TY  - JOUR
A1  - Adnan, Hassan Sami
A1  - Srsic, Amanda
A1  - Venticich, Pete Milos
A1  - Townend, David M.R.
T1  - Using AI for mental health analysis and prediction in school surveys
JF  - European journal of public health
N2  - Background:
Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models.

Methods:
For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases.
Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning
can then be deployed to classify specific behaviour and mental health patterns.

Results:
We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through
rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project.

Conclusions:
This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns
of unknown effects.
KW  - ethics
KW  - artificial intelligence
KW  - adolescent
KW  - child
KW  - confidentiality
KW  - health personnel
KW  - mental disorders
KW  - mental health
KW  - personal satisfaction
KW  - privacy
KW  - school (environment)
KW  - statutes and laws
KW  - public health medicine
KW  - surveillance
KW  - medical
KW  - prevention
KW  - datasets
KW  - machine learning
KW  - supervised machine learning
KW  - data analysis
Y1  - 2020
U6  - https://doi.org/10.1093/eurpub/ckaa165.336
SN  - 1101-1262
SN  - 1464-360X
VL  - 30
SP  - V125
EP  - V125
PB  - Oxford Univ. Press
CY  - Oxford [u.a.]
ER  - 
TY  - JOUR
A1  - Ermakova, Tatiana
A1  - Fabian, Benjamin
A1  - Zarnekow, Ruediger
T1  - Improving Individual Acceptance of Health Clouds through Confidentiality Assurance
JF  - Applied clinical informatics
N2  - Background: Cloud computing promises to essentially improve healthcare delivery performance. However, shifting sensitive medical records to third-party cloud providers could create an adoption hurdle because of security and privacy concerns. Methods: We empirically investigate our research question by a survey with over 260 full responses. For the setting with a high confidentiality assurance, we base on a recent multi-cloud architecture which provides very high confidentiality assurance through a secret-sharing mechanism: Health information is cryptographically encoded and distributed in a way that no single and no small group of cloud providers is able to decode it.
KW  - Cloud computing
KW  - cloud service
KW  - cloud storage
KW  - data security
KW  - privacy
KW  - confidentiality
KW  - acceptance process
Y1  - 2016
U6  - https://doi.org/10.4338/ACI-2016-07-RA-0107
SN  - 1869-0327
VL  - 7
SP  - 983
EP  - 993
PB  - Schattauer
CY  - Stuttgart
ER  -