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Unlocking the evidence from electronic patient records for smart intervention of mental health disorders
– a case study in Alzheimer’s Disease
University of Oxford, Department of Psychiatry
Dr Chi-Hun Kim (Clinician and medical researcher)
Dr Alejo Nevado-Holgago (Machine learning and data analytics)
Dr Andrey Kormitlizin (Machine learning and data analytics)
University of Manchester, School of Computer Science
Prof Goran Nenadic (Computer Science, text analytics)
Dr Azad Dehghan (Text analytics, also working at DeepCognito)
Overview
•Project outline
•Project progress
•Future development
Project outline
• Mental health Electronic Patient Records (EPR) are underutilised
Problem
• Text mining algorithms
• Bespoke EPR data analytics
Challenges
• Smarter intervention for dementia patients
Outcomes
UK-CRIS: largest mental health EPR network
Clinical Record Interactive Search
(CRIS) system
– De-identify EPR database
– Structured and free text data
UK-CRIS network
– 10 Mental Health NHS Trusts
(total 2M patients)
– 100K-200K dementia patients
– Free and easy access
Why Text Mining for Mental Health ERP(CRIS)
Structured Free text (e.g. letters)
Diagnosis ICD-10 code (F00-03…) More detailed
Symptoms/Events Not available Present
Test results Limited (Cluster/HoNOS) MMSE, MOCA, Brain imaging etc
Medication Limited Present
Intervention Limited CBT, education etc
Name: ZZZZZ
MMSE
-score: 25/30
-date: 20140714
Medication
-Donepezil, 10mg/day
-S/E: nausea, weight loss
Diagnosis
-Alzheimer’s disease
Mr ZZZZZ has 2 years history of short term memory
decline which has worsened since his admission to
hospital in July 2014. His MMSE on 14.7.2014 was
25/30 and today was 22/30. Brain CT and all screening
bloods are normal….He is very anxious about his
Alzheimers and experienced severe nausea and
weight loss after taking donepezil 10 mg per day...
Manual vs. Automatic?
Grand challenges
Text mining – Contextualisation of information – Temporal ordering – Generalizability across trusts
EPR data analytics – Large and complex
• Free text + Structured • Missing, Biased • Live, Real world
– Epidemiology + Machine learning (Deep learning)
Diagnosis Demographics Symptom Medication/Lab
Shah et al 2014
Our grand vision
Case scenarios:
EPS
research
challenges
Robust text mining • Various mental health
conditions • Application across
multiple Trusts
Tailored EPR data analytics • Population- and personal-
level predictive models
Health
outcomes
framework
Smarter intervention • Stratify subgroups of dementia patients • Identify the right intervention at the right timing • Medical decision support • Patient summary dashboard
Feasibility funding proposal
• Single site
– Oxford UK-CRIS database (100K patients)
• Population-level hypothesis
– Impact of anti-inflammatory drug use on the cognitive function of patients with Alzheimer’s disease
• Focus on dementia related drugs, test scales and diagnoses
– Steps/Deliverables: 1) Human generated reference dataset
2) Text mining algorithms
3) Apply to the whole Oxford CRIS database
4) Integration of free text and structured information
Overview
•Project outline
•Project progress
•Future development
Work packages
Task
No.
Description Deliverables Timeline
2017 2018
Progress
(%)
3 4 5 6 7 8 9 10 11 12 1 2
1 Manual
annotation of
EPR free text
Annotation guideline -
Gold-standard
annotated dataset
2 Text mining:
algorithm
development
Open-source text
mining tools for
dementia research
3 Text mining:
application
Text mining derived
values from the whole
Oxford CRIS data
4 Data analysis Publication
Work packages
Task
No.
Description Deliverables Timeline
2017 2018
Progress
(%)
3 4 5 6 7 8 9 10 11 12 1 2
1 Manual
annotation of
EPR free text
Annotation guideline
Gold-standard
annotated dataset 100
2 Text mining:
algorithm
development
Open-source text
mining tools for
dementia research 75
3 Text mining:
application
Text mining derived
values from the whole
Oxford CRIS data 20
4 Data analysis Reports/publication
10
Task 1 deliverables/challenges Gold-standard annotated dataset
Time
Diagnosis
Scale
Medication
Task 1 deliverables/challenges
Annotation guideline Dictionary and examples
UK-CRIS external access model for NLP
UK-CRIS
Oxford data server
NLP workstation
Big data server in Slough
- Buy computing resources
for a large scale NLP
Oxford local PC
Oxford Health External NLP experts
Remote access Computing power/enviro
nment
Task 1 challenges
•CRIS system update: DCRIS UKCRIS
–Re-doing work in the new system
–Real-world live data
Task 2 deliverables 4 Text mining components: performance F1 %
Time
Diagnosis: 90%
Scale: 80-90%
Medication:
98%
Task 3 deliverables
•Software engineering
–Optimisation for data extraction-processing
–Scalability i.e., processing millions of documents
–Testing i.e., unit testing
Overview
•Project outline
•Project progress
•Future development
Future development
•Other mental health disorders, e.g. depression
–Code re-use and algorithm optimisation
•Symptom/event extraction
•Ontology-based text mining
•Application to other UKCRIS sites (NHS trusts)
–UKCRIS text mining pipeline
•Grant applications
–NewMind Stage 2 grant, EPSRC, NIHR, MRC…
Acknowledgement • Oxford team
–Chi-Hun Kim(Neurologist, PI)
–Alejo Nevado-Holgado (Data analysis)
–Andrey Kormilitzin (Data analysis, Text mining)
–Christopher Lucas (Psychiatry trainee, Annotation)
–Dora Amos/Natalia Cotton (Medical students, Dictionary/examples)
• Manchester team
–Goran Nenadic (Text mining, Reader)
–Azad Dehghan (Text mining, Postdoc & DeepCognito Ltd)
• Funding
–NewMind Stage 1 feasibility funding for Azad Dehghan
–MRC-DPUK for Chi-Hun Kim
–IM-ROADMAP for Andrey Kormilitzin
–In-kind contribution from all other members
