Dataset Information

Scanning the horizon: a systematic literature review of methodologies.

ABSTRACT:

Objectives

Society is confronted with the rapid emergence of innovation in science and technology. To manage this, horizon scanning is being adopted globally to identify, assess and prioritise innovations and trends at an early stage of their development. This enables decision-makers to be better informed and to prepare for change. The aim of this paper is to systematically identify and evaluate horizon scanning methodologies employed in the healthcare and biomedical fields.

Methods

A systematic literature review was performed using PubMed and Embase and was supplemented with grey literature searches (2008-2018). The principal methodologies used in horizon scanning were extracted.

Results

Approximately 100 articles were summarised in a literature map. The search revealed many examples of horizon scanning across disciplines. Challenges, such as the need to refine prioritisation criteria, manage uncertainty inherent in the findings and improve the dissemination of identified issues, have been highlighted.

Conclusion

Horizon scanning, when performed appropriately, is a flexible and potentially reliable tool, with a wide variety of methods. Horizon scanning can inform and influence decision-making, through identifying opportunities and challenges, from an organisational to an international level. Further research to identify the most effective methodologies available would add depth to this landscape and enable the evolution of best practice to most efficiently anticipate novel developments and innovations.

SUBMITTER: Hines P

PROVIDER: S-EPMC6549743 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Publications

Scanning the horizon: a systematic literature review of methodologies.

Hines Philip P Hiu Yu Li L Guy Richard H RH Brand Angela A Papaluca-Amati Marisa M

BMJ open 20190527 5

<h4>Objectives</h4>Society is confronted with the rapid emergence of innovation in science and technology. To manage this, horizon scanning is being adopted globally to identify, assess and prioritise innovations and trends at an early stage of their development. This enables decision-makers to be better informed and to prepare for change. The aim of this paper is to systematically identify and evaluate horizon scanning methodologies employed in the healthcare and biomedical fields.<h4>Methods</ ...[more]

PMID: 31133588

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Project description:BackgroundThe use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results.ObjectiveThis study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used.MethodsA systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations.ResultsThe 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention.ConclusionsThe sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs.

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Dataset Information

Scanning the horizon: a systematic literature review of methodologies.

Objectives

Methods

Results

Conclusion

Publications

Scanning the horizon: a systematic literature review of methodologies.

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