Air pollution and attention in Polish schoolchildren with and without ADHD

Background: Development and functioning of attention—a key component of human cognition—can be affected by en vironmental factors. We investigated whether long- and short-term exposure to particulate matter with aerodynamic diameter < 10 μm (PM10) and nitrogen dioxide (NO2) are related to attention in 10- to 13-year-old children living in Polish towns recruited in the NeuroSmog case-control study. Methods: We investigated associations between air pollution and attention separately in children with attention deficit hyperactivity disorder (ADHD, n = 187), a sensitive, at-risk population with impaired attention and in population based typically developing children (TD, n = 465). Alerting, orienting, and executive aspects of attention were mea sured using the attention network test (ANT), while inhibitory control was measured with the continuous performance test (CPT). We assessed long-term exposure to NO2 and PM10 using novel hybrid land use regression (LUR) models. Short-term exposures to NO2 and PM10 were assigned to each subject using measurements taken at the air pollution monitoring station nearest to their home address. We tested associations for each exposure-outcome pair using adjusted linear and negative binomial regressions. Results: We found that long-term exposures to both NO2 and PM10 were associated with worse visual attention in chil dren with ADHD. Short-term exposure to NO2 was associated with less efficient executive attention in TD children and more errors in children with ADHD. It was also associated with shorter CPT response times in TD children; however, this effect was accompanied by a trend towards more CPT commission errors, suggestive of more impulsive performance in these subjects. Finally, we found that short-term PM10 exposure was associated with fewer omission errors in CPT in TD children. Conclusions: Exposure to air pollution, especially short-term exposure to NO2, may have a negative impact on attention in children. In sensitive populations, this impact might be different than in the general population

PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries

We introduce an approach called PLENARY (exPlaining bLack-box modEls in Natural lAnguage thRough fuzzY linguistic summaries), which is an explainable classifier based on a data-driven predictive model. Neural learning is exploited to derive a predictive model based on two levels of labels associated with the data. Then, model explanations are derived through the popular SHapley Additive exPlanations (SHAP) tool and conveyed in a linguistic form via fuzzy linguistic summaries. The linguistic summarization allows translating the explanations of the model outputs provided by SHAP into statements expressed in natural language. PLENARY accounts for the imprecision related to model outputs by summarizing them into simple linguistic statements and for the imprecision related to the data labeling process by including additional domain knowledge in the form of middle-layer labels. PLENARY is validated on preprocessed speech signals collected from smartphones from patients with bipolar disorder and on publicly available mental health survey data. The experiments confirm that fuzzy linguistic summarization is an effective technique to support meta-analyses of the outputs of AI models. Also, PLENARY improves explainability by aggregating low-level attributes into high-level information granules, and by incorporating vague domain knowledge into a multi-task sequential and compositional multilayer perceptron. SHAP explanations translated into fuzzy linguistic summaries significantly improve understanding of the predictive modelling process and its outputs.Small Grants Scheme within the research project "Bipolar disorder prediction with sensor-based semi-supervised Learning (BIPOLAR)" NOR/SGS/BIPO LAR/0239/2020-00European Commission RPMA.01.02.00-14-5706/16-00Systems Research Institute Polish Academy of SciencesJuan de la Cierva Incorporacion grant - MCIN/AEI IJC2019-039152-IGoogle Research Scholar ProgramItalian Ministry of University and Research through the European PON project AIM (Attraction and International Mobility) 185241

Incremental Semi-Supervised Fuzzy C-Means for Bipolar Disorder Episode Prediction

Bipolar disorder is a chronic mental illness characterized with changing episodes (depression, mania, mixed state, euthymia). In the recent years, smartphone becomes an increasingly important tool in the early prediction of a starting episode. Usually, the state of the art research applies supervised learning methods and first of all, limits the dataset only to those days that have valid labels (from the psychiatric assessment), secondly, ignores the time structure of data. We pursue an alternative approach and apply incremental semi-supervised fuzzy learning without the need to limit the dataset only to labeled data. As observed, it is able to adapt the model as new data arrive. Preliminary results show that the algorithm is able to detect some of healthy episodes (euthymia) and disease episodes even when only 25% of labels are available

Intelligent analysis of data streams about phone calls for bipolar disorder monitoring

Voice features from everyday phone conversations are regarded as a sensitive digital marker of mood phases in bipolar disorder. At the same time, although acoustic data collected from smartphones are relatively large, their psychiatric labelling is usually very limited, and there is still a need for intelligent and interpretable approaches to process such multiple data streams with a low percentage of labelling. Furthermore, both acoustic data and psychiatric labels are subject to several sources of uncertainty (e.g., irregular phone usage, background noises, subjectivity in psychiatric evaluation). To cope with these characteristics of an acoustic data stream, this paper introduces an intelligent qualitative and quantitative analysis based on the Dynamic Incremental Semi-Supervised Fuzzy C-Means algorithm (DISSFCM) for supporting bipolar disorder monitoring. The proposed approach is illustrated with real-life data collected from smartphones and psychiatric assessments of a bipolar disorder patient. Analysis of the dynamics of data streams basing on the cluster prototypes from fuzzy semi-supervised learning is a highly novel approach. It is also showed that the DISSFCM algorithm obtains relatively high classification performance (accuracy ranging from 0.66 to 0.76) already with 25% labelling percentage, thanks to the splitting mechanism that is adapting the number of clusters to the structure of data

Fuzzy Linguistic Summaries for Explaining Online Semi-Supervised Learning

Intelligent systems for the medical domain often require processing data streams that evolve over time and are only partially labeled. At the same time, the need for explanations is of utmost importance not only due to various regulations, but also to increase trust among systems' users. In this work, an online data-driven learning method with focus on the explainability of evolving models equipped with incremental semi-supervised learning algorithms is considered. The proposed method combines: (i) the Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM) algorithm to incrementally classify subsets of data; with (ii) Linguistic Summarization, which provides explanations of the classification results in terms of short sentences in a natural language. The approach has been illustrated for streaming data collected from voice calls of patients affected by Bipolar Disorder. The results show the effectiveness of the proposed method in classifying instances belonging to healthy and affective states, and explaining the approximate reasoning behind the classification of new acoustic data related to patients

Explaining smartphone-based acoustic data in bipolar disorder: Semi-supervised fuzzy clustering and relative linguistic summaries

Smartphones enable to collect large data streams about phone calls that, once combined with Computational Intelligence techniques, bring great potential for improving the monitoring of patients with mental illnesses. However, the acoustic data streams recorded in uncontrolled environments are dynamically changing due to various sources of uncertainty. In addition, such acoustic data are usually difficult to interpret by psychiatrists. Within this study, we propose an approach based on Linguistic Summaries with Fuzzy Clustering (LS-FC) aiming at the development of human-consistent and easily interpretable summaries about relations between acoustic data and mental state of a patient affected by Bipolar Disorder, e.g., Most calls in the state of hypomania have low loudness compared to the state of euthymia [T = 1]. To capture the dynamics of acoustic data streams, we apply a dynamic incremental semi-supervised fuzzy clustering that synthesizes data into clusters. These clusters are represented by prototypes which are used for the construction of the membership functions describing linguistic terms e.g., low loudness, and then, linguistic summaries. The main contribution of this paper is the incorporation of information about clusters’ prototypes in the generation of linguistic summaries. The primary goal of this research is explainability. The semi-supervised learning algorithm is used mainly for deriving clusters and building improved linguistic summaries. Numerical results indicate that linguistic summaries provide intuitive and clear information about voice features in a patient's affective state and they are consistent with clinical observation. In particular, during most calls in hypomania/mania both the quality of the patient's voice and the dynamics of change in the spectrum signal reflected in spectral flux are low compared to euthymia. The proposed approach enables to summarize large data streams into meaningful descriptions that, although relatively simple, offer information granules that are very intuitive for clinicians and are promising to support the smartphone-based monitoring of bipolar disorder patients to inform about the potential change of mental state

Confidence path regularization for handling label uncertainty in semi-supervised learning: use case in bipolar disorder monitoring

Semi-supervised learning has gained great interest because of its ability to combine unlabeled data with-potentially few-labeled observations in a training process. However, in some application contexts, one can question whether all available labels are equally valid. For example, in the context of bipolar disorder (BD) remote monitoring, a common practice is to extrapolate the psychiatrist's assessment onto some fixed time window surrounding the visit, the so-called ground truth period. In consequence, all data from this period are labeled with the same category. Such an approach may potentially result in misguided supervision affecting the model's performance. In this paper, we consider the problem of label uncertainty, assuming that the labels are crisp, but they may be assigned to particular observations with varying confidence. We propose a novel method called Confidence Path Regularization (CPR) that incorporates this uncertainty into the fuzzy c-means semi-supervised learning. The proposed CPR approach is a novel method for automatic, data-driven handling of label uncertainty. We achieve it by estimating the confidence factor for each labeled observation. In addition, CPR allows for the exploration of potential class-specific patterns in the adjusted confidence. The proposed method is illustrated with experiments on partially labeled data about speech characteristics collected from smartphone application for BD monitoring. In this particular applied scenario, we also use additional contextual data to improve the construction of confidence paths. It is shown that the proposed CPR approach enables to reflect the varying confidence in labels as compared with the nominal approach which assigns the majority of observations to the same class associated with relevant ground truth perio

Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder

Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients. Nonetheless, the psychiatric assessment of the patient’s mental state is typically a sporadic occurrence that takes place every few months. Consequently, only a slight fraction of the acoustic data is labeled and applicable for supervised learning. The majority of the related work on mental health monitoring limits the considerations only to labeled data using a predefined ground-truth period. On the other hand, semi-supervised methods make it possible to utilize the entire dataset, exploiting the regularities in the unlabeled portion of the data to improve the predictive power of a model. To assess the applicability of semi-supervised learning approaches, we discuss selected state-of-the-art semi-supervised classifiers, namely, label spreading, label propagation, a semi-supervised support vector machine, and the self training classifier. We use real-world data obtained from a bipolar disorder patient to compare the performance of the different methods with that of baseline supervised learning methods. The experiment shows that semi-supervised learning algorithms can outperform supervised algorithms in predicting bipolar disorder episodes