PMID- 30944913
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220408
IS  - 2574-2531 (Electronic)
IS  - 2574-2531 (Linking)
VI  - 2
IP  - 1
DP  - 2019 Apr
TI  - Natural language processing and recurrent network models for identifying genomic 
      mutation-associated cancer treatment change from patient progress notes.
PG  - 139-149
LID - 10.1093/jamiaopen/ooy061 [doi]
AB  - OBJECTIVES: Natural language processing (NLP) and machine learning approaches 
      were used to build classifiers to identify genomic-related treatment changes in 
      the free-text visit progress notes of cancer patients. METHODS: We obtained 5889 
      deidentified progress reports (2439 words on average) for 755 cancer patients who 
      have undergone a clinical next generation sequencing (NGS) testing in Wake Forest 
      Baptist Comprehensive Cancer Center for our data analyses. An NLP system was 
      implemented to process the free-text data and extract NGS-related information. 
      Three types of recurrent neural network (RNN) namely, gated recurrent unit, long 
      short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to 
      classify documents to the treatment-change and no-treatment-change groups. 
      Further, we compared the performances of RNNs to 5 machine learning algorithms 
      including Naive Bayes, K-nearest Neighbor, Support Vector Machine for 
      classification, Random forest, and Logistic Regression. RESULTS: Our results 
      suggested that, overall, RNNs outperformed traditional machine learning 
      algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of 
      accuracy, precision, recall, and F1 score. In addition, pretrained word embedding 
      can improve the accuracy of LSTM by 3.4% and reduce the training time by more 
      than 60%. DISCUSSION AND CONCLUSION: NLP and RNN-based text mining solutions have 
      demonstrated advantages in information retrieval and document classification 
      tasks for unstructured clinical progress notes.
FAU - Guan, Meijian
AU  - Guan M
AD  - Department of Computer Science, Wake Forest University, Winston-Salem, North 
      Carolina, USA.
AD  - Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, North Carolina, 
      USA.
FAU - Cho, Samuel
AU  - Cho S
AD  - Department of Computer Science, Wake Forest University, Winston-Salem, North 
      Carolina, USA.
AD  - Department of Physics, Wake Forest University, Winston-Salem, North Carolina, 
      USA.
FAU - Petro, Robin
AU  - Petro R
AD  - Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, North Carolina, 
      USA.
FAU - Zhang, Wei
AU  - Zhang W
AD  - Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, North Carolina, 
      USA.
AD  - Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, 
      North Carolina, USA.
FAU - Pasche, Boris
AU  - Pasche B
AD  - Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, North Carolina, 
      USA.
AD  - Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, 
      North Carolina, USA.
FAU - Topaloglu, Umit
AU  - Topaloglu U
AD  - Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, North Carolina, 
      USA.
AD  - Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, 
      North Carolina, USA.
LA  - eng
GR  - P30 CA012197/CA/NCI NIH HHS/United States
GR  - UL1 TR001420/TR/NCATS NIH HHS/United States
PT  - Journal Article
DEP - 20190103
PL  - United States
TA  - JAMIA Open
JT  - JAMIA open
JID - 101730643
PMC - PMC6435007
OTO - NOTNLM
OT  - cancer
OT  - electronic health records
OT  - genomics
OT  - machine learning
OT  - natural language processing
EDAT- 2019/04/05 06:00
MHDA- 2019/04/05 06:01
PMCR- 2019/01/03
CRDT- 2019/04/05 06:00
PHST- 2018/08/19 00:00 [received]
PHST- 2018/11/26 00:00 [revised]
PHST- 2018/12/21 00:00 [accepted]
PHST- 2019/04/05 06:00 [entrez]
PHST- 2019/04/05 06:00 [pubmed]
PHST- 2019/04/05 06:01 [medline]
PHST- 2019/01/03 00:00 [pmc-release]
AID - ooy061 [pii]
AID - 10.1093/jamiaopen/ooy061 [doi]
PST - ppublish
SO  - JAMIA Open. 2019 Apr;2(1):139-149. doi: 10.1093/jamiaopen/ooy061. Epub 2019 Jan 
      3.