Risk assessment of psychiatric complications in infectious diseases: CALCulation of prognostic indices on example of COVID-19.

Introduction: Factors such as coronavirus neurotropism, which is associated with a massive increase in pro-inflammatory molecules and neuroglial reactivity, along with experiences of intensive therapy wards, fears of pandemic, and social restrictions, are pointed out to contribute to the occurrence of neuropsychiatric conditions. Aim: The aim of this study is to evaluate the role of COVID-19 inflammation-related indices as potential markers predicting psychiatric complications in COVID-19. Methods: A total of 177 individuals were examined, with 117 patients from a temporary infectious disease ward hospitalized due to COVID-19 forming the experimental group and 60 patients from the outpatient department showing signs of acute respiratory viral infection comprising the validation group. The PLR index (platelet-to-lymphocyte ratio) and the CALC index (comorbidity + age + lymphocyte + C-reactive protein) were calculated. Present State Examination 10, Hospital Anxiety and Depression Scale, and Montreal Cognitive Assessment were used to assess psychopathology in the sample. Regression and Receiver operating characteristic (ROC) analysis, establishment of cutoff values for the COVID-19 prognosis indices, contingency tables, and comparison of means were used. Results: The presence of multiple concurrent groups of psychopathological symptoms in the experimental group was associated (R² = 0.28, F = 5.63, p < 0.001) with a decrease in the PLR index and a simultaneous increase in CALC. The Area Under Curve (AUC) for the cutoff value of PLR was 0.384 (unsatisfactory). For CALC, the cutoff value associated with an increased risk of more psychopathological domains was seven points (sensitivity = 79.0%, specificity = 69.4%, AUC = 0.719). Those with CALC > 7 were more likely to have disturbances in orientation (χ² = 13.6; p < 0.001), thinking (χ² = 7.07; p = 0.008), planning ability (χ² = 3.91; p = 0.048). In the validation group, an association (R²McF = 0.0775; p = 0.041) between CALC values exceeding seven points and the concurrent presence of pronounced anxiety, depression, and cognitive impairments was demonstrated (OR = 1.52; p = 0.038; AUC = 0.66). Discussion: In patients with COVID-19, the CALC index may be used for the risk assessment of primary developed mental disturbances in the context of the underlying disease with a diagnostic threshold of seven points.

Assessment of a Person's Emotional State Based on His or Her Posture Parameters.

This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware-software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases.

Assessment of the Status of Patients with Parkinson's Disease Using Neural Networks and Mobile Phone Sensors.

Parkinson's disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient's condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone's sensors) and interactive (data directly entered by the user). For the classification of the patient's condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.