# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 2 Methodology  
To perform the literature review, we relied in great part on the PRISMA Statement and flowchart [3]. Our main source was Scopus because it is the largest abstract and indexing database in the world (according to Elsevier) and thus includes a very expanded spectrum of journals. A detailed search of publication of papers about big data in healthcare between 2016 and 2020 was used for review. The review included only English language publications with the search strategy in Table 1. Our strategy of research is composed of seven steps: (1) decompose each subject into concepts; (2) enrich its vocabulary that means for each of the concepts identified, the vocabulary is enriched by listing synonyms or equivalent terms; (3) understand the logical links (AND, OR, NOT) between words, in order to place them correctly; (4) check the particularities of the search tool to be used, if quotes are permitted, for example; (5) formulate the first research request; (6) analyze the results; (7) improve the query. We used the keywords that directly describe the disciplines we were interested in. We chose the appropriate combination operators. Asterisks at the end of the word 'a*' search for multiple variants of 'a' in the database. The code [tiab] restricts the query to search in the title and the abstract of the paper, sometimes using acronyms for specific algorithms, e.g., Support Vector Machine (SVM), K-nearest neighbor (KNN).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Filtering** & **Search Query** \\ \hline \multirow{3}{*}{Data mining} & (”machine learning”[tiab] OR “data mining”[tiab] OR “text mining”[tiab] OR “mining”[tiab] OR “decision”[tiab] OR “decision”[tiab] OR “decision”[tiab] OR “KNN”[tiab] OR “K-means”[tiab] AND (med*[tiab] OR clinic”[tiab] OR health*[tiab]) \\ \hline \multirow{3}{*}{Ontologies and semantic reasoning} & Ontology AND (med*[tiab] OR clinic”[tiab] OR health*[tiab] OR emergence”[tiab] OR”[tiab] OR psychology”[tiab] OR pain [tiab] OR therapy [tiab] OR treat*[tiab] OR “semantic”[tiab] OR disease”[tiab] OR category”[tiab] OR “(computerized clinical guideline”[tiab] OR computerized clinical practice guideline”[tiab]) \\ \hline \multirow{3}{*}{Clinical decision support systems} & (monitor*[tiab] OR assist*[tiab] OR recommend*[tiab] OR “decision support”[tiab] OR “decision making”[tiab] OR “computer assisted”[tiab] OR “reminder system”[tiab] OR “support system”[tiab] OR expert system”[tiab] AND (med*[tiab] OR clinic”[tiab] OR”[tiab] OR”health*[tiab] OR emergence*[tiab] OR physician*[tiab] OR psychology”[tiab] OR pain [tiab] OR therapy [tiab] OR treat*[tiab] OR drug*[tiab] OR disease*[tiab] OR diagnosis*[tiab] OR emotion*[tiab]) \\ \hline \multirow{3}{*}{Smart homes} & (smart home*[tiab]) AND (activity recognition*[tiab] “expression recognition”[tiab] OR \\  & assist*[tiab] OR recogni*[tiab] OR detect*[tiab] OR guid*[tiab] OR algorithm*[tiab] OR \\  & “security”[tiab] OR web service*[tiab]OR “system*[tiab] OR “application”[tiab] OR \\  & “technology”[tiab] OR “engine”[tiab] OR “framework”[tiab] OR “behaviour analysis”[tiab]) \\ \hline Medical big data & “Big data”[tiab] AND (med*[tiab] OR clinic*[tiab] OR health*[tiab]) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search strategy.  
As shown in Table 2, our search strategy resulted in the following tally of articles as per five AIA applications. We report here the number of articles at each stage of our review. The criteria for selection at each stage are as follows:  
1. Found: articles matching our search strategy of AIA applications.
2. Relevant: articles relevant as it presents one of the AIA applications we focused on.
3. Cited: article cited in our review as it made significant advances complementary to others.  
The first column of Table 2 (found) refers to the papers found in Scopus that respond to the key words used in the request search. As the number of articles found is huge, we cannot cite all of them, we cite the most relevant in terms of novelty in artificial intelligence in healthcare.