## 5 Discussion and Conclusions  
In general, a relatively small textual dataset referring to the attitude of people with anorexia towards body image was available (Experiment 1--research/control group size: 115/85). This fact also limited the possibility of expanding the model architecture--the best results were obtained with a relatively simple architecture (two hidden layers of five GRU cells each). The average best performance for the three expert flags was 80%. Though the result is not ideal, it does not exclude the practical utilizing of the proposed method. For the sentiment flag, the results were much worse. However, our observation indicates using various variants of learning the selected model for the best results. This study points out that the obtained values were received without the use of general language models. Based on the results of the linguistic analysis, significant differences were found in the statements of people suffering from anorexia and healthy people. Therefore, the authors doubted the validity of using general language models, which are currently very popular and considered to be the state of the art in NLP methods. But we would like to emphasize that this is an open issue for further research. Additionally, in future research, significant work should be put into receiving the best quantitative results without losing the characteristic features of the anorexia patients' notes. An important aspect is also the explainability of decisions made by the model. Known explainability mechanisms for RNN models can be based on backpropagation through time (BPTT), gradients-based methods, the attention mechanism, layer-wise relevance propagation (LRP), or the SHapley Additive exPlanations (SHAP) [41]. However, in the case of the body image notes, we are dealing with subjective, unstructured statements, which makes them difficult to interpret. We must also consider the ambiguity of the  
Figure 3: The results of the questionnaire according to the Likert scale. words used and their subjective meaning for a given person. This refers to the so-called projective psychological methodology, which has arisen from consultations with specialists in eating disorders, where the examined person speaks freely on a given topic by choosing words arbitrarily. Due to the difficulty of interpreting the SHAP charts in this particular application, the authors refrained from using these methods in favor of building a linguistic profile of the examined person using the dictionary methods. The combination of these approaches gives valuable information about the condition of a patient in the opinion of the direct medical personnel. The main emphasis in this work was on the psychological aspects of anorexia, due to the authors' awareness of the lack of experience of the first contact staff in specialized issues related to eating disorders and in the use of NLP models and methods. The developed tools can be regarded as computer-assisted tools for the rapid assessment of the human condition. Using the participants' notes, the psychologist can generate results that provide additional information about the patient's condition. The survey shows that the developed approaches using NLP were assessed as the most useful by psychologists specializing in the treatment of eating disorders. Slightly worse results were obtained from the group of school psychologists and nurses. The nurses found general indicators (healthy/sick) to be the most useful. Detailed indicators concerning the analysis of the examined person's language are beyond the scope of interest of the nurses. Having considered the results, we believe that the current pilot studies justify further work on tools allowing for minimally invasive assessment of the human condition. The conducted experiments and the evaluation results revealed the need for the first contact staff to use the tool for a longer period of time (there is no access to specialists in eating disorders at school). There can be two potential useful usage scenarios.