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  - Pick, Leonie
A1  - Effenberger, Frederic
A1  - Zhelavskaya, Irina
A1  - Korte, Monika
T1  - A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements
JF  - Earth and Space Science
N2  - Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003)
KW  - geomagnetic observatory data
KW  - geomagnetic storm drivers
KW  - historical geomagnetic storms
KW  - supervised machine learning
Y1  - 2019
U6  - https://doi.org/10.1029/2019EA000726
SN  - 2333-5084
VL  - 6
SP  - 2000
EP  - 2015
PB  - American Geophysical Union
CY  - Malden, Mass.
ER  -