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Dr. Ehud Reiter, Computing Science, University of Aberdeen 1
BabyTalk: Generating English Summaries of
Clinical DataEhud Reiter
Univ of Aberdeen, CS Dept
Dr. Ehud Reiter, Computing Science, University of Aberdeen 2
Structure
Background: data-to-text Babytalk project Results of first evaluation Current work
Dr. Ehud Reiter, Computing Science, University of Aberdeen 3
What is data-to-text
Goal: generate English summaries of non-linguistic data» Numerical weather predictions» Medical records» Statistics» Etc
Dr. Ehud Reiter, Computing Science, University of Aberdeen 4
Simple Example:Weather Forecasts
Input: numerical weather predictions» From supercomputer running a numerical weather
simulation
Output: textual weather forecast We’ve developed several systems
» Two used commercially (oil rig, road gritting)– Users prefer some gen texts to human texts!
» Demo of pollen system on our webpage
So have others (FoG, MultiMeteo, …)
Dr. Ehud Reiter, Computing Science, University of Aberdeen 5
Pollen forecasts
Grass pollen levels for Tuesday have decreased from the high levels of yesterday with values of around 4 to 5 across most parts of the country. However, in South Eastern areas, pollen levels will be high with values of 6.
Dr. Ehud Reiter, Computing Science, University of Aberdeen 6
Other data-text apps
Medical: to-be-discussed Assistive technology: help blind people
access statistical data Financial: summarise stock-market data Education: Summarise assessment
results, help write stories Engineering: Sum. gas-turbine data Etc
Dr. Ehud Reiter, Computing Science, University of Aberdeen 7
Why is data-to-text useful
The world is drowning in data» NLP researchers talk about problems of
too much text, but data problems are worse
– Texts are at least read by someone (writer)– Most data is automatically collected and never
looked at by a human
Dr. Ehud Reiter, Computing Science, University of Aberdeen 8
Data overload
Sensor recording 2 bytes/second» 170KB/day» 63MB/year» Millions of sensors in hospitals, jet engines, …
Simulations» Weather: 30MB for one day in one UK county,
from one model» Climate models: petabytes of data
Too much data, need better tools for utilising!
Dr. Ehud Reiter, Computing Science, University of Aberdeen 9
Decision Support
Data often used for decision support» Medical: help doctors make decisions» Weather: helps staff on offshore oil rigs plan their
operations» Engineering: help plan maintenance» Etc
Often under time pressure» Make a decision in 3 min, here is 30MB of data to
help you
Dr. Ehud Reiter, Computing Science, University of Aberdeen 10
Using data for decision support
Alarming» Trigger alarm if value exceeds threshold
– Or other such simple rule
» Works, doesn’t get full value from data Visualisation
» Show data to experts visually– People like this, unclear how much it helps,
especially when massive amount of data
Dr. Ehud Reiter, Computing Science, University of Aberdeen 11
Using data for decision support
Knowledge-based systems» Feed data into an expert system which
makes recommendations based on it» Can work in some contexts, but problems
– Domain experts dislike being told what to do– Often key data not available to KBS– Can be brittle, fragile
Dr. Ehud Reiter, Computing Science, University of Aberdeen 12
Data-text for decision support
Idea: use KBS, NLP tech to generate a short text summary of a data set
Intermediate between KBS and visualisation» Use domain reasoning to highlight key info,
infer causal links, add background know» But stick to describing data, don’t tell
experts what to do!
Dr. Ehud Reiter, Computing Science, University of Aberdeen 13
Data-text for decision support
vs alarms: deeper info vs visualisation
» Just key facts, not everything» Supplemented with causal links, etc
vs KBS» More acceptable to users» More robust, since not useless if missing
some key data or knowledge
Dr. Ehud Reiter, Computing Science, University of Aberdeen 14
Data-text for decision support
Above is still somewhat speculative But people in many domains are
interested in exploring the concept to see if it works» Esp since current situation is so bad!
Of course other uses of data-to-text» Assistive technology, education
Dr. Ehud Reiter, Computing Science, University of Aberdeen 15
Language and World
How does language relate to the world? Data-to-text is a great way of exploring
this» The real reason I got into this…
Dr. Ehud Reiter, Computing Science, University of Aberdeen 16
BabyTalk
Goal: Summarise clinical data about premature babies in neonatal ICU
Input: sensor data; records of actions/observations by medical staff
Output: multi-para texts, summarise» BT45: 45 mins data, for doctors (completed)» BT-Nurse: 12 hrs data, for nurses» BT-Family: 24 hrs data, for parents» BT-Clan: 24 hrs data, for other friends, family» Bt-Doc: several hrs data, for doctors
Dr. Ehud Reiter, Computing Science, University of Aberdeen 17
Neonatal ICU
Dr. Ehud Reiter, Computing Science, University of Aberdeen 18
Baby MonitoringSpO2 (SO,HS)
ECG (HR)
Core Temperature (TC)
Arterial Line
(Blood Pressure)
Peripheral Temperature (TP)
Transcutaneous Probe
(CO,OX)
Dr. Ehud Reiter, Computing Science, University of Aberdeen 19
Input: Sensor Data
Dr. Ehud Reiter, Computing Science, University of Aberdeen 20
Input: Action Records
FullDescriptor Time
SETTING;VENTILATOR;FiO2 (36%)
10.30
MEDICATION;Morphine 10.44
ACTION;CARE;TURN/CHANGE POSITION;SUPINE
10.46-10.47
ACTION;RESPIRATION;HAND-BAG BABY
10.47-10.51
SETTING;VENTILATOR;FiO2 (60%)
10.47
ACTION;RESPIRATION;INTUBATE 10.51-10.52
Dr. Ehud Reiter, Computing Science, University of Aberdeen 21
BT45 texts
Human corpus text At 1046 the baby is turned for re-intubation and re-intubation is
complete by 1100 the baby being bagged with 60% oxygen between tubes. During the re-intubation there have been some significant bradycardias down to 60/min, but the sats have remained OK. The mean BP has varied between 23 and 56, but has now settled at 30. The central temperature has fallen to 36.1°C and the peripheral temperature to 33.7°C. The baby has needed up to 80% oxygen to keep the sats up.
Computer-generated text By 11:00 the baby had been hand-bagged a number of times
causing 2 successive bradycardias. She was successfully re-intubated after 2 attempts. The baby was sucked out twice.At 11:02 FIO2 was raised to 79%.
Dr. Ehud Reiter, Computing Science, University of Aberdeen 22
Babytalk architecture
Signal analysis: patterns, trends Data interpretation: based on medical
knowledge (like expert sys) Doc planning: select and structure
events to be mentioned Microplanning: choose words, syntactic
structures, referring exp Realisation: generate actual text
Dr. Ehud Reiter, Computing Science, University of Aberdeen 23
Signal Analysis
Detect trends, patterns, events, etc» Blood oxygen levels increasing» Downward spike in heart rate
Detect artefacts» Changes due to sensor problems
Plenty of algorithms exist for this Will not further discuss here
Dr. Ehud Reiter, Computing Science, University of Aberdeen 24
Data Abstraction
Detect higher-level events in the data» Sequence of bradycardias (downward
spikes in HR) Determine medical importance
» Bradycardia more important if simultaneous desaturation (downward spike in SO)
Medical KBS
Dr. Ehud Reiter, Computing Science, University of Aberdeen 25
Data Abs: Links Between Events
Infer links between events» Blood O2 falls, therefore O2 level in
incubator is increased» HR up because baby is being handled» Morphine given as part of the intubation
procedure Very imp, much of value added of text
» Helps readers build good mental model of what is happening to the baby
Dr. Ehud Reiter, Computing Science, University of Aberdeen 26
Document Planning
First NLP stage Decide what events to mention Decide how these are ordered and
organised
Dr. Ehud Reiter, Computing Science, University of Aberdeen 27
Content Determination
First approach: Include most medically important events» Also include moderately important events
which are linked to very important events Doesn’t always work
Dr. Ehud Reiter, Computing Science, University of Aberdeen 28
Problem: Continuity
Omitting intermediate events confuses readers» Example: TcPO2 suddenly decreased to
8.1. SaO2 increased to 92. TcPO2 suddenly decreased to 9.3
» There is a gradual rise in TcPO2 between the sudden falls
– This is less important medically– But important for reader’s comprehension
Dr. Ehud Reiter, Computing Science, University of Aberdeen 29
Document Structure
How do we order/group events» By time» By medical importance» By body subsystem (eg, respiration)
Initially focused on time, but users want more emphasis on subsystem» Eg, first a “scene” about respiration, then a
“scene” about thermoregulation– Not constant shifting between two
Dr. Ehud Reiter, Computing Science, University of Aberdeen 30
Doc Planning: Narrative
High-level analysis: need to do a better job of generating a “story” from the data» Link events together» Include events needed for story
progression even if not important» “Scene” structure
Qualitative observation by users
Dr. Ehud Reiter, Computing Science, University of Aberdeen 31
Microplannig
Second NLP stage Choose words and syntactic structure to
express information Aggregation Reference
Dr. Ehud Reiter, Computing Science, University of Aberdeen 32
Challenge: Time
Need to communicate temporal info» Enough so that readers can interpret the
data» Not too much, text becomes unreadable
– Imagine story with “At 10.14 John left home. At 10.28 he met Mary in the pub. At 10.39…”
Dr. Ehud Reiter, Computing Science, University of Aberdeen 33
Tenses
Use Reichenbach model» Speech time: time of report being read» Event time: time of event being described» Reference time: determined using a
salience model– Similar to resolving anaphoric reference
Usually worked, sometimes failed» Need better model for reference time
Dr. Ehud Reiter, Computing Science, University of Aberdeen 34
What does event time mean?
Sometimes explicit time given for event» Supposed to be start time of event, sometimes
misinterpreted Ex:”After three attempts, at 13.53 a peripheral
venous line was inserted successfully.”» 13.53 refers to time of first (failed) attempt
– Start of LINE-INSERT-ATTEMPTS event
» Readers interpret as time of final (succ) attempt Need better linguistic model of time
» Linguistic temporal ontology (Moens Steedman)?
Dr. Ehud Reiter, Computing Science, University of Aberdeen 35
Lexical Choice
Need mechanism to map domain events (instances in a Protégé ontology) to linguistic structures
Use JESS rules» Lexical info from Verbnet, NIH lexicon
Engineering challenge» Relate to Sheffield work on NLG/ontologies
Dr. Ehud Reiter, Computing Science, University of Aberdeen 36
Vague language
Human texts are full of vague language» Ex: There is a momentary bradycardia» What does “momentary” mean?
Our models of this are very crude, need to be improved!
Dr. Ehud Reiter, Computing Science, University of Aberdeen 37
Realisation
Last NLG stage Generate actual text, once choices
made Use Aberdeen simplenlg package Will not further discuss here
Dr. Ehud Reiter, Computing Science, University of Aberdeen 38
BT45 Evaluation
Showed 35 medical professionals 24 scenarios in 3 conditions (8 of each)» Visualisation of medical data» Textual summary (manually written)» Textual summary (from BT45)
Asked to make a treatment decision» Limited to 3 minutes» Measured correctness (against gold stan)
Off-ward, using historical data» So no other knowledge about baby
Dr. Ehud Reiter, Computing Science, University of Aberdeen 39
Free-text comments
Comments were not solicited, but were recorded if made
Most important were» Better layout (eg, bullet lists)» Continuity (as mentioned before)
Dr. Ehud Reiter, Computing Science, University of Aberdeen 40
Decision-Support results
No sig difference in time taken Avg decision-quality (scale -1 to 1)
» Human texts: 0.39» Computer texts: 0.34» Visualisation: 0.33
Human sig better than comp, visual No sig diff comp, visual
Dr. Ehud Reiter, Computing Science, University of Aberdeen 41
Results by subject type
Analysis by type of subjects» Human texts especially good for junior
nurses (ie, least experienced subjects)
Dr. Ehud Reiter, Computing Science, University of Aberdeen 42
Results by scenario
Each scenario had a main target action» 8 different ones
Computer texts as good as human texts for five of these; worse for three» No action, manage temperature, monitor
equipment» These relate to specific problems in the
system, which can be fixed
Dr. Ehud Reiter, Computing Science, University of Aberdeen 43
Target Actions with Poor Perf
No action: Needs high-level summary, not blow-by-blow event description
Manage Temperature: Two temp channels, need to describe together
Monitor equipment: Need to mention (not ignore) sensor artefacts
Dr. Ehud Reiter, Computing Science, University of Aberdeen 44
Summary
Good performance with human texts shows textual presentation is effective» Also seen in previous study
Babytalk as good as visualisation, could make better by addressing above issues» Even now giving users BabyTalk text as
supplement to visualisations could help
Dr. Ehud Reiter, Computing Science, University of Aberdeen 45
Current Work
BT-Nurse: shift summaries for nurses» Use live data from current babies» Evaluate on ward, using babies that
subjects (nurses) actually looking after» Focus on info relevant to nurse shift
planning, not real-time decision support» Longer time period (12 hrs)
– Need more sensor abstraction
» Longer texts (multi-page)
Dr. Ehud Reiter, Computing Science, University of Aberdeen 46
Current Work
BT-Family: information for parents» Estimate how stressed parents are, use
this to control content, phrasing– High stress means less content– Relate to Sheffield work on personality??
» Express information in language which parents can understand, not medicalese
Dr. Ehud Reiter, Computing Science, University of Aberdeen 47
Current Work
BT-Clan: Information for friends, family» Social networking perspective: encourage
useful support, minimise hassle of dealing with numerous inquiries
– Parents decide what to tell people– Intentional deceit: if granny is frail, don’t tell her
bad news
» Info about parents as well as baby
Dr. Ehud Reiter, Computing Science, University of Aberdeen 48
Research agenda
Detecting complex events in the data Integration with medical guidelines Better use of vague language Better stories Role of text in interactive multimodal
information presentation system Try in domain of assisted living