It's Time to View Severe Medically Unexplained Symptoms as Red-Flag Symptoms of Depression and Anxiety

Understanding Patient-Provider Interaction, Treatment Acceptance, and Outcomes in Medically Unexplained Symptoms

INTRODUCTION

Medically unexplained symptoms (MUS) is an umbrella term used for chronic and often disabling health symptoms and conditions that remain unexplained after standard medical examinations, testing, and/or appropriate workup. Patients with MUS tend to receive little to no treatment but remain distressed, stigmatized, and disabled by symptoms and iatrogenic factors.

METHODS

A qualitative phenomenological study was conducted to explore daily challenges and psychosocial and iatrogenic factors affecting the management of MUS.

RESULTS

The analysis of the interviews revealed that MUS could cause significant distress to patients, impairing their functioning and leading to permanent disability. Conventional healthcare cannot meet the medical needs of these patients and might be a potential source of harm to them. It should be noted that confirmation of conditions associated with clinically significant psychiatric premorbidity was not provided.

CONCLUSION

Inconsistent diagnostic criteria, lack of proper training and research, diagnostic overshadowing, and implicit bias in healthcare professionals can lead to negative patient outcomes and the overuse of alternative or non-evidence-based services. Guidance, practice-based improvement ideas, and suggestions specific to improving patient-provider relationships can be applied to generate positive health effects.

An investigation into the deep learning approach in sentimental analysis using graph-based theories

Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a "black-box" and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features' extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on the input unit and backward path respectively. We experiment on the Twitter Health News dataset were the model has been extended to approach different approximations (tweet/aspect and tweets' source levels, frequency, polarity/subjectivity), it was also transparent and traceable. Moreover, results of comparing with four recent models on same data corpus for tweets analysis showed a rapid convergence with an overall accuracy of ≈83% and 94% of correctly identified true positive sentiments. Therefore, weights can be ideally assigned to specific active features by following the proposed method. As opposite to other compared works, the inferred features are conditioned through the users' preferences (i.e., frequency degree) and via the activation's derivatives (i.e., reject feature if not scored). Future direction will address the inductive aspect of graph embeddings to include dynamic graph structures and expand the model resiliency by considering other datasets like SemEval task7, covid-19 tweets, etc.