PMID- 34042745
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20210531
LR  - 20210531
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 281
DP  - 2021 May 27
TI  - Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon 
      Comprehend Medical.
PG  - 258-262
LID - 10.3233/SHTI210160 [doi]
AB  - Extracting meaningful information from clinical notes is challenging due to their 
      semi- or unstructured format. Clinical notes such as discharge summaries contain 
      information about diseases, their risk factors, and treatment approaches 
      associated to them. As such, it is critical for healthcare quality as well as for 
      clinical research to extract those information and make them accessible to other 
      computerized applications that rely on coded data. In this context, the goal of 
      this paper is to compare the automatic medical entity extraction capacity of two 
      available entity extraction tools: MetaMap (MM) and Amazon Comprehend Medical 
      (ACM). Recall, precision and F-score have been used to evaluate the performance 
      of the tools. The results show that ACM achieves higher average recall, average 
      precision, and average F-score in comparison with MM.
FAU - Shah-Mohammadi, Fatemeh
AU  - Shah-Mohammadi F
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Cui, Wanting
AU  - Cui W
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
FAU - Finkelstein, Joseph
AU  - Finkelstein J
AD  - Icahn School of Medicine at Mount Sinai, New York, NY, USA.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
OTO - NOTNLM
OT  - Amazon Comprehend Medical (ACM)
OT  - Clinical documents
OT  - Entity Extraction
OT  - MetaMap (MM)
EDAT- 2021/05/28 06:00
MHDA- 2021/05/28 06:01
CRDT- 2021/05/27 12:27
PHST- 2021/05/27 12:27 [entrez]
PHST- 2021/05/28 06:00 [pubmed]
PHST- 2021/05/28 06:01 [medline]
AID - SHTI210160 [pii]
AID - 10.3233/SHTI210160 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2021 May 27;281:258-262. doi: 10.3233/SHTI210160.