RISK PREDICTION OF 30-DAY MORTALITY AFTER STROKE USING MACHINE LEARNING TECHNIQUES: A NATIONWIDE REGISTRY-BASED COHORT STUDY

Research output: Contribution to journal › Meeting Abstract › peer-review

Original language	English
Pages (from-to)	75-75
Journal	International Journal Of Stroke
Volume	15
Issue number	1_SUPPL
Publication status	Published - Nov 2020

Cite this

@article{84944a4e8af34f9b86ec08faa37f6285,

title = "RISK PREDICTION OF 30-DAY MORTALITY AFTER STROKE USING MACHINE LEARNING TECHNIQUES: A NATIONWIDE REGISTRY-BASED COHORT STUDY",

author = "W. Wang and N. Peek and A. Douiri and V. Curcin and C. Wolfe and B. Bray",

year = "2020",

month = nov,

language = "English",

volume = "15",

pages = "75--75",

journal = "International Journal Of Stroke",

issn = "1747-4930",

publisher = "SAGE Publications Ltd",

number = "1_SUPPL",

}

TY - JOUR

T1 - RISK PREDICTION OF 30-DAY MORTALITY AFTER STROKE USING MACHINE LEARNING TECHNIQUES: A NATIONWIDE REGISTRY-BASED COHORT STUDY

AU - Wang, W.

AU - Peek, N.

AU - Douiri, A.

AU - Curcin, V.

AU - Wolfe, C.

AU - Bray, B.

PY - 2020/11

Y1 - 2020/11

M3 - Meeting Abstract

SN - 1747-4930

VL - 15

SP - 75

EP - 75

JO - International Journal Of Stroke

JF - International Journal Of Stroke

IS - 1_SUPPL

ER -