I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
A Machine Learning Model to Predict Patients' Adherence Beh..:
Masiero, Marianna
;
Spada, Gea Elena
;
Sanchini, Virginia
...
JMIR Research Protocols. 12 (2023) - p. e48852 , 2023
Link:
https://doi.org/10.2196/48852
RT Journal T1
A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial
UL https://suche.suub.uni-bremen.de/peid=cr-10.2196_48852&Exemplar=1&LAN=DE A1 Masiero, Marianna A1 Spada, Gea Elena A1 Sanchini, Virginia A1 Munzone, Elisabetta A1 Pietrobon, Ricardo A1 Teixeira, Lucas A1 Valencia, Mirtha A1 Machiavelli, Aline A1 Fragale, Elisa A1 Pezzolato, Massimo A1 Pravettoni, Gabriella PB JMIR Publications Inc. YR 2023 SN 1929-0748 JF JMIR Research Protocols VO 12 SP e48852 LK http://dx.doi.org/https://doi.org/10.2196/48852 DO https://doi.org/10.2196/48852 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)