RT Book, Section
A1 Saltz, Joel
A2 Kaushansky, Kenneth
A2 Prchal, Josef T.
A2 Burns, Linda J.
A2 Lichtman, Marshall A.
A2 Levi, Marcel
A2 Linch, David C.
SR Print(0)
ID 1178736900
T1 Application of Big Data and Deep Learning in Hematology
T2 Williams Hematology, 10e
YR 2021
FD 2021
PB McGraw-Hill Education
PP New York, NY
SN 9781260464122
LK hemonc.mhmedical.com/content.aspx?aid=1178736900
RD 2024/04/19
AB SUMMARYArtificial  intelligence  (AI)  is  transforming  both  biomedical  research  and  the  way  we  practice  medicine.  It  is  essential  for  hematologists  to  understand  the  principles  underlying  AI  (or  deep  learning)  methods  to  understand  and  evaluate  the  results  produced  by  software  systems  and  instruments  that  rely  on  these  methods.  We  will  see  that  simple  mathematical  principles,  leveraging  the  huge  increase  in  computational  speed  available  on  modern  computers,  is  the  underpinning  of  AI  methods.The  core  technology  behind  the  rapid  advances  in  AI  is  the  neural  network.  In  this  chapter,  we  provide  a  hematology-focused  overview  of  neural  networks.  The  goal  is  to  impart  an  understanding  of  how  neural  network  techniques  can  be  most  effectively  applied  to  hematology.  This  chapter  surveys  core  neural  network  concepts,  presents  hematology-related  examples,  and  provides  references  to  the  rapidly  expanding  literature.  Many  different  methodologies  can  be  used  in  the  art  and  the  science  of  AI.  This  chapter  outlines  several  of  the  most  important  techniques  currently  in  use.  As  will  be  described  in  this  chapter,  neural  networks  share  many  principles  in  common  with  commonly  used  techniques  such  as  linear  regression  and  logistic  regression.