IDENTIFYING CHILDHOOD CANCER PATIENTS IN NEED FOR SUPPORT - PSYCHOSOCIAL RISK PREDICTION FROM ELECTRONIC HEALTH RECORDS WITH MACHINE LEARNING

Paivi Lahteenmaki; Akseli Reunamo; Sanna Salanterä; Hans Moen

IDENTIFYING CHILDHOOD CANCER PATIENTS IN NEED FOR SUPPORT - PSYCHOSOCIAL RISK PREDICTION FROM ELECTRONIC HEALTH RECORDS WITH MACHINE LEARNING

Paivi Lahteenmaki, Akseli Reunamo, Sanna Salanterä, Hans Moen

Not published at Aalto University

University of Turku

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

Original language	English
Pages (from-to)	S493-S493
Number of pages	1
Journal	Pediatric blood & cancer
Volume	69
Publication status	Published - Nov 2022
MoE publication type	Not Eligible

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@article{f7ce4b0cdee74c35a3188884e737c738,

title = "IDENTIFYING CHILDHOOD CANCER PATIENTS IN NEED FOR SUPPORT - PSYCHOSOCIAL RISK PREDICTION FROM ELECTRONIC HEALTH RECORDS WITH MACHINE LEARNING",

author = "Paivi Lahteenmaki and Akseli Reunamo and Sanna Salanter{\"a} and Hans Moen",

year = "2022",

month = nov,

language = "English",

volume = "69",

pages = "S493--S493",

journal = "Pediatric blood & cancer",

issn = "1545-5009",

publisher = "Wiley",

}

TY - JOUR

T1 - IDENTIFYING CHILDHOOD CANCER PATIENTS IN NEED FOR SUPPORT - PSYCHOSOCIAL RISK PREDICTION FROM ELECTRONIC HEALTH RECORDS WITH MACHINE LEARNING

AU - Lahteenmaki, Paivi

AU - Reunamo, Akseli

AU - Salanterä, Sanna

AU - Moen, Hans

PY - 2022/11

Y1 - 2022/11

M3 - Meeting Abstract

SN - 1545-5009

VL - 69

SP - S493-S493

JO - Pediatric blood & cancer

JF - Pediatric blood & cancer

ER -