Presentation is loading. Please wait.

Presentation is loading. Please wait.

BabyTalk: Generating English Summaries of Clinical Data

Similar presentations

Presentation on theme: "BabyTalk: Generating English Summaries of Clinical Data"— Presentation transcript:

1 BabyTalk: Generating English Summaries of Clinical Data
Ehud Reiter Univ of Aberdeen, CS Dept

2 Structure Background: data-to-text Babytalk project
Results of first evaluation Current work

3 What is data-to-text Goal: generate English summaries of non-linguistic data Numerical weather predictions Medical records Statistics Etc

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, …)

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.

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

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

8 Data overload Sensor recording 2 bytes/second Simulations
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!

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

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

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

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!

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

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

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…

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

17 Neonatal ICU

18 Peripheral Temperature (TP)
Baby Monitoring SpO2 (SO,HS) ECG (HR) Peripheral Temperature (TP) Core Temperature (TC) Transcutaneous Probe (CO,OX) Arterial Line (Blood Pressure)

19 Input: Sensor Data

20 Input: Action Records FullDescriptor Time

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%.

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

23 Signal Analysis Detect trends, patterns, events, etc Detect artefacts
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

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

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

26 Document Planning First NLP stage Decide what events to mention
Decide how these are ordered and organised

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

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

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

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

31 Microplannig Second NLP stage
Choose words and syntactic structure to express information Aggregation Reference

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 John left home. At he met Mary in the pub. At 10.39…”

33 Tenses Use Reichenbach model Usually worked, sometimes failed
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

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 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)?

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

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!

37 Realisation Last NLG stage Generate actual text, once choices made
Use Aberdeen simplenlg package Will not further discuss here

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

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)

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

41 Results by subject type
Analysis by type of subjects Human texts especially good for junior nurses (ie, least experienced subjects)

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

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

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

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)

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

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

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

Download ppt "BabyTalk: Generating English Summaries of Clinical Data"

Similar presentations

Ads by Google