## 2 Related Work
### Clinical Decision Making  
The use of clinical decision support systems in the management of serious injuries in emergency departments is very important [17]. Management errors that occur due to time pressure, inexperience, dependence on memory, multi-tasking, information flow analysis, and failures due to lack of care team coordination, especially during first aid, can be greatly reduced by making use of the decision support system. In the context of clinical medicine, the study [18] mentioned machine learning clinical decision making, which deals with estimating outcomes based on past experiences and data patterns using a computer generated algorithm that combines technical intelligence through visual and auditory data to treat severely injured patients. Physiotherapists must manage a large amount of information in order to make therapeutic decisions, but they can enhance their practice by using information gleaned from technology, methodical data processing, and expertise [19]. Frontline staff members can choose the best interventions for patients with musculoskeletal injuries with the aid of clinical decision support tools. These resources are based on online surveys, therapy algorithms and models, clinical prediction criteria, and classification schemes. They are being developed, employ quickly advancing computer technology, and might be of interest to healthcare professionals. Utilizing a decision support system can assist in standardizing data collecting and presenting the steps necessary to apply operational metrics that can be applied across health care disciplines [20]. In [21], the authors evaluated the SIAVA-FIS system based on two modules: a web module where data are entered, configured, and printed and a second module in the form of a mobile device app where different types of graphics are used to present the assessment of patients with musculoskeletal disorders. Through this system, clinical parameters such as vital signs (blood pressure, temperature, heart rate, and respiratory rate), body mass index, goniometry, muscle strength, girth, muscle tone, and pain sensation were recorded. However, there is also research effort that describes the danger of using one-sided decision support systems for the management of musculoskeletal injuries. In [22], the authors evaluated the validity of a clinical decision support tool--the Work Assessment Triage Tool (WATT)--in relation to physicians' recommendations regarding the choice of treatment for workers with musculoskeletal conditions. Clinicians tended to recommend functional rehabilitation, physiotherapy, or no rehabilitation, while the WATT recommended additional evidence-based interventions, such as workplace interventions. On the other hand, a review study [23] attributed the diversity of existing decision-making systems for the management of musculoskeletal injuries to the complexity of their inherent diagnostic complexity. However, given the multidimensional nature of musculoskeletal injuries, the fuzzy logic that underpins these systems may assist in the design of knowledge bases for clinical decision support systems. A large proportion of these systems were designed for the diagnosis of inflammatory/infectious disorders of bones and joints, and knowledge was extracted by a combination of three methods (expert information acquisition, data analysis, and literature review). Many coaches and athletes are adopting an increasingly scientific approach to both the design and monitoring of training programs. The appropriate monitoring of training load (frequency, duration, and intensity) can help determine whether an athlete is adapting to a training program and minimize the risk of developing non-functional overuse, illness, and/or injury. In order to understand the function of training load and the effect of load on the athlete, Ref. [24] lists a number of indicators that are available for use via tracking devices (e.g., smart swatch). The main information obtained from these devices refers to the training load (frequency, duration, intensity, etc.