data-driven decision-making

One of the constant problems that people with mental health conditions are faced with now is that they cannot establish a good relationship with their therapist, or the client's disease type is not in the therapist's specialty. These clients may not receive adequate treatment and stop the therapy before feeling well. Therefore, the classification of mental patients based on their disorder types and allocating a therapist with the same expertise to them could lead to better treatment and improve the quality of the therapy sessions. This paper will compare several machine learning (ML) algorithms to classify patients with mental conditions. Moreover, benefiting from the best ML algorithm, patients will be categorized into different classes based on their disorder types. Finally, a mathematical model will be developed to determine the allocation policy of therapists to each group of patients to maximize the summation of the utilization between therapists and patients. To explore the implementation of the proposed method, we have conducted a real-life case study to assess the validation of the model.

Since human societies have endured massive financial disruptions and life losses after the outbreak of the COVID-19 pandemic, it is critical to eliminate this disease as soon as possible. Today, the invention of the COVID-19 vaccine made this objective more reachable. But unfortunately, the suppliant of the vaccines is limited. Hence, to prevent further lethal harms, it seems rational to use a scientific method for vaccine allocation. This study proposes a method for prioritizing the patients based on their level of life-threatening danger according to the proven risk factors (e.g., age, sex, pregnancy, and underlying diseases) of the COVID-19. That is a new data-driven decision-making method for patients’ classification based on their health condition information using several machine learning algorithms. In this method, vaccine applicants are classified into four classes. The scheduling of vaccine distribution would be conducted based on the results of this classification. Furthermore, a real-life case study is also investigated through the proposed method for better illumination in this paper. The vaccine distribution schedule of the real-case study has been performed with 94% accuracy. It should be mentioned that the main achievement of this research is to design a new efficient method for a vaccine distribution schedule.