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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Moving Towards Ethical-Practical Recommendations for Alzheimer's Disease Prediction: Addressing Interindividual,
Silke Schicktanz1, Zümrüt Alpinar-Segawa1,2, Natalie Ulitsa1,3
1Department of Medical Ethics and History of Medicine, University Medical Center Göttingen, Göttingen, Germany.
Abstract:
Biomarkers for predicting Alzheimer's disease (AD) are advancing and their implementation in various healthcare systems is imminent. There is a need for ethical standards addressing information needs, socio-ethical concerns, and expectations of healthy and at-risk persons. We present an ethical approach that integrates different existing ethical frameworks and discussion of our empirical, cross-cultural findings in a multi-layered perspective by addressing three levels. The micro-level focuses on the communication between counseling professionals, persons at risk or in an early stage of dementia, and family members. The meso-level addresses interprofessional cooperation and exchange as a key element for best person-centered care. The macro-level considers public health promotion, the media, and public-funded research. This approach allows to address key ethical concepts including beneficence, non-maleficence, autonomy, informational self-determination, empowerment, and justice. Our contribution specifically examines the ethical challenges associated with AD prediction by means of biomarkers, based on insights from a German-Israeli comparison, and promotes a transdisciplinary discussion across different healthcare contexts. We propose a reflection on three levels to go beyond the clinical counseling context and to consider the rapidly evolving field of biomarkers in the coming years. Our ethical-practical recommendations should not be considered final, but rather procedural and will require continuous adaptation regarding culturally varying practices, new algorithms, meta-analyses, and re-evaluation of established recommendations.
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