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A clustering approach for topic filtering within systematic literature reviews.

ABSTRACT: Within a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatically convert large article corpora into a distribution of focal topics. This allows efficient filtering as well as objectifying the process through the discussion of the clustering results. Beyond that, it allows to quickly identify scientific communities and therefore provides an iterative perspective for the so far linear SLR methodology.•NLP and k-means clustering to filter large article corpora during systematic literature reviews.•Automated clustering allows filtering very efficiently as well as effectively compared to manual selection.•Presentation and discussion of the clustering results helps to objectify the nontransparent filtering step in systematic literature reviews.

SUBMITTER: Weißer T

PROVIDER: S-EPMC7078380 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A clustering approach for topic filtering within systematic literature reviews.

Weißer Tim T Saßmannshausen Till T Ohrndorf Dennis D Burggräf Peter P Wagner Johannes J

MethodsX 20200222

Within a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatic ...[more]

PMID: 32195145

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Project description:BACKGROUND:Evidence supports the benefits of exercise for patients with cancer; however, specific guidance for clinical decision making regarding exercise timing, frequency, duration, and intensity is lacking. Efforts are needed to optimize clinical recommendations for exercise in the cancer population. OBJECTIVES:To aggregate information regarding the benefit of exercise through a systematic review of existing systematic reviews in the cancer exercise literature. DATA SOURCES:PubMed, CINAHL Plus, Scopus, Web of Science, and EMBASE. STUDY ELIGIBILITY CRITERIA:Systematic reviews and meta-analyses of the impact of movement-based exercise on the adult cancer population. METHODS:Two author teams reviewed 302 abstracts for inclusion with 93 selected for full-text review. A total of 53 studies were analyzed. A Measurement Tool to Assess Systematic Reviews (AMSTAR) was used as a quality measure of the reviews. Information was extracted using the PICO format (ie, participants, intervention, comparison, outcomes). Descriptive findings are reported. RESULTS:Mean AMSTAR score = 7.66/11 (±2.04) suggests moderate quality of the systematic reviews. Exercise is beneficial before, during, and after cancer treatment, across all cancer types, and for a variety of cancer-related impairments. Moderate-to-vigorous exercise is the best level of exercise intensity to improve physical function and mitigate cancer-related impairments. Therapeutic exercises are beneficial to manage treatment side effects, may enhance tolerance to cancer treatments, and improve functional outcomes. Supervised exercise yielded superior benefits versus unsupervised. Serious adverse events were not common. LIMITATIONS:Movement-based exercise intervention outcomes are reported. No analysis of pooled effects was calculated across reviews due to significant heterogeneity within the systematic reviews. Findings do not consider exercise in advanced cancers or pediatric populations. CONCLUSIONS:Exercise promotes significant improvements in clinical, functional, and in some populations, survival outcomes and can be recommended regardless of the type of cancer. Although generally safe, patients should be screened and appropriate precautions taken. Efforts to strengthen uniformity in clinical trial reporting, develop clinical practice guidelines, and integrate exercise and rehabilitation services into the cancer delivery system are needed.

Project description:BackgroundThe recent, rapid development of digital technologies offers new possibilities for more efficient implementation of electronic health record (EHR) and personal health record (PHR) systems. A growing volume of healthcare data has been the hallmark of this digital transformation. The large healthcare datasets' complexity and their dynamic nature pose various challenges related to processing, analysis, storage, security, privacy, data exchange, and usability.Materials and methodsWe performed a systematic review of systematic reviews to assess technological progress in EHR and PHR systems. We searched MEDLINE, Cochrane, Web of Science, and Scopus for systematic literature reviews on technological advancements that support EHR and PHR systems published between January 1, 2010, and October 06, 2020.ResultsThe searches resulted in a total of 2,448 hits. Of these, we finally selected 23 systematic reviews. Most of the included papers dealt with information extraction tools and natural language processing technology (n = 10), followed by studies that assessed the use of blockchain technology in healthcare (n = 8). Other areas of digital technology research included EHR and PHR systems in austere settings (n = 1), de-identification methods (n = 1), visualization techniques (n = 1), communication tools within EHR and PHR systems (n = 1), and methodologies for defining Clinical Information Models that promoted EHRs and PHRs interoperability (n = 1).ConclusionsTechnological advancements can improve the efficiency in the implementation of EHR and PHR systems in numerous ways. Natural language processing techniques, either rule-based, machine-learning, or deep learning-based, can extract information from clinical narratives and other unstructured data locked in EHRs and PHRs, allowing secondary research (i.e., phenotyping). Moreover, EHRs and PHRs are expected to be the primary beneficiaries of the blockchain technology implementation on Health Information Systems. Governance regulations, lack of trust, poor scalability, security, privacy, low performance, and high cost remain the most critical challenges for implementing these technologies.

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A clustering approach for topic filtering within systematic literature reviews.

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A clustering approach for topic filtering within systematic literature reviews.

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