Medical T ext Data Sendong (Stan) Zhao + , Meng Jiang * , Ming Liu + - - PowerPoint PPT Presentation

CausalTriad: T

• ward Pseudo Causal Relation

Discovery and Hypotheses Generation from Medical T ext Data

Sendong (Stan) Zhao+, Meng Jiang*, Ming Liu+, Bing Qin+, Ting Liu+

+Harbin Institute of Technology, China *University of Notre Dame, USA

Pseudo Causal Relation

• Golden standard
• Randomized controlled experiments
• Too costly
• Observational data
• Structured data, eg. EHR
• Unstructured data (Text data), eg. medical literature, patient report
• Pseudo causal relation
• Semantic-level causal relations
• Verified true causal knowledge
• Or, have not been identified previously
• Or, no evidence to support them

Previous Studies

• Extract causal relations from single sentences
• While causal relations usually span multiple sentences
• Use only textual information and ignore structural information
• While causal relations naturally have an attached network structure
• Only extraction rather than inference
• While causality itself is a basic logical rule

Causation Transitivity

• Preserving transitivity is a basic desideratum for an

adequate analysis of causation

• -L. A. Paul and Ned Hall “Causation: A User’s Guide”

𝐵 𝐶 …… 𝐷 𝐵 𝐷

Causation Transitivity in Medical Text

Obesity usually increases the risk of diabetes. People with diabetes have more sugar in blood called hyperglycemia. Metformin has become a mainstay of type 2 diabetes management and is now the recommended first-line drug for treating the disease.

Obesity Diabetes Hyperglycemia Metformin ? ? cause cause

Motivation

• Jointly utilize
• Textual information (context and co-occurrence)
• Structural information (causation transitivity rule)
• Through inference to
• Discover causal relations in text
• Generate new causal relation hypotheses

Problem Definition

• Problem: Causal Relation Discovery from Triad Structures
• Medical Cause-Effect Candidates Network

𝐻 = 𝑊, 𝐹 , 𝐹 ∈ 𝑊 × 𝑊

• Triad Structure
• Each Triangle in the network
• Basic unit

Our method

• Causal Relation Candidates Matching
• 3 Clues for Causal Discovery
• Causal Association
• Contextual Information
• Causal Transitivity Rules
• Factor Graph Model

Causal Relation Candidates Matching

• Medical Dictionary
• Dryad data package
• TCMonline and TCMID
• For every n consecutive sentences
• Match medical entities
• Pair each of them into several pairs
• Every two pairs with a shared entity generate a triad structure
• Eg. (𝑓𝑗, 𝑓𝑙) and (𝑓𝑗, 𝑓

𝑘) generate a triad structure (𝑓𝑙, 𝑓𝑗, 𝑓 𝑘)

Our method

• Causal Relation Candidates Matching
• 3 Clues for Causal Discovery
• Causal Association
• Contextual Information
• Causal Transitivity Rules
• Factor Graph Model

3 Clues for Causal Discovery

• Causal Association
• Frequently co-occurring entities are more likely to be a causation [Do and

Roth 2013]

• ei is a possible cause of entity ej, if ej happens more frequently with ei than by

itself [Suppes 1970]

• Contextual Information
• Causal relations in the text tend to share special contexts
• Like domain-related words, causal triggers, connectives, etc.
• Causation Transitivity Rule

Causal Association

• Modeling causal association

𝐷𝐵 𝑓𝑗𝑘 = 𝐽(𝑓𝑗, 𝑓

𝑘) × 𝐸(𝑓𝑗, 𝑓 𝑘) × 𝑁𝑏𝑦(𝑣𝑗, 𝑣𝑘)

• Larger mutual information

𝐽 𝑓𝑗, 𝑓

𝑘 = 𝑚𝑝𝑕 𝑄(𝑓𝑗, ej)

𝑄 𝑓𝑗 𝑄(𝑓

𝑘)

• Award pairs that co-exist closer, while penalizing those are further apart in text

𝐸 𝑓𝑗, 𝑓

𝑘 = − log 𝑡𝑓𝑜𝑢 𝑓𝑗 − 𝑡𝑓𝑜𝑢 𝑓 𝑘

+ 1 2 × 𝑋𝑇

• Model the frequency of co-occurrence of two medical entities, 𝑁𝑏𝑦 𝑣𝑗, 𝑣𝑘

𝑣𝑗 =

𝑄(𝑓𝑗,𝑓𝑘) max

𝑙

𝑄 𝑓𝑗,𝑓𝑙 −𝑄(𝑓𝑗,𝑓𝑘 )+𝜁 , 𝑣𝑘 = 𝑄(𝑓𝑗,𝑓𝑘) max

𝑙

𝑄 𝑓𝑙,𝑓𝑘 −𝑄(𝑓𝑗,𝑓𝑘 )+𝜁

Contextual Information (1)

• Encode Synthetic Context

Contextual Information (2)

• Encode context based on pre-trained word2vec Word Embedding
• Three ways

Causation Transitivity Rules

• angle rules and triadic rule

Integrate 3 Clues

• Combining evidence from both textual supports and structural

inferences, the above three clues are better equipped to discover causal relations.

• They are complementary in several ways:
• Causal association gives preferences to frequently co-occurring causal pairs.
• Causal transitivity rules are designed to identify causal relations with few

textual supports except for those that follow the transitivity rule and generate new causal hypothesis.

• Incorporating contextual information from the text can potentially eliminate

those frequently co-occurring medical entities which are not causal.

Our method

• Causal Relation Candidates Matching
• 3 Clues for Causal Discovery
• Causal Association
• Contextual Information
• Causal Transitivity Rules
• Factor Graph Model

CausalTriad: Factor Graph for Each Triad Structure

Experiments

• Data collection
• TCM consists of the abstracts of 106,151 papers.
• HealthBoards consists of post messages on health and medical issues such as

diseases, symptoms, medicines, and side-effects, etc.

• Generating new causal relation hypotheses

Experimental Results

• Different types of causal relations
• DISEASE–cause–SYMPTOM
• FORMULA–against–DISEASE
• HERB–against–DISEASE
• FORMULA–relieve–SYMPTOM
• HERB–relieve–SYMPTOM
• DISEASE–bring–DISEASE
• DRUG–against–DISEASE
• DISEASE–cause–SYMPTOM

Experimental Results

• Patterns causal reasoning rules

Experimental Results

• Causal relation extraction

Experimental Results

• Extracting causal relations from single sentence and multiple

sentences.

• Extracting implicit causal relations

Experimental Results

Influence Factors

• Influence from the size of labeled training data

Influence Factors

• Influence from the number of bootstrapping rounds and window size

Conclusions

• We propose CausalTriad to incorporate both textual and structural

clues for causal relation discovery from text.

• Experimental results on two datasets demonstrate that:
• CausalTriad is effective for discovering explicit and implicit causal relations

from both single sentence and multiple sentences.

• CausalTriad can generate new causal relation hypotheses through inference.

Thank You!

Any comments and suggestions?

Homepage: http://ir.hit.edu.cn/~sdzhao/ Email: zhaosendong@gmail.com

Sendong (Stan) Zhao Meng Jiang Ming Liu Ting Liu Bing Qin