Mining literature and medical records

Lars Juhl Jensen

literature mining

exponential growth

some things are constant

~45 seconds per paper

computer

as smart as a dog

teach it specific tricks

named entity recognition

comprehensive lexicon

orthographic variation

“black list”

Reflect.ws

augmented browsing

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009O’Donoghue et al., Journal of Web Semantics, 2010

small molecules

proteins

subcellular compartments

tissues

diseases

information extraction

no access

collaboration

medical record mining

electronic patient journals

psychiatric diseases

F20

F200

Negation

Family

domain specific

patient stratification

comorbidity matrix

detailed phenotype data

thousands of individuals

coarse phenotype data

millions of patients

national discharge registry

6.2 million individuals

66 million admissions

119 million diagnoses

comorbidity matrix

confounding factors

gender

age

obesity

smoking

thousands of known links

surprising comorbidities

embedded/impacted tooth

neoplasms in oral cavity

reporting bias

predict future diseases

Reflect.wsSune Frankild

Heiko HornEvangelos Pafilis

Michael KuhnReinhardt Schneider

Sean O’Donoghue

LPR-miningAnders B Jensen

Søren Brunak

EPJ-miningFrancisco S RoquePeter B JensenRobert ErikssonHenriette SchmockMarlene DalgaardMassimo AndreattaThomas HansenKaren SøebySøren BredkjærAnders JuulThomas WergeSøren Brunak

Thank you!

larsjuhljensen