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

Abstract

Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

KNOWLEDGE MANAGEMENT IN VIRTUAL COMMUNITY OF PRACTICE (VCoP)

“…Penelitian ini menggunakan Systematic Literature Review (SLR) berdasarkan metodologi PICOC untuk melakukan tinjauan pustaka. Metode PICOC disarankan untuk mendapatkan fokus yang tepat (Petticrew & Roberts, 2006;Sabharwal & Miah, 2022). Langkah-langkah melakukan tinjauan literatur adalah sebagai berikut: 1.…”

Implementation of Big Data Analysis in Banking Management Reform, Action Aid and Risk Control

“…In the last, engineers use the model and data to get the result. Based on different functions, big data models can be mainly classified into three types, describing model, forecasting model and prescriptive model [9]. The describing model will transfer complex and mass data into acceptable information [9].…”

“…Based on different functions, big data models can be mainly classified into three types, describing model, forecasting model and prescriptive model [9]. The describing model will transfer complex and mass data into acceptable information [9]. Forecasting model will use Machine Learning technology or simulation to predict the trend.…”

Research-Problem Validity in Primary Research: Precision and Transparency in Characterizing Past Knowledge

“…Finally, recent calls to make distinct aspects of the scientific process machine-readable (Lakens & DeBruine, 2021;Spadaro et al, 2022) could enhance transparency, standardize the literature-review process, and reduce time and effort expenditure (Brisebois et al, 2017;Sabharwal & Miah, 2022) Open-science practices to improve research-problem validity. A systematic literature review we conducted Open-science practices make transparency the default choice (Klein et al, 2018) for the steps in the scientific-research process, from sharing materials that inform study-design choices (Landy et al, 2020) and raw data (Simonsohn, 2013) to data analyses (Botvinik-Nezer et al, 2020;Schweinsberg et al, 2021).…”

“…Finally, recent calls to make distinct aspects of the scientific process machine-readable ( Lakens & DeBruine, 2021 ; Spadaro et al, 2022 ) could enhance transparency, standardize the literature-review process, and reduce time and effort expenditure ( Brisebois et al, 2017 ; Sabharwal & Miah, 2022 )…”