# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications
### Ontologies and Semantic Reasoning
An ontology-based herb therapy recommendation web for respiration system health was proposed by the authors. The aim was to sort out the knowledge and build an easy-access web system. The latter provides reliable and comprehensive knowledge of herbs to improve respiration system wellness. The feedback from users confirm the practicality and usefulness of the designed system. The use of herbs in daily diet and as alternative treatments is not new, but it still needs great promotion and education as many people have gone to hospitals to get quick and easy pills to feel better. Promoting this knowledge remains a long and great challenge. On the other hand [PERSON] et al. [36] were interested in the use of an ontology to detect drug abuse epidemiology. The authors improved an existing ontology-based system (PREDOSE for PREscription Drug abuse Online Surveillance and Epidemiology) in which the objective was to extract the relevant knowledge from websites and blogs on illicit drug usage. PREDOSE does not cover the brand names and slang terms of drugs, nor the risk factors related to the illicit drugs. It does not provide the information on drugs that are prohibited in different countries. The proposed system aims to compensate for any lack of PREDOSE by providing useful information related to drug addiction and its prohibited information. Thanks to this new approach, users can query the knowledge base for information such as street names or slang terms for drugs. Users can also obtain information on the different company names for a single chemical formulation of a drug, as well as their side effects. Some information is updated and expanded in the approach as the number of classes has increased from 43 to 114. In addition, the model gains efficiency by defining semantic rules. In [37] the authors used a Semantic-Based Knowledge Management System (SKMS) that supports knowledge acquisition and integrates various approaches to provide a systematic practical platform to knowledge practitioners. The aim of SKMS is to identify roles of healthcare professionals, duties that can be performed according to personnel competencies, and projects that are conducted as a part of tasks to complete defined goals of clinical processes. In this work, the authors proposed an ontology-based approach to investigate implicit knowledge acquisition and information flow issues. SKMS is useful to assist providing the right information to right users as needed in different tasks to carry out activities in processes of healthcare units. This framework supports tacit and explicit knowledge acquisition management. It provides a systematic way to acquire implicit knowledge from subject matter experts or knowledge mentors, model it formally, and transform it into a machine-readable form to develop a health information system (HIS) that provides a variety of health services that can better meet the needs of health care professionals and users. The goal of the ontological-based modelling method is inviting the organization to move towards patient-centered systems, enlarge opportunities, and simplify reuse of existing healthcare practices for new reform initiatives. Using an ontology gives new vision to manage knowledge and provides help to develop HIS with different and new implementation strategies. The work in [38] focused on the evaluation of the ability of crowdsourcing methods to detect errors in SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and to investigate the demanding challenges of upgradable ontology verification. The authors presented a methodology to crowdsource ontology verification that uses micro tasking combined with a Bayesian classifier. The results of the study showed that the crowd can indeed identify errors in SNOMED CT that experts also find, which suggests that the proposed method will likely perform well on similar ontologies. When experts are unavailable, added to limitation of budget, or width of the ontology which makes it difficult to evaluate it manually, we can use the crowd which can be particularly useful.