Learning Causal Relationships From Medical Abstracts

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Learning Causal Relationships From Medical
Abstracts

• Jonathan Elsas
• Jaime Arguello

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Goal

• Extract entity pairs (A,B,P), where it is said
  that A ? B with Pr P
• Extract contexts (pre, mid, post,direction, P)
  that mark causal relationships with Pr P
• Use the best entity pairs to extract more
  contexts
• Use the best contexts to extract more entity
  pairs
• BEST highest P( C (A,B)) P( C (pre, mid,
  post))

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Examples
post
pre
mid
Toxic dermatitis due to TB 1 therapy.
post
mid
pre
In 518 cases, a single factor caused the abortion
post
mid
Risk of cancer persists for years following
smoking cessation.
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Algorithm

• Input ContextList seeds, EntityPairList seeds
• 1 Use all Contexts in ContextList to compute
• P( c (Ai, Bi)) Sk P( c Sk ) P ( Sk (Ai
  ,Bi))
• 2 Keep N best (Ai, Bi) in EntityPairList
• 3 Use all EntityPairs in EntityPairList to
  compute
• P( c Si) Sk P( c (Ak, Bk) ) P ((Ak, Bk)
  Si)
• 4 Keep N best Si in ContextList to compute 1
• 5 Increment N and return to 1
• P ( Sk (Ai ,Bi)) Sk , (Ai ,Bi) / (Ai
  ,Bi)
• P ((Ai ,Bi) Sk) Sk , (Ai ,Bi) / Sk

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Corpus and Tools

• 300,000 MEDLINE abstracts
• Annotated with POS sentence boundaries
• Limited to within-sentence contexts
• Use Indri IR toolkit to index annotations
• Allows sentence retrieval
• Structure Query Language
• e.g. 1(ltanynn anynnpgt cause.vb ltanynn
  anynnpgt)

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Observations (1)

• The algorithm seems to learn well during first
  few iterations
• One seed (Heavy smoking, cancer)
• Learns contexts caused by and due to
• Learns entity pairs
• (rubber, dermatitis), (bromobenzene, necrosis)
• All this within 2-3 iterations!

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Observations (2)

• Quality Rapidly degrades
• Quickly converges to specialized area of the
  corpus.
• A quickly growing population of people with
  appears to be non women
• Need a better way of limiting the window of pre-
  and post-context, or when pre- and post- are
  irrelevant
• Yet while men are increasingly kicking the habit
  doctors fear that induced could become as
  much of a threat to women in the future.
• Doesnt depend on whether we start with
  context-seeds or entity-pair-seeds

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Next Steps

• More Data!
• 300,000 abstracts way too sparse
• Several million abstracts available from NLM
• Add better entity extraction, and
  canonicalization
• Problem NLM METATHESAURUS needs 26 Gigs!

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Last Minute Google Results

• Pre-context seems to help a lot
• e.g. studies show that_____ causes.
• Some Good contexts learned
• null is less in people who quit null
• null is the major single cause of null
• null deaths in 2002 attributed to passive
  null
• null accounts for at least 30 of all null
• null is less in people who quit null
• Need to find way to transform learned contexts
  into more general form

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Ideas?