RT Book, Section
A1 Celi, Leo Anthony
A1 Gruhl, Daniel
A1 Ishii, Euma
A1 Shivade, Chaitanya
A1 Terdiman, Joseph
A1 Wu, Joy Tzung-yu
A2 Hashimoto, MD, MS, Daniel A.
A2 Meireles, MD, Ozanan R.
A2 Rosman, PhD, Guy
SR Print(0)
ID 1180350067
T1 Natural Language Processing
T2 Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice
YR 2021
FD 2021
PB McGraw-Hill Education
PP New York, NY
SN 9781260452730
LK accesssurgery.mhmedical.com/content.aspx?aid=1180350067
RD 2024/04/23
AB HIGHLIGHTSNatural  language  processing  (NLP)  is  a  collection  of  tools  and  computer  algorithmic  techniques  that  aim  to  help  humans  “structure”  and  gain  an  in-depth  understanding  of  free  text  information.Overview  of  different  types  of  NLP  tools:Vocabulary-  and  rule-based  NLP  is  the  oldest  but  most  easily  interpretable  type  of  NLP.  Complex  clinical  NLP  pipelines  take  a  lot  of  resources  and  years  to  build  and  are  often  difficult  to  adapt  to  different  clinical  domains.  However,  simple  look-up–type  techniques  can  be  useful  in  many  clinical  auditing  cases  where  precision  is  more  important  than  recall.Supervised  NLP  is  powerful  as  long  as  there  is  a  large  enough  human  labeled  data  set  to  train  the  machine  learning  model.  However,  task-specific  and  large  well-labeled  data  sets  take  substantial  clinical  resources  to  curate.With  unsupervised  NLP,  there  is  no  need  for  labeling  because  these  machine  learning  models  can  automatically  discover  patterns  in  the  data  and  propose  groups  or  classes.  However,  a  human  needs  to  interpret  the  resulting  groups  to  figure  out  the  “why”  and  “what.”Expert-in-the-loop  NLP:  What  if  we  make  the  experts  more  efficient  at  helping  the  machine  learn  a  task?  The  challenge  is  to  present  questions  or  uncertainties  from  the  machine  models  to  the  human  in  a  user-friendly  and  interactive  manner.In  health  care,  there  is  no  one-size-fits-all  NLP  solution.  There  are  many  tasks  in  the  clinical  domain  amenable  to  different  types  or  combinations  of  NLP  methods.  Understanding  the  performance  requirements  of  the  clinical  task  and  the  limitations  of  different  NLP  tools  can  help  with  implementing  the  most  appropriate  NLP  solution.