Long COVID: Assessment of Neuropsychiatric Symptoms in Children and Adolescents - A Clinical Data Analysis

COVID-19 infections in adults often result in medical, neuropsychiatric, and unspecific symptoms, called Long COVID, and the premorbid functional status cannot be achieved. Regarding the course in children and adolescents, however, reliable data are not yet available.Objective380 children and adolescents/young adults aged between 6 and 21 years, being treated for various psychiatric diseases in an outpatient clinical service, were examined for COVID-19 infections and Long COVID symptoms following a structured protocol.ResultsThree patients had COVID-19; one patient had symptoms of Long COVID in his medical history, but they could not be objectivized in an in-depth neuropsychiatric and neuropsychological assessment.ConclusionsLong COVID seems to occur rarely in children and adolescents. Objectivizing the symptoms is a difficult task that requires various diagnostic considerations.

ClinFlow – An Interactive Application for Clinical Data Mining

Analyzing clinical data comes with many challenges. Medical expertise combined with statistical and programming knowledge must go hand-in-hand when applying data mining methods on clinical datasets. This work aims at bridging the gap between clinical expertise and computer science knowledge by providing an application for clinical data analysis with no requirement for statistical programming knowledge. Our tool allows clinical researchers to conduct data processing and visualization in an interactive environment, thus providing an assisting tool for clinical studies. The application was experimentally evaluated with an analysis of Type 1 Diabetes clinical data. The results obtained with the tool are in line with the domain literature, demonstrating the value of our application in data exploration and hypothesis testing.

Clinical Data Analysis of Nodular Erythema

Objective Investigate the relationship between clinical manifestations and pathological changes of Erythema Nodosum. Subjects and Methods 94 patients diagnosed with erythema nodosum were collected by the clinical data. Results Five etiologies were treated, which was found by p<0.05. It shows that the recovery within one month was statistically significant. 9 of 94 patients were diagnosed with TB infection. 7 patients were found with multiple nuclear giant cells infiltration in the HE pathological films. Conclusion The treatment of erythema nodosum is mainly due to treatment. It shows that there are multinucleated giant cells in the pathology, which may be suspected of tuberculosis infection.

Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement

Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it needs methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare. Results Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover a smaller set of succinct significant patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.

fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to obtain the specificity and sensitivity. This helps to determine the best features for ADHD classification using convolutional neural networks (CNN). The methodology using fALFF and ReHo resulted in an accuracy of 67%, while SBC gained an accuracy between 84% and 86% and sensitivity between 65% and 75%.

Explanation and Prediction of Clinical Data with Imbalanced Class Distribution Based on Pattern Discovery and Disentanglement

Background: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it needs methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods: In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare.Results: Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover a smaller set of succinct significant patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions: In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.