Multi-agent Systems for Automating Large Naturalistic Treatment Trials in Routine Practice

Zina Ibrahim

Motivating Research❏ By: Lorena Fernández de la Cruz, Argyris Stringaris, Robert Goodman and

others at the Department of Child and Adolescent Psychiatry, Institute of Psychiatry, King’s College London.

❏ Aim: Conduct large naturalistic treatment trials on ADHD ❏ Test the effectiveness of ADHD treatments in routine clinical settings❏ Establish the effectiveness of ADHD treatments for the subgroup of

children with mood dysregulation.

❏ Assessment Variable: Strength and Difficulties Questionnaire clinical outcome measure (SDQ) scores in the de-identified electronic health records.

The Strengths and Difficulties Questionnaire (SDQ)

❏ A brief child mental health questionnaire for children and adolescents ages 2 through 17 years old, developed by Dr. Robert Goodman.

❏ Measures 25 attributes, some positive and others negatives.❏ The 25 attributes fall into the following 4 scales:

❏ Emotional Symptoms (5 items)❏ Conduct problems (5 items)❏ Hyperactivity/inattention (5 items)❏ Peer relationship problems (5 items)❏ Prosocial behaviour (5 items)

❏ The patient is given a score for each attribute. The total of all the scores provides the SDQ score of the patient.

Outcome of Initial Research

❏ Excellent baseline SDQ

data available

❏ Baseline SDQ scores

predictive of diagnosis

outcome

Number of EPJS Records for ADHD Cases 8,434

Gender (% Female) 44%

Age: M (SD) 11.2 (3.8)

Records with SDQ 6, 912

Problem: No Treatment Outcome Measures

❏ Apart from the initial diagnosis SDQ, few patients have repeated SDQ measures, collected at subsequent intervals, to assess treatment outcomes.

Subsample of ADHD individuals with at least 2 SDQ

N:157 Age: M(13) Gender: 82% F

Medication: 148 valid, 9 missing

Medication N %

Methylphenidate 137 87.3

Dextroamphetamine 1 0.6

Atomoxetine 29 18.5

Clonidine 9 5.7

Number of Meds N %

1 121 77.1

2 26 16.6

3 1 0.6

Solution: Automatic Collection of Outcome Measures

❏ Aims: ❏ Improve the quality of treatment outcome measure (SDQ)

collection in EPJS (and subsequently CRIS). ❏ Engage with patients through online web resources.

❏ Objectives: ❏ Automate clinical trial feedback without clinicians’ intervention.❏ Provide patients with detailed analyses based on their SDQ

scores. The analyses provide books, helpful hints and link to help them better understand their situations.

❏ Description: build an automated computer system which: ❏ Regularly queries EPJS for newly-created first (baseline) SDQ entries❏ Creates virtual agents (autonomous software components) for every

SDQ entry found. Every agent will: ❏ Generate a personalised guidance report using youthinmind.info based on

the SDQ entries❏ Make the following available on an online web resource:

❏ The personalised guidance report❏ Forms for filling new SDQ entries

Solution: Automatic Collection of Outcome Measures

❏ Create a monthly follow-up schedule for the case. Every month the virtual agent will:

❏ Monitor adherence to the follow-up schedule by regularly sending reminders to participants to complete the web form until one is filled

❏ Once filled, the agent sends the new SDQ entries for the case from the web resource to EPJS

❏ Generate a new personalised guidance report based on the new entries (from youthinmind.info)

Solution: Automatic Collection of Outcome Measures

❏ A Software Agent: is a computer program which:❏ Acts autonomously on behalf of its user❏ Acts proactively to achieve a predefined goal❏ Reacts to input from the changing environment❏ Is social, i.e. it communicates with other agents

❏ A Multi-agent system: computer system made of a number of software agents jointly interacting to achieve the design requirements of the overall system.

Multi-Agent Systems

Multi-Agent Systems for Generating Treatment Trials

Overall System Architecture

A Month after last SDQ is entered

Weekly reminders until user signs up and fills the next SDQ form.

Repeat for six SDQs

youthinmind.info

Implementation and Progress

❏ Implementation Details: ❏ JADE (Java-based Agent Development Environment) Java-based environment

which interacts with the user through Servlets and JSP pages. ❏ Prototype developed based on the EPJS Testing database

❏ Development Progress:❏ Core functionalities 95% complete❏ Currently working on:

❏ Parsing patient/informer e-mail addresses from free-text data❏ Interacting with the users using text messaging for reminders and alerts. ❏ Designing a (good looking) web interface.

❏ Post-development Phase❏ Acquisition of permissions for deployment over EPJS.

Acknowledgmentshttp://core.brc.iop.kcl.ac.uk

Dr Richard J Dobson (BRC)Dr Lorena Fernandez de la Cruz (IoP)Dr Argyris Stringaris(IoP)Prof. Robert Goodman (IoP)Prof. Emily Siminoff (IoP)Prof. Andrew Pickles (IoP)Dr Matthew Broadbent (BRC)Dr Caroline Johnston (BRC)Dr Amos Folarin (BRC)John Turp (SLAM)