, Heleen Internet Interv Special issue for the ISRII 2017 meeting INTRODUCTION: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA). Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP) data mining approach, using combined trial data of the EU E-COMPARED depression trial. METHODS: We explored the capability of ILP to handle and combine (implicit) multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1) Time reasoning is involved; 2) there is a reasonable amount of explicit useful relations to be analyzed; 3) ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4) both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity), and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks. RESULTS: E-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much) slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA), PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories. DISCUSSION: This work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically meaningful predictors of system use and treatment outcome. Strengths and limitations of the ILP approach given this objective will be discussed. Elsevier 2018-03-13 /pmc/articles/PMC6096320/ /pubmed/30135781 http://dx.doi.org/10.1016/j.invent.2018.03.003 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Special issue for the ISRII 2017 meeting Rocha, Artur Camacho, Rui Ruwaard, Jeroen Riper, Heleen Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
title | Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
title_full | Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
title_fullStr | Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
title_full_unstemmed | Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
title_short | Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
title_sort | using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data |
topic | Special issue for the ISRII 2017 meeting |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096320/ https://www.ncbi.nlm.nih.gov/pubmed/30135781 http://dx.doi.org/10.1016/j.invent.2018.03.003 |
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