TY  - CHAP
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
T2  - 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  - Shams, Boshra
A1  - Wang, Ziqian
A1  - Roine, Timo
A1  - Aydogan, Dogu Baran
A1  - Vajkoczy, Peter
A1  - Lippert, Christoph
A1  - Picht, Thomas
A1  - Fekonja, Lucius Samo
T1  - Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract
JF  - Brain communications
N2  - Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables. 

Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.
KW  - machine learning
KW  - support vector machine
KW  - tractography
KW  - diffusion MRI;
KW  - corticospinal tract
Y1  - 2022
U6  - https://doi.org/10.1093/braincomms/fcac141
SN  - 2632-1297
VL  - 4
IS  - 3
PB  - Oxford University Press
CY  - Oxford
ER  -