Skip to main content

Table 1 Previous review studies utilizing various methodologies in the clinical domain

From: An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain

Existing studies Literature methodologies The outcome of the analysis
Brnabic and Hess (2021) [8] 34 articles, systematic review Found a wide assortment of approaches, methods, techniques, software and validation procedures utilized in using ML/DL strategies to illuminate patient-provider decision making
Robles Mendo et al. (2021) [27] 20 articles, (PRISMA framework, systematic review Reviewed commercial applications found in the best-known commercial platforms
Salazar-Reyna et al. (2020) [42] 576 articles, systematic review Assessed and synthesized the published literature related to applying data analytics, big data, data mining, and ML to healthcare engineering systems
Verma et al. (2021) [47] 15 articles, systematic review Utilized ML/DL strategies at various phases of exploiting datasets consisting of patient-detailed outcome measures for anticipating clinical outcomes, introducing the promising study and demonstrating the utility of patient-reported outcome measures data for developmental research, and personalized treatment and precision medicine with the help of ML-based decision-support systems