# An Ontological Approach for Early Detection of Suspected COVID-19 among COPD Patients
## 3 Architecture for COVID-19 Suspected Detection in the Context of Monitoring COPD Patients
### Design Ontology for Symptoms COVID-19 Detection in COPD Patients
Based on [PERSON]'s [26] approach, the following four steps should be realized: (1) identify the terms, (2) identify their relations, (3) identify attributes, and (4) create instances of the ontology. The steps used in designing our five ontologies are given below. These ontologies use detection rules described in Table 4 to identify suspected COPD patients. \begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & **Rules-Based on Ontology Web Language Description Logics (OWL/DLS/WRL)** \\ \hline \multirow{4}{*}{1. Evaluation vital sign (Rule1)} & Patient(?P)’SensorDataBucTp(BT)’hasSDBP(?P,?BT)’SensorDataFev1(?Fv)’hasSDSP(?P,?Fv) \\  & \(\hat{\text{'SensorDataSpo2(?Sp)}}\)’hasSDSP(?P,?Sp)’hasCurrentValue(?Sp,?AnkRangeMin(?Sp,?Min)\) \\  & swrlrb:lessThanOrEqual(?cv, SPmin)’swrlrb:greaterThanOrEqual(?BT,BTmax) -> has\_TriggeredQ(?QE, Posif), \\  & SendMessage(?He) \\ \hline \multirow{4}{*}{2. Identification suspected (Rule2)} & Patient(?p)’Fever(?fv)’hasSympton\_Fever(?pt,?Fv)’Cough(?cg)’hasSympton\_Cough (? pt,?cg)’headaches (? hd)’hasheadaches(?pt,?hd)’Extreme, tiredness(?et)’hasSympton\_Extreme, tiredness(?pt,et)’Pneumonia (?pn)’hasympton\_Pneumonia(?pt,?pm)’SensorDataBucTemp(?bt)’hasS- \\  & Dfp(?pt,?bt)’SensorDataFev1(?fv)’hasSDF(?bt,?tv)’wasCaptualSpo(?psp)’hasSDSp(?t,?sp)’hasCurrent \\  & Value(?sp,?cv)’hasRangeMin(?Sp,?Min)’swrlrb: lessThanOrEqual(?cv,spmin)’swrlrb:greaterThanOrEqual(?cv,spmin) \\  & swrlrb:greaterThanOrEqual(?bt,Btmax) -> SuspectedCOVID-19 Alert (?pt, Suspected) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection of suspected patient’s rules. \begin{table}
\begin{tabular}{l l} \hline \hline
**Step** & **Description and Purpose** \\ \hline
1 & Determine the domain and scope of the ontology, COPD domain, and alert management. \\ \hline
2 & Ontology reuse and addressing poor ontological coverage of pulmonary diseases. \\ \hline
3 & Development of a conceptual model. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The steps in [PERSON]’s proposal. Table 4 describes the rules that enable the detection of suspected patients. Rule 1 means, for any patient instances and all instances of vital parameters, if the percentage rate in the blood is smaller than a minimum (80%), with a temperature greater than a maximum (38.5 \({}^{\circ}\)C), then, an alert is sent to the health care staff. Evaluation Vital Sign Ontology  
We identify the key terms of our domain, such as sensors, sensor data buccal temperature, FEV1, SPo2, medical profile, and patients. Next, we identify the relations between these entities (terms), such as isPartOf, collectsData, hasSDF, hasId, and isa. Finally, attributes are identified and linked to entities, such as hasPhone, hasFrame, and hasLname. Details of these elements are given in Figure 8.  
Figure 8: Evaluation vital sign ontology. Figure 7: SuspectedCOPDcoviDology overview. The concepts of this ontology are proposed according to the criteria of the first symptoms of COVID-19. SensorDataFEV1, SensorDataSpo2, and SensorDataBuccaTp are concepts in the ontology approach, but their instances represent data collected from sensors. The following are examples of such instances: SensorDataFEV1 = 88%, SensorDataBuccaTp = 38.8 \({}^{\circ}\)C, and SensorDataSpo2 = 80%. If a patient's vital signs range matches with the COVID-19 vital signs rule, then, the alert vital sign and the COVID-19 questionnaire are activated. Furthermore, a message is sent to healthcare staff (physicians, nurses, etc.) for information. Figure 8 describes the vital sign assessment based on Rule 1. Engine reasoning uses Rule 1 to trigger questionnaire forms if the evaluation of a vital sign parameter is positive. Table 5 presents some concepts and properties of the vital sign assessment ontology. The questionnaires are used to assess the symptoms of COVID-19 for patients with a positive vital signs result. The information is extracted from Google Forms by program, and then transmitted to populate the ontology.