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Mining literature and medical records
1.
Mining literature and
medical records Lars Juhl Jensen
2.
literature mining
3.
exponential growth
4.
5.
6.
some things are
constant
7.
8.
~45 seconds per
paper
9.
computer
10.
as smart as
a dog
11.
teach it specific
tricks
12.
13.
14.
named entity recognition
15.
comprehensive lexicon
16.
orthographic variation
17.
“ black list”
18.
Reflect.ws
19.
augmented browsing
20.
Pafilis, O’Donoghue, Jensen
et al., Nature Biotechnology , 2009 O’Donoghue et al., Journal of Web Semantics , 2010
21.
small molecules
22.
proteins
23.
subcellular compartments
24.
tissues
25.
diseases
26.
information extraction
27.
no access
28.
29.
collaboration
30.
31.
medical record mining
32.
electronic patient journals
33.
psychiatric diseases
34.
F20 F200 Negation
Family
35.
domain specific
36.
patient stratification
37.
38.
39.
comorbidity matrix
40.
41.
detailed phenotype data
42.
thousands of individuals
43.
coarse phenotype data
44.
millions of patients
45.
national discharge registry
46.
6.2 million individuals
47.
66 million admissions
48.
119 million diagnoses
49.
comorbidity matrix
50.
confounding factors
51.
gender
52.
age
53.
obesity
54.
smoking
55.
thousands of known
links
56.
surprising comorbidities
57.
embedded/impacted tooth
58.
neoplasms in oral
cavity
59.
reporting bias
60.
predict future diseases
61.
62.
larsjuhljensen
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