Authors:
Mikko Nuutinen
1
;
2
;
Sonja Korhonen
2
;
Anna-Maria Hiltunen
2
;
Ira Haavisto
3
;
2
;
Paula Poikonen-Saksela
4
;
Johanna Mattson
4
;
Haridimos Kondylakis
5
;
Ketti Mazzocco
6
;
7
;
Ruth Pat-Horenczyk
8
;
Berta Sousa
9
and
Riikka-Leena Leskelä
2
Affiliations:
1
Haartman Institute, University of Helsinki, Helsinki, Finland
;
2
Nordic Healthcare Group, Helsinki, Finland
;
3
Laurea University of Applied Sciences, Sustainable and Versatile Social and Health Care, Vantaa, Finland
;
4
Helsinki University Hospital Comprehensive Cancer Center and Helsinki University, Finland
;
5
FORTH-ICS, Heraklion, Greece
;
6
Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy
;
7
Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
;
8
Paul Baerwald School of Social Work and Social Welfare, The Hebrew University of Jerusalem, Jerusalem, Israel
;
9
Champalimaud Clinical Centre, Breast Unit, Champalimaud Centre for the Unknown, Champalimaud Foundation, Lisboa, Portugal
Keyword(s):
Clinical Decision Support System, Breast Cancer, Resilience, Machine Learning.
Abstract:
Proper and well-timed interventions may improve breast cancer patient adaptation, resilience and quality of life (QoL) during treatment process and time after disease. The challenge is to identify those patients who would benefit most from a particular intervention. The aim of this study was to measure whether the machine learning prediction incorporated in the clinical decision support system (CDSS) improves clinicians’ performance to predict patients’ QoL during treatment process. We conducted an experimental setup in which six clinicians used CDSS and predicted QoL for 60 breast cancer patients. Each patient was evaluated both with and without the aid of machine learning prediction. The clinicians were also open-ended interviewed to investigate the usage and perceived benefits of CDSS with the machine learning prediction aid. Clinicians’ performance to evaluate the patients’ QoL was higher with the aid of machine learning predictions than without the aid. AUROC of clinicians was .
777 (95% CI .691 − .857) with the aid and .755 (95% CI .664 − .840) without the aid. When the machine learning model’s prediction was correct, the average accuracy (ACC) of the clinicians was .788 (95% CI .739 − .838) with the aid and .717 (95% CI .636 − .798) without the aid.
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