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As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant. Protected attributes, such as gender, play an important role in PD diagnosis development. However, analysis of sub-group populations stemming from different genders is seldom taken into consideration in ML models' development or the performance assessment for PD detection. In this work, we perform a systematic analysis of the detection ability for gender sub-groups in a multi-center setting of a previously developed ML algorithm based on power spectral density (PSD) features of rs-EEG. We find significant differences in the PD detection ability for males and females at testing time (80.5% vs. 63.7% accuracy) and significantly higher activity for a set of parietal and frontal EEG channels and frequency sub-bands for PD and non-PD males that might explain the differences in the PD detection ability for the gender sub-groups.

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha} and \b{eta}) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically validated frequency bands, and feature selection before evaluating the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.

Background The aim of the study was to explore patients’ attitudes towards voluntary and involuntary hospitalization in Norway, and predictors for involuntary patients who wanted admission. Methods A multi-centre study of consecutively admitted patients to emergency psychiatric wards over a 3 months period in 2005–06. Data included demographics, admission status (voluntary / involuntary), symptom levels, and whether the patients expressed a wish to be admitted regardless of judicial status. To analyse predictors of wanting admission (binary variable), a generalized linear mixed modelling was conducted, using random intercepts for the site, and fixed effects for all variables, with logit link-function. Results The sample comprised of 3.051 patients of witch 1.232 (40.4%) were being involuntary hospitalised. As expected 96.5% of the voluntary admitted patients wanted admission, while as many as 29.7% of the involuntary patients stated that they wanted the same. The involuntary patients wanting admission were less likely to be transported by police, had less aggression, hallucinations and delusions, more depressed mood, less use of drugs, less suicidality before admission, better social functioning and were less often referred by general practitioners compared with involuntary patients who did not want admission. In a multivariate analysis, predictors for involuntary hospitalization and wanting admission were, not being transported by police, less aggression and less use of drugs. Conclusions Almost a third of the involuntary admitted patients stated that they actually wanted to be hospitalized. It thus seems to be important to thoroughly address patients’ preferences, both before and after admission, regarding whether they wish to be hospitalized or not.

Additional file 2. Screening questions, eligibility criteria and data charting.

Additional file 1. Search strategy.