# Artificial Intelligence in Virtual Telemedicine Triage:
## 3 Materials and Methods
### Creation of the AI Algorithm
#### 3.5.2 Dataset Adaptation  
Initially, the data were recorded in MS Excel, with the symptoms present in medical diagnoses of COVID-19, the common cold, and allergic rhinitis in labeled and related columns. However, the reliability of vital signs for the diagnosis of disease has been questioned because they are not a specific diagnostic class and cannot be used as definitive criteria for determining the presence of a particular disease. In addition, there are cases of asymptomatic patients with normal vital signs who may have a disease, while other patients may have abnormal vital signs without presenting a serious disease. Therefore, the artificial intelligence model for the diagnosis of respiratory infections was built using other factors, such as the patient's symptoms, in the evaluation and diagnosis. The data measured by the device developed is used to monitor the patient's vital signs in real-time but is not used in this project as a training source for the artificial intelligence model. When processing the database, it should be considered that the lexicon used has to be understandable by an average person who does not have technical knowledge in the area of health. Therefore, the vocabulary used by health professionals was changed to a more understandable one as shown in Table 1.  
This change was made together with the medical personnel who collaborated in the research project since symptoms are the most important data when predicting what type of disease the user has so that they can seek quick attention. By prioritizing domain knowledge and integrating it with state-of-the-art AI techniques, we can enhance the accuracy and utility of our predictive models. After implementing the necessary changes to the vocabulary of the symptom descriptions to enhance user understanding, the subsequent step was to further refine the dataset \begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Medical Vocabulary** & **Common Vocabulary** \\ \hline Rhinorrhea & Nasal congestion \\ Odynophagia & Sore throat \\ Tiredness & General fatigue \\ Dizziness & Headache, sight \\ Rib pain & Chest pain \\ Anorexia & Loss of appetite \\ Throat dryness & Sore throat, strong \\ Epigastralgia & Belly pain \\ Arthalgia & Joint pain \\ Migraine & Headache, severe \\ Cough that does not move secretions & Dry cough \\ Cough that moves secretions & Cough with phlegm \\ Hyaline rhinorrhea & Nasal congestion \\ Heat spike & Fever \\ Unquantified thermal rise & Fever \\ Cephalalgia & Headache \\ Dryness of mucous membrane of throat & Sore throat, strong \\ Asthenia & General fatigue \\ Headache Frontal headache & Headache, moderate \\ Rhinopharyngealgitis & Cold \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Changes from technical medical vocabulary to common vocabulary. to facilitate better interpretability by the artificial intelligence algorithm. This involved separating each symptom into distinct columns to avoid confusion or ambiguity when feeding the data to the algorithm. Furthermore, to enhance the usefulness of the dataset, a patient ID column was included to identify the number of positive cases of COVID-19, the common cold, and allergic rhinitis recorded in the database. The dataset comprised a total of 24 symptom columns, one column for the diagnosis provided by the physicians, a column for the patient's gender, and a unique patient ID to enable efficient data tracking and management. During the dataset manipulation process, careful consideration was given to determining the appropriate categories for each column. For most of the symptoms, the presence of the symptomatology was categorized as \"yes\", while its absence was indicated with the word \"no\". However, for three columns, namely, \"pain\", \"fatigue\", and \"fever\", the categories \"mild\", \"moderate\", and \"strong\" were used to provide a more nuanced understanding of symptom severity. In the case of the \"cough\" column, the categories \"dry\" and \"with phlegm\" were employed to capture the distinct types of cough that may manifest in patients.