## 3 Methodology
### Experimentation
#### 3.2.1 Datasets Background and Information  
The datasets chosen for this analysis and experimental study of metrics were specific to the medical sphere. The selection criteria to choose the datasets are listed here:  
* Should be a medical/healthcare domain dataset;
* Datasets should be open source;
* Should have sufficient events, activities and samples to generate sizeable process maps.  
We searched for these datasets using Google Search and Kaggle datasets. Seven datasets fit our criteria and application. Their statistics are summarised in Table 1.  
1. **Remote Patient Monitoring Data--Almazov Institute**: This dataset was provided by PMT online (an online patient healthcare monitoring system). It contains events triggered by in-home blood pressure measurements made by patients suffering from arterial hypertension [90];
2. **Hospital Records of Dutch Hospital--Eindhoven University**: This dataset was published by Eindhoven University of Technology. The dataset consists of real-life event logs of a Dutch academic hospital [91];
3. **Hospital Billing Log--Eindhoven University**: This event log was sourced from Eindhoven University of Technology. The event log was obtained from the financial modules of the ERP system of a regional hospital [92];
4. **Nurse Work Flow--Almazov Institute**: This event log was provided by Almazov National Research centre. It consists of data from the hospital access control system concerning staff activities, laboratory procedures, branch communications. etc. [93];
5. **Data Driven Process Discovery (An Artificial Event log)--Eindhoven University**: This dataset was generated by Eindhoven University. It is a synthetic event log that simulates an artificial process log of a hospital (we have used the dataset with 0% noise) [94];
6. **Central Venous Catheter Process--Conformance checking challenge 2019**: This is a dataset produced by Eindhoven University. It is also the dataset used for a Conformance checking challenge in 2019. The dataset describes the procedure to perform central venous catheter with ultrasound [95];7. **Sepsis Treatment Pathway Dataset--Eindhoven University**: This dataset was sourced from Eindhoven University. It is a real-life event log consisting of events of sepsis cases from a hospital and its treatment [96].  
Upon acquiring these datasets, we explored the data using Python3 and the PM4 PY and ProFIT toolkits. The discovery of data was performed in two stages:  
* **Stage 1**: This stage involved sparsely overseeing the various attributes of the process model and filtering fields that may not be useful to our application (e.g., Dataset 2 had 128 fields of information, such as \"special code\", \"diagnosis index\", etc.). These additional fields were excluded and the basic three were retained in all datasets;
* **Stage 2**: In this stage, process models were visualised using Python3 and ProFIT. The Activity field in all the datasets was cleaned and made uniform with respect to all event logs. The filtering and cleaning were performed using a self-built Python script which removed all unnecessary elements in the event data (e.g., ',',',',',', etc.).  
The two-stage filtered data were then used for all further experiments.