| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A context-aware approach for progression tracking of medical concepts in electronic medical records
|
Chang, Nai-Wen
|
|
|
58 |
S |
p. S150-S157
|
artikel
|
| 2 |
Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes
|
Khalifa, Abdulrahman
|
|
|
58 |
S |
p. S128-S132
|
artikel
|
| 3 |
Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge
|
Cormack, James
|
|
|
58 |
S |
p. S120-S127
|
artikel
|
| 4 |
A hybrid model for automatic identification of risk factors for heart disease
|
Yang, Hui
|
|
|
58 |
S |
p. S171-S182
|
artikel
|
| 5 |
An automatic system to identify heart disease risk factors in clinical texts over time
|
Chen, Qingcai
|
|
|
58 |
S |
p. S158-S163
|
artikel
|
| 6 |
Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus
|
Stubbs, Amber
|
|
|
58 |
S |
p. S20-S29
|
artikel
|
| 7 |
Annotating risk factors for heart disease in clinical narratives for diabetic patients
|
Stubbs, Amber
|
|
|
58 |
S |
p. S78-S91
|
artikel
|
| 8 |
A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases
|
Kotfila, Christopher
|
|
|
58 |
S |
p. S92-S102
|
artikel
|
| 9 |
Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
|
Stubbs, Amber
|
|
|
58 |
S |
p. S11-S19
|
artikel
|
| 10 |
Automatic de-identification of electronic medical records using token-level and character-level conditional random fields
|
Liu, Zengjian
|
|
|
58 |
S |
p. S47-S52
|
artikel
|
| 11 |
Automatic detection of protected health information from clinic narratives
|
Yang, Hui
|
|
|
58 |
S |
p. S30-S38
|
artikel
|
| 12 |
Combining glass box and black box evaluations in the identification of heart disease risk factors and their temporal relations from clinical records
|
Grouin, Cyril
|
|
|
58 |
S |
p. S133-S142
|
artikel
|
| 13 |
Combining knowledge- and data-driven methods for de-identification of clinical narratives
|
Dehghan, Azad
|
|
|
58 |
S |
p. S53-S59
|
artikel
|
| 14 |
Comparison of UMLS terminologies to identify risk of heart disease using clinical notes
|
Shivade, Chaitanya
|
|
|
58 |
S |
p. S103-S110
|
artikel
|
| 15 |
Coronary artery disease risk assessment from unstructured electronic health records using text mining
|
Jonnagaddala, Jitendra
|
|
|
58 |
S |
p. S203-S210
|
artikel
|
| 16 |
Cover 2: Editorial Board
|
|
|
|
58 |
S |
p. IFC
|
artikel
|
| 17 |
Cover 1/Spine
|
|
|
|
58 |
S |
p. OFC
|
artikel
|
| 18 |
Creation of a new longitudinal corpus of clinical narratives
|
Kumar, Vishesh
|
|
|
58 |
S |
p. S6-S10
|
artikel
|
| 19 |
CRFs based de-identification of medical records
|
He, Bin
|
|
|
58 |
S |
p. S39-S46
|
artikel
|
| 20 |
Ease of adoption of clinical natural language processing software: An evaluation of five systems
|
Zheng, Kai
|
|
|
58 |
S |
p. S189-S196
|
artikel
|
| 21 |
fmi-ii: Table of Contents
|
|
|
|
58 |
S |
p. i-ii
|
artikel
|
| 22 |
Hidden Markov model using Dirichlet process for de-identification
|
Chen, Tao
|
|
|
58 |
S |
p. S60-S66
|
artikel
|
| 23 |
Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2
|
Stubbs, Amber
|
|
|
58 |
S |
p. S67-S77
|
artikel
|
| 24 |
Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models
|
Urbain, Jay
|
|
|
58 |
S |
p. S143-S149
|
artikel
|
| 25 |
Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks
|
Uzuner, Özlem
|
|
|
58 |
S |
p. S1-S5
|
artikel
|
| 26 |
Predicting changes in systolic blood pressure using longitudinal patient records
|
Solomon, John Wes
|
|
|
58 |
S |
p. S197-S202
|
artikel
|
| 27 |
Risk factor detection for heart disease by applying text analytics in electronic medical records
|
Torii, Manabu
|
|
|
58 |
S |
p. S164-S170
|
artikel
|
| 28 |
Textual inference for eligibility criteria resolution in clinical trials
|
Shivade, Chaitanya
|
|
|
58 |
S |
p. S211-S218
|
artikel
|
| 29 |
The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs
|
Roberts, Kirk
|
|
|
58 |
S |
p. S111-S119
|
artikel
|
| 30 |
Using local lexicalized rules to identify heart disease risk factors in clinical notes
|
Karystianis, George
|
|
|
58 |
S |
p. S183-S188
|
artikel
|
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