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Zina Ibrahim - Big Data in Mental Health - 23rd July 2014
- 1. Multi-agent Systems for Automating Large Naturalistic Treatment Trials in Routine Practice Zina Ibrahim
- 2. 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.
- 3. 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.
- 4. 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
- 5. 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
- 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.
- 7. ❏ 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
- 8. ❏ 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
- 10. ❏ 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
- 11. Multi-Agent Systems for Generating Treatment Trials
- 13. A Month after last SDQ is entered Weekly reminders until user signs up and fills the next SDQ form. Repeat for six SDQs
- 14. youthinmind.info
- 15. 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.
- 16. Acknowledgments http://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)