# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications
### Clinical Decision Support Systems
In the context of the patient's infectious illness and comorbidities, the authors in [70] proposed an approach to the problem of inflammatory syndrome identification. It consists of implementing a computerized early warning tool for sepsis in which they researched to determine clinical metric performance of a cloud-based computerized sepsis clinical decision support system (CDSS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. The study was conducted in several hospitals in different regions of the United States. According to the authors the cloud-based sepsis CDSS integrated with enterprise EHR systems is an approach toward early recognition of sepsis in a hospital setting. [PERSON] et al. undertook a retrospective study over 25 years of use of CDSS since 1990 [71], which was designed to provide clinicians, staff, and patients with knowledge and person-specific information. Those system filters filter information and present it at convenient times, to enhance health and health care. The authors subscribe to the belief that, as a component of a Learning Health System CDSS is essential where a cycle of information is created from data generation, to aggregation, analysis, knowledge creation, knowledge dissemination and use, and ongoing measurement for continuous feedback and refinement. Over time, evolution of the techniques and approaches made it more and more efficient for practitioners and CDSS became fundamental. CDSS is evolving in important ways: increasing in availability of electronic data and knowledge bases for CDSS from industry to support traditional evidence-based medicine. Electronic health records provide data that are being aggregated in efficient ways to support the notion of 'practice-based' evidence. They help to support developing new forms of inference for CDSS. [PERSON] et al. reflected on the number of main dimensions of CDSS which adversely impact the effective implementation and use of CDSS in clinical practice, and certain developments which may accelerate its use. In summary, the authors suggested that the evolution and increased use of CDSS in practice is inevitable given the explosion of biomedical knowledge, and the pressure to improve quality and lower costs in value-based care. In [72], the interest in developing a CDSS for pediatric abdominal pain in an Emergency Department is related to an important demand for organizing the process. Emergencies are under heavy pressure on a regular basis. The authors were interested in abdominal pain with focus on appendicitisis of children. Diagnosis of appendicitisis in children is challenging. The CDSS was designed to help guide management of pediatric patients with acute abdominal pain within the Health Care Systems Research Network (HCSRN). The system uses, when available, prior diagnoses, medicines, laboratory, and other clinical data. At intervention sites, \"risk for appendicitisis\" is defined and assigned in exact terms. The system provides targeted recommendations regarding management and a summary that can be pasted into the clinical note. At this stage, the system is ongoing at two large health care systems in the HCSRN which each have histories of successfully developing, implementing, and evaluating CDSS systems. Experience from HCSRN sites was of great importance and helped the study team. Using the collaborative approach to problem solving may have been difficult to achieve with organizations outside of the network. The challenge remains in finding the balance between achieving target enrolment and enrolling the appropriate target population. Fuzzy sets can be defined from statistical information and rules for constructing CDSSs are, in this case, based on fuzzy logic. In [73], an approach is proposed to make use of statistical information describing dependence between input variables and output classes. The approach is based on possibility theory. The goal was to obtain fuzzy partitions particularly useful for classification purposes and suitable for medical applications.