Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

Artur Rocha; Rui Camacho; Jeroen Ruwaard; Heleen Riper

doi:10.1016/j.invent.2018.03.003

Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

Internet Interv. 2018 Mar 13:12:176-180. doi: 10.1016/j.invent.2018.03.003. eCollection 2018 Jun.

Authors

Artur Rocha¹, Rui Camacho², Jeroen Ruwaard³, Heleen Riper³

Affiliations

¹ INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.
² DEI & Faculdade de Engenharia & LIAAD-INESC TEC, Universidade do Porto, Portugal.
³ Vrije Universiteit Amsterdam, Department of Clinical Psychology, De Boelelaan 1081, Amsterdam 1081 HV, Netherlands.

Abstract

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.

Keywords: Ecological momentary assessment; Internet intervention; Log data; Moodbuster; Multi-relational data mining.