# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications
### Clinical Decision Support Systems
2. What cognitive strategies do ID experts use to deal with complexity? Several medical experts were interviewed for the study. A structured documentation process of coding within a systematic examination of individual interviews were included in the procedure, in which the ID experts have a long experience of on average 18.5 years. Out of 10 ID experts, two were female and eight were male. Identification of complexity factors in the procedure needed use of cognitive strategies which can be of help to the ID experts. It was shown from the study that a way to rework the paradigm of evidence-based medicine exists and can enhance the management of complex clinical tasks. It was typically assumed from the practice guidelines of the reviewers that leeway for clinical judgement is allowed as the experienced clinician is assessing the patient. However, clinicians are prompt to accept the rule-based recommendations as the guidelines are incorporated into the clinical decision support systems. ID experts deal with decision complexity using different mechanisms. Some cognitive factors contribute to this decision complexity, in which we can cite:  
1. overall clinical picture does not match the pattern,
2. lack of comprehension of the situation, and
3. social and emotional pressures. There are multiple facets and potentials of sentiments analysis; the authors of [77] managed to describe them in the context of medicine and healthcare for several reasons. Various persons are involved in a treatment process; we can find physicians of different specialties, therapists, and nurses. The importance of identifying personal observations and attitudes of a physician is highly acknowledged as they play a role when appearing in medical records. They permit having a complete view of a patient's health status, and other healthcare professionals will benefit from it. Observations or experiences in contrast to examinations results, are communicated in an unstructured way in reports and documents. Examination results on the other hand are often reported in a structured manner. That is why it is important to pay attention when extracting opinions from observations or examination results. They are very helpful in assessing clinical data and monitoring health status of patients. Automated decision support is made easier for practitioners. The medical conditions of a patient impact his life even though clinical documents are often written in an objective manner, for this reason sentiment analysis is of importance and helps to determine the impact from those written documents. Sentiment lexicons are usual in approaches that deal with sentiment analysis [78; 79; 80]. The authors of [77] provide an important basis for recognizing sentiment terms and patterns of sentiment expressions in natural language texts. Existing approaches to sentiment analysis from medical web data are either machine-learning based or rule-based. The authors worked on technical topics in which they expected a similarity to clinical narratives. The aim of their analysis is to examine the language and sentiment expressions in clinical narratives and medical social media. They used thousands of entries from different data sources to perform the experiment. Within a quantitative comparison, they analyzed and compared the word usage of the six text types: (1) nurse letter; (2) discharge summaries; (3) drug reviews; (4) Medblogs; and (5) Slashdot interviews. This analysis could be used for many purposes. Uncertainty, attitudes, and implicit sentiment could be collected and considered in clinical decision-making. Solutions for sentiment analysis lay in the construction of a domain-specific sentiment lexicon and the development of methods that allow sentiment to be judged depending on the context. The IBM Watson [81] is a CDSS that includes both machine learning and natural language processing modules, and has made promising progress in oncology. In a cancer, 99% of the treatment recommendations made from [PERSON] are like the physician decisions.