Recent developments in natural language processing (NLP) techniques for working with clinical texts have largely on extracting medical problems, diagnoses and treatments from hospital notes in physical medicine, such as progress notes, discharge summaries and lab reports. This has been often been done for purposes of decision support: modeling the patient as a set of problems to be solved and identifying the correct course of treatment according to the prevailing medical model. Narrative medicine, however, places importance on the meaning of illness as experienced by the patient, or as reflected by the clinician's personal experience of working with the patient. These narratives may make rich use of emotive language that may be missing from traditional clinical notes. Patient narratives consist of a series of unfolding events involving interacting protagonists and their roles. Computationally, these can be modeled as narrative event chains: sets of partially ordered events related by a common protagonist. In psychotherapy, cognitive analytic therapy (CAT) is a model that specifically considers these narrative events in order to reformulate the patient¹s problems in terms of the event sequences and reciprocal roles that lead to and maintain maladaptive personal relations. In this presentation, I present exploratory work on developing a computational framework for extracting and visualizing narrative event chains from CAT narratives, and consider the potential uses and benefits of such a framework.

2. Phil Gooch
School of Arts and Humanities
Department of Digital Humanities
Tools for discourse analysis and
visualisation of clinical narratives
Extracting event chains from psychotherapy narratives

3. Overview
• Current state of play of natural language processing (NLP) in the clinical domain
• Narrative medicine: reflective patient and clinician stories vs traditional clinical
notes
• Cognitive analytic therapy and the use of narrative
• Narrative event chains
• Discourse analysis and visualisation of narrative
• Development and application of a framework for extraction and visualisation of
event chains from clinical narratives
• Results and discussion

4. Unstructured text to structured data
http://www.marywood.edu/web/cont
ent-editors/tutorials/structures/

5. Unstructured text to structured data
http://www.45cat.com/record/am792

6. Unstructured text to structured data

7. Clinical natural language processing
• Current state of the art: hybrid approaches that combine rules, machine learning,
and external knowledge resources (e.g. UMLS, WordNet) and ontologies to identify
and classify:
• Current vs historical problems
• Current vs historical medications and procedures
• Family history
• Experiencer (patient vs other), negation, possibility
• Coreference and anaphora resolution
• Most recently, temporal concept and relation discovery (Sun et al, JAMIA 2013)
• Focus however has been on corpora of traditional clinical narratives: discharge
summaries, progress notes, lab reports
• Medical model of patient as a set of problems to be solved; NLP for decision support
to identify these problems and the best treatment for them (e.g. Wagholikar et al,
JAMIA 2012)

8. Narrative medicine
• Patient-described medical history is more than a set of problems
• Reframing: events and situations that have meaning for the patient
• Rich material that helps the clinician better understand the patient experience, build
empathy and trust (Charon 2001)
• Reflective practice and professional development
• Narrative as a temporal flow of unfolding events from the viewpoint of different
protagonists with different roles (Greenhalgh & Hurwitz, BMJ 1999)

9. Visualisation of a schizophrenia narrative
Cometstarmoon (2005) http://www.flickr.com/photos/45499571@N00/3402234312

10. Cognitive analytic therapy (CAT) (Ryle 2002)
• A model of psychotherapy that makes use of narrative as a ‘key tool of understanding
and therapeutic change’ (Jefferis 2001)
• Life as a narrative form
• Goal is to reformulate the patient’s story in terms of the event sequences and
reciprocal roles that lead to and maintain maladaptive personal relations
• Reformulation letter aims to retell the patient story in a way that makes it
accessible to therapeutic change
http://www.catsandwomenwilldo.com/archives/tag/cats

11. CAT procedural sequences and reciprocal roles
Potter 2002, http://www.acat.me.uk/reformulation.php?issue_id=20&article_id=197

12. CAT reciprocal role procedures
Ahmadi 2011, http://www.acat.me.uk/reformulation.php?issue_id=1&article_id=25

13. Research goals
• How to apply NLP to narrative medicine, in particular CAT narratives?
• Can existing tools for working with ‘traditional’ clinical narratives be usefully applied
to these richer, patient and clinician narratives?
• Can we identify the flow of events in a narrative, and their associated protagonists
and roles?
• Structured data for summarisation and visualisation
• Narrative event chains: partially ordered (just ‘before’ and ‘after’) events related by a
common protagonist (Chambers & Jurafsky 2008)
• Machine learning of narrative schema from Gigaword newswire corpus (C & J 2010):
Events Roles
A write B A = author
A edit B B = book
A publish B
C distribute B C = company
C sell B

14. Narrative event chains
• Three steps
• Identifying events (narrative event induction)
• Temporal ordering of events
• Event pruning into discrete chains for each protagonist (coreference resolution)
• Problem
• Require large corpora of clinical narratives for application of Chambers &
Jurafsky’s unsupervised learning approach
• C & J’s code not publicly available?
• Anyway, we are interested in in-depth processing and visualisation of individual
narratives, rather than learning general schema from a large corpus
• Possible solution
• Extend existing modular framework for processing clinical discharge summaries

15. Pipeline structure
• GATE framework (visual editor, modular, plug-and-play architecture, no
programming skills required for end users)
• Standard NLP modules (Tokenization, Sentence splitting, POS tagging, Noun-phrase
chunking) plus
• Temporal relation identification (for event ordering)
• Predicate phrase chunking (verb events)
• Clinical concept identification (disease, symptom, procedure, medication)
• Clinical abbreviation expansion
• Domain knowledge integration (UMLS, WordNet)
• Protagonist-based coreference resolution (Gooch & Roudsari 2012)

16. Example: ‘Sam’ narrative (Ryle & Kerr 2002)
Shows coreferring Person entities (e.g. ‘Sam’, ‘who’ and ‘his’), temporal
concepts (TIMEX3, Age), clinical concepts, verb group phrases (VG) and
unclassified entities (Thing)

17. Narrative event chains: timeline visualisation
Wellcome Timeline[1] visualisation generated from annotated output of NLP pipeline
[1] https://github.com/wellcomelibrary/timeline

18. … vs traditional visualisation

19. Application to CAT: diagrammatic reformulation
• As noted on Slide 10, part of CAT process involves therapist writing a letter to the
patient that reformulates the patient’s story according to the CAT model
• The letter is then expressed in visual form, in collaboration with the patient
• Diagrammatic reformulation is often difficult for CAT trainees (Jenaway 2011)
• Can NLP help?
• Exploratory processing of the ‘Bobby’ and ‘Beatrice’ reformulation letters from Ryle
(2002)

20. ‘Bobby’ reformulation

21. ‘Bobby’ reformulation: XML event chains
<actors>
<actor>
<name>Bobby</name>
<events>
<event>childhood either feeling especially loved and treasured or being a nuisance and ignored</event>
<event>were cared for if ill otherwise ignored by your older brothers and sisters</event>
<event>tried to please them … always felt scared</event>
<event>neglect … ignore your needs … or seek comfort through drink or smoking dope</event>
<event>are usually neglectful of your body … .have not seen a doctor … asthma … other ailments</event>
<event>tend to cling anxiously and alienate others … Elizabeth your partner leaving you</event>
<event>to drink smoke dope … ignore problems which then build up</event>
<event>receive care if 'special’ … strive to create special claims … feel you must suffer to deserve it … become agitated drink smoke dope</event>
<event>the limited options of your childhood … they seem to have given you some intimacy relief</event>
<event>this difficult time you are no longer in a relationship with a woman who will rescue you</event>
<event>have said you have been impressed with my help … the honeymoon phase … one your relationships</event>
<event>neediness</event>
</events>
</actor>
<actor>
<name>Steve Potter</name>
<events>
<event>suspect it will be hard to imagine short our relationship is 16 sessions … how you will cope with tolerating the disappointment</event>
<event>cannot meet your current pattern of neediness</event>
</events>
</actor>
<actor>
<name>your [Bobby] older brothers and sisters</name>
<events>
<event>always felt scared</event>
</events>
</actor>
</actors>

22. ‘Bobby’: simplified diagrammatic reformulation
loved and treasured or being a nuisance and ignored

neglectful of your body

seek comfort through drink or smoking dope

asthma and other ailments

cling anxiously and alienate others

strive to create special claims

need to be rescued

neediness
Linear narrative chains vs
reciprocal role procedures
identified in Ryle & Kerr (2002):
Source: Fig. 2.1 in Ryle & Kerr (2002)

23. ‘Beatrice’ reformulation: XML event chains
<actor>
<name>Beatrice</name>
<events>
<event>father's desertion</event>
<event>remember mother’s unaffectionate figure ... you felt she was concerned with appearances not your feelings</event>
<event>set off ended up making a success of work making two or three good woman friends</event>
<event>felt securely loved</event>
<event>learned to expect little from others ... it was safer to manage on your own</event>
<event>trying to please others ... the hope getting acceptance only to be used by them which makes you hate yourself</event>
<event>have experienced abandoned uncared feelings which I feel you had learned to put aside in your early life</event>
<event>the belief that you be emotionally involved and doomed to be abandoned</event>
<event>deserved the difficulties of your childhood ... the brief rebellion at school may be the source of your irrational guilt</event>
<event>were not to be happy so you sabotage things that do go well</event>
<event>need to please me to be accepted- you may feel angry with yourself</event>
<event>will certainly be abandoned at the end of our 12 further weeks</event>
<event>this may make you reluctant to be involved it will also protect you feeling overwhelmed by dependency</event>
</events>
</actor>
<actor>
<name>Kate Freshwater</name>
<events>
<event>feel that you learned to expect little from others it was safer to manage on your own</event>
<event>believe that working next three months will give you support for you to revise the damaging ways you have relied on up to now</event>
</events>
</actor>
<actor>
<name>your [Beatrice] mother</name>
<events><event>was concerned with appearances ... not your feelings</event></events>
</actor>
<actor>
<name>Richard</name>
<events><event>was the first person whom you experienced the depth of your need for affection</event>
<event>leaving was a terrible blow ... you have experienced abandoned uncared feelings … had learned to put aside in your early life</event>
</events>

24. ‘Beatrice’: simplified diagrammatic
reformulation
Father’s desertion, mother unaffectionate

making a success of work

felt securely loved

learned to expect little from others

trying to please others only to be used by them

experienced abandoned feelings

doomed to be abandoned

rebellion at school

source of irrational guilt

sabotage things that do go well

will certainly be abandoned at the end of 12 further weeks [of therapy]

reluctant to be involved

protect you feeling overwhelmed by dependency

25. ‘Beatrice’: reformulation from Ryle & Kerr (2002)
Source: Fig 6.2 in Ryle & Kerr (2002)

26. Conclusion
• Protagonists and their associated events can be explored, extracted and visualised
using an NLP framework originally developed for processing discharge summaries
• Configurable, component based architecture utilising generalised linguistic patterns
makes this possible
• But the linear diagrams generated lack sophistication
• Grouping events according to protagonist loses the interaction between events and
multiple actors
• ‘leaving was a terrible blow’ event associated with Richard’s event chain, but this is
more relevant to Beatrice
• Much more work to be done. E.g. combine machine learning with the linguistic
patterns used in this pipeline.
• Pipeline components available at https://github.com/philgooch

27. THANK YOU
Phil Gooch
Research Developer
Department of Digital Humanities
King’s College London
philip.gooch@kcl.ac.uk