## 3 Proposed Procedure to Develop Predictive Models in Healthcare
### Stage 2: Data Acquisition and Understanding
#### Clinical Data and Patient Data  
Firstly, two types of data related to patients can be identified when we distinguish between clinical data and other data related to the patient:  
* Patient characterization data. They are generally static data (although there may be small fluctuations in certain data values over large time intervals, such as in the case of the weight of the patient). They can be grouped in the following categories:Identification data: name, age, gender, educational level, nationality, etc. * Temporary data: dates of control or highlighted clinical evolution, visits to the hospital, start and end of treatments, etc. * Anthropometric data: measurements of the size and proportions of the human body, such as the height, weight, percentage of fat and muscle, foot length, abdominal perimeter, etc., that usually require instrumentation to be obtained (scale, tape measure, etc.). * Daily life activities (DLA) data: data usually reported by the patient related to the habits and activities that he/she usually performs on a daily basis. In some cases, some of these data can be measured using wearables sensors or other devices deployed in smart homes. * Clinical data: data of a medical nature that may require instrumentation and medical tests for their acquisition. **Data According to the Degree of Objectivity**  
Another possible classification is to categorize the data according to the degree of objectivity:  
* Measures: objective data that do not require assessment by a clinician. These data are not affected by the reproducibility factor. Examples are test or clinical scales, test results, or data collected by medical instrumentation. Data recorded by sensor devices provide objective data on some measurable dimensions of the patient and can be of different types: motion capture (MoCap) sensors, surface electromyography (EMG), stabilometric platforms, dynamometers, etc. * Assessed data: information that depends on the assessment of the clinician, such as diagnoses and treatments. * Reported data: subjective information provided by the patient regarding his/her condition (perceived symptoms). **Data According to Clinical Considerations**  
We also present a classification that groups the collected data according to clinical considerations:  
* Clinical profile: data about symptoms and clinical signs of the patient that can lead to a diagnosis by the clinician. We refer to symptoms when they are of subjective nature, reported by the patient, and to signs if they are objective and obtained by the clinician about the pathology. In addition, the signs can be qualitative (binary) or (discrete or continuous) quantitative (e.g., the temperature of a thermometer, image tests, other measurements, etc.). * Treatment data: data about the treatment that the clinician has prescribed, such as the type of treatment, number of rehabilitation sessions, drugs received, surgery, etc. * Clinical scales, tests, or surveys: data resulting from scales or validated and photocolized tests whose objective is to obtain objective information about the patient (e.g., the timed up and go test, the Unterberger test, psychological tests, etc.). * Medical history: data concerning the patient's clinical history, ordered chronologically (e.g., first hospital visit, imaging test for diagnosis, treatment administration after diagnosis, etc.). **Data According to their Data Types**  
Finally, the different data variables can be grouped according to their types, independently of the specifics of the health area. For example, we could consider:  
* Qualitative variables: also called categorical variables, they are variables that are not numerical. They describe data that fit into categories (e.g., educational level, the level of development of a disease, the level of invasiveness of a treatment, etc.). * Quantitative variables: also called measurement or numerical variables, they represent quantities of different nature.