Topic modeling of Emergency Department Triage notes for characterising pain-related

chief complaintsKarin Verspoor, The University of MelbourneAntonio Jimeno Yepes, The University of MelbourneSimon Kocbek, RMIT UniversityWray Buntine, Monash UniversityTheresa Vassiliou, Royal Melbourne HospitalMarie Gerdtz, Royal Melbourne Hospital

World health organisation (WHO) guidelines on pain management (2007)

Emergency care acute pain management manual (2011)

Australasian College of Emergency Medicine (ACEM) Policy (58) (2009)

Background

Aims of the study• Apply topic modeling to explore the triage

notes for pain-related descriptors• Apply temporal (dynamic) topic modeling

to identify temporal variations in symptoms

Safety

Risk

Acuity & Severity

Presenting Complaint

Time to treatment

Pain Score

ComplexityNeed for admission

Assigned ATS 1 - 5

2 - 5 minute Assessment

Allocated to treatment stream

Triage process

Numerical Rating Score (NRS)

Pain Score at triage

Sample57,984 Patients

ATS 4

(10,655)

ATS 3

(9,200)

ATS 2

(2,572)

Exclusions

Presenting complaints for psychiatric distress

ATS 1 – 5 , “Unable” 0/10

(35,612)

Pain related distress

and ATS 2 – 4

(22,372)

Subgroups of interest

ATS 2 ATS 4

ATS 3ConsistentATS High urgency &

High Pain score

InconsistentATS Low urgency &

High Pain score

Moderate pain (4-6)

Mild pain (1-3)Severe pain (7-10)

Topic Modeling• Unsupervised machine learning task that uncovers the hidden topical patterns in text collections.• Based on a probabilistic model that allows documents to have mixtures of topics.• Topic:

– distribution over terms in a vocabulary. – represented with a list of top most probable words/tokens.

• Our static model: – grouping of the most related tokens in each patient subgroup of interest, using topic modeling– ED records text is split into tokens or mapped to concepts in the UMLS

Topic Modeling

Blei, MLSS 2012

Topics by consistency subgroup

• First token based topic for both sets showing the top 10 most relevant words

• We find that the terms related to the inconsistent groups denote painful but not life threatening conditions

Inconsistent (Topic 0) Consistent (Topic 0)

swelling hr

painful chest

knee hx

swollen bp

arm sob

yesterday reg

shoulder spo

forte central

injury ht

present increased

Dynamic topic models• Capable of analysing

the time evolution of topics.

• Data can be split into epochs (e.g. months, weekdays-weekend)

• First order Markov model: current epoch depends on the previous epoch. Blei, MLSS 2012

Results – Dynamic Model

Topic

Problem

Top representative words

T1 Flu aches, runny, chills, flu-like, fever

T2 Asthma sentences, speaking, ventolin, talk

T3 Angina gtn, patch, anginine, spray, aspirin

T4 Arm foosh, rotation, shortening, rotated

Topic Top representative words

Car car, loc, driver, hit, speed, head

Finger finger, cut, vasc, intact, rom, hand

Abdomen abdo, flank, chronic, lower

Issues and future work

• Preprocessing of the data to address problems with clinical language used in triage notes.• Explore different numbers of topics.• Pain-related topics analysis: Assessment of topic coherence based on nurses’ feedback

• Dynamic topic models: – More data is needed (statistical significance) –Adaptation of topic model to deal with periodic effects

Acknowledgements

Our collaborators at the Royal Melbourne Hospital ED• A/Prof Jonathan Knott• Theresa Vassiliou• Marie Gerdtz• Rochelle Wynne