Mining literature and medical records
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Transcript of Mining literature and medical records
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Mining literature and medical records
Lars Juhl Jensen
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literature mining
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exponential growth
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some things are constant
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~45 seconds per paper
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computer
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as smart as a dog
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teach it specific tricks
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named entity recognition
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comprehensive lexicon
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orthographic variation
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“black list”
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Reflect.ws
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augmented browsing
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Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009O’Donoghue et al., Journal of Web Semantics, 2010
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small molecules
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proteins
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subcellular compartments
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tissues
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diseases
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information extraction
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no access
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collaboration
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medical record mining
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electronic patient journals
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psychiatric diseases
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F20
F200
Negation
Family
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domain specific
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patient stratification
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comorbidity matrix
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detailed phenotype data
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thousands of individuals
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coarse phenotype data
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millions of patients
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national discharge registry
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6.2 million individuals
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66 million admissions
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119 million diagnoses
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comorbidity matrix
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confounding factors
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gender
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age
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obesity
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smoking
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thousands of known links
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surprising comorbidities
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embedded/impacted tooth
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neoplasms in oral cavity
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reporting bias
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predict future diseases
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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!
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larsjuhljensen