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Using Semantic Dependencies to Mine Depressive Symptoms from

Consultation Records

Chung-Hsien Wu and Liang-Chih, YuNational Cheng Kung University

Fong-Lin JangChi-Mei Medical Center

IEEE INTELLIGENT SYSTEMS NOVEMBER/DECEMBER 2005

D a t a M i n i n g i n B i o i n f o r m a t i c s

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A Example: Consultation Records

During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do?

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A Example: Consultation Records

<Depressed>During past few months, I always felt upset. My life is full of sadness.</Depressed> <Suicide>This caused me to attempt to kill myself several times.</Suicide> <Anxiety>Now, I often worry about some minor matters.</Anxiety> <Others>What can I do?</Others>

<Depressed>….</Depressed> is a discourse segment- that is, successive sentences describing the same depressive symptom.

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A Example: Consultation Records

<Depressed>During past few months, I always felt upset. My life is full of sadness.</Depressed> <Suicide>This caused me to attempt to kill myself several times.</Suicide> <Anxiety>Now, I often worry about some minor matters.</Anxiety> <Others>What can I do?</Others>

Cause-Effect

Temporal Sequence

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a Good Problem?

During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do?

Marked semantic label Identify discourse segments Discover semantic relations

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Domain Knowledge

HDRS (1960) is the most prominent rating scale to assess symptoms of depression.

<Others> is to handle the out-of-domain symptoms.

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Framework for mining depressive symptoms

Marked semantic label

Identify discourse segments

Discover semantic relations

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Semantic dependency graph (SDG)

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Semantic dependency model f is the function that maps a word

to a concept in HowNet. For example : “ worry” and “conce

rn” , f(worry)=f(concern)

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Inferring semantic labels

“worry about some minor matters” (<Anxiety>) “worry about many health problems” (<Hypochondriasis>).

H=(l1,l2,…lk-1) is the semantic label’s history.

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Inferring semantic labelsBigram assumption to approximate P(l1,l2,…lk-1)

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Inferring semantic labels

P(lk|H, D) as a semantic label’s confidence score. The best hypothesis is accepted if its confidence score i

s over the threshold Tlabel. Otherwise, the sentence will be labeled <Others>, which mea

ns the sentence is out-of-domain or ambiguous.

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Identifying discourse segments

Discourse segments: group of successive sentences with the same semantic l

abel. First use a reestimation process to resolve the <Ot

hers> labels. For each <Others>, the process computes the strength of lexical

cohesion (LC) between <Others> and its surrounding. i.e., The previous and following n semantic labels. The n is a window size.

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Identifying discourse segments

The LC (lexical cohesion) between two semantic labels is measured by the similarity between their corresponding SDGs.

Each semantic dependency in an SDG has the format ( modifer, head, relmodifer,head )

SimM: modifer node score. simH: head node score. simR: relation score. 1,2,3, are the weightings of simM,simH and simR.

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Identifying discourse segments

r is concept hierarchy in HowNet.

Z is normalization factors.

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Identifying discourse segments

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Discovering semantic relations

Cause-effect—because, therefore Contrast—however, but Joint—and, also Temporal sequence—before, after

A huge knowledge base like WordNet can solve part of the problem.

But contrastive relations are not easy collected.

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Discovering semantic relations

Consider positive and negative (depressive) symptoms (P-N pairs). HowNet doesn’t include many such pairs.

Affective Norms for English words (ANEW) list provides a set of normative emotional ratings for a large number of words in the English language.

Using an automated method to align ANEW with HowNet and then extract the P-N pairs.

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Discovering semantic relations

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Experiment

Consultation records from PsychPark (www.psychpark.org)

Total data set included 1,514 consultation records. 1,208 for training. 306 for testing.

Three experienced psychiatric physicians to help annotate those records. (golden standard)

Using majority-vote mechanism to handle disagreements among the physicians.

The experiments compare with noisy-channel (NC) models (i.e., non building SDG structure)

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Evaluation of semantic label inference

Expert: A physician wasn’t involved in creating the golden standard.

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Evaluation of discourse segment identification

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Evaluation of semantic relation discovery

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Conclusion

The semantic-dependency structure (SDG) captures the intra-sentential information,

The lexical cohesion captures the inter-sentential information to identifying discourse segments.

The domain ontology (HowNet,WordNet and so on) models the domain knowledge and discovers the semantic relations.

Integrating these knowledge sources is a promising approach to the mining task.

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Conclusion

<Depressed>During past few months, I always felt upset. My life is full of sadness.</Depressed> <Suicide>This caused me to attempt to kill myself several times.</Suicide> <Anxiety>Now, I often worry about some minor matters.</Anxiety> <Others>What can I do?</Others>