scholarly journals Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Anne A. H. de Hond ◽  
Artuur M. Leeuwenberg ◽  
Lotty Hooft ◽  
Ilse M. J. Kant ◽  
Steven W. J. Nijman ◽  
...  
Keyword(s):  
Software Development ◽  
Impact Assessment ◽  
Scoping Review ◽  
Prediction Models ◽  
Relevant Literature ◽  
Quality Criteria ◽  
Daily Practice ◽  
Complex Data ◽  
Data Preparation ◽  
Development Evaluation

AbstractWhile the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1–3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance.

Mental health and stigma in Saudi Arabia: a scoping review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nada Alattar ◽  
Anne Felton ◽  
Theodore Stickley
Keyword(s):  
Mental Health ◽  
Saudi Arabia ◽  
Scoping Review ◽  
Mental Health Problems ◽  
Cultural Beliefs ◽  
Relevant Literature ◽  
Mental Health Stigma ◽  
Health Issues ◽  
Content Type ◽  
Mental Health Issues

Purpose Stigma associated with mental health problems is widespread in the Kingdom of Saudi Arabia (KSA). Consequently, this may prevent many Saudi people from accessing the mental health-care services and support they need. The purpose of this study is to consider how stigma affects people needing to access mental health services in the KSA. To achieve this aim, this study reviews the knowledge base concerning stigma and mental health in KSA and considers specific further research necessary to increase the knowledge and understanding in this important area. Design/methodology/approach This review examines the relevant literature concerning mental health stigma and related issues in KSA using the Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses frameworks. As a scoping review, it has used a systematic approach in literature searching. The results of the search were then thematically analysed and the themes were then discussed in light of the concepts of stigma and mental health. Findings Stigma around mental health impedes access to care, the nature of care and current clinical practice in the KSA. The voices of those with mental health issues in KSA are almost entirely unrepresented in the literature. Originality/value The review identifies that mental health stigma and cultural beliefs about mental health in KSA may act as barriers to accessing services. The voice of mental health service users in KSA remains largely unheard. If public discussion of mental health issues can increase, people’s experiences of accessing services may be improved.

scholarly journals A Systematic Mapping Study on Privacy by Design in Software Engineering

2019 ◽  
Vol 22 (1) ◽  
Author(s):  
Miguel Ehecatl Morales-Trujillo ◽  
Gabriel Alberto García-Mireles ◽  
Erick Orlando Matla-Cruz ◽  
Mario Piattini
Keyword(s):  
Software Development ◽  
Data Privacy ◽  
Theoretical Perspective ◽  
Relevant Literature ◽  
Personal Data ◽  
Software Systems ◽  
Systematic Mapping Study ◽  
Privacy By Design ◽  
Mapping Study ◽  
Systematic Mapping

Protecting personal data in current software systems is a complex issue that requires legal regulations and constraints to manage personal data as well as a methodological support to develop software systems that would safeguard data privacy of their respective users. Privacy by Design (PbD) approach has been proposed to address this issue and has been applied to systems development in a variety of application domains. The aim of this work is to determine the presence of PbD and its extent in software development efforts. A systematic mapping study was conducted in order to identify relevant literature that collects PbD principles and goals in software development as well as methods and/or practices that support privacy aware software development. 53 selected papers address PbD mostly from a theoretical perspective with proposals validation based primarily on experiences or examples. The findings suggest that there is a need to develop privacy-aware methods to be integrated at all stages of software development life cycle and validate them in industrial settings.

scholarly journals Urological Manifestations of the Systemic Vasculitides—A Scoping Review

Uro ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 209-221
Author(s):  
Łukasz Nowak ◽  
Wojciech Krajewski ◽  
Joanna Krajewska ◽  
Joanna Chorbińska ◽  
Paweł Kiełb ◽  
...  
Keyword(s):  
Scoping Review ◽  
Web Of Science ◽  
Relevant Literature ◽  
Underlying Disease ◽  
Genitourinary System ◽  
Size Type ◽  
Appropriate Therapy ◽  
Proper Diagnosis ◽  
Almost All ◽  
Specific Symptoms

Background: Vasculitides are specific inflammations of the blood vessel wall that can take place in any organ system of the human body. They may occur as a primary process (primary systemic vasculitides, PSV) or may be secondary to another underlying disease. In general, in association with the specific type of vasculitis, affected vessels vary in size, type, and location. In the following scoping review, we present clinical characteristics and manifestations of PSV with reference to the genitourinary system. Materials and methods: A non-systematic search of the relevant literature was conducted using three electronic databases (PubMed, Embase, and Web of Science) up to 29 October 2021. Results: Urogenital manifestations of PSV are infrequent, with the most commonly reported findings as prostatic or testicular involvements. However, almost all other organs of the genitourinary system can be affected. Conclusions: Because of the clinical heterogeneity and non-specific symptoms, the proper diagnosis of PSV is often delayed and constricted. Fast identification of urological manifestations of vasculitides is essential in implementing appropriate therapy and avoiding unnecessary, harmful, and invasive surgery.

scholarly journals A Protocol of Scoping Review of Peak Point Prediction Methods for Epidemic Diseases: Applicable to Coronavirus 2019 Prediction

2021 ◽  
Vol 10 (1) ◽  
pp. 71
Author(s):  
Elham Nazari ◽  
Zahra Ebnehoseini ◽  
Hamed Tabesh
Keyword(s):  
Scoping Review ◽  
Quantitative Methods ◽  
Prediction Models ◽  
Data Extraction ◽  
Prediction Methods ◽  
Peak Time ◽  
Epidemic Disease ◽  
Qualitative And Quantitative Methods ◽  
Model Studies ◽  
Protocol Study

Introduction: Given, widespread COVID-19 across the world a comprehensive literature review can be used to forecast COVID-19 peak in the countries. The present protocol study aimed to explore epidemic peak prediction models in communicable diseases.Material and Methods: This protocol study was conducted based on Arksey and O'Malley's. This framework encompasses purpose and hypothesis, modeling, model achievements aspects. A systematic search of English in PubMed was conducted to identify relevant studies. In the pilot step, two reviewers independently extracted the variables from 10 eligible studies to develop a primary list of variables and a data extraction form. In the second step, all eligible studies were assessed by researchers. In the third step, two data extraction forms were combined. The data were extracted and categories were created based on frequency. Qualitative and quantitative methods were used to synthesize the extracted data.Results: The current study were focused on forecasting the epidemic peak time that is a worlds’ concern issue. The results of current scoping review on prediction methods for epidemic disease can provide foundational knowledge, and have important value for the prediction model studies of COVID-19.Conclusion: Our findings will help researchers by a summary of evidence to present new ideas and further research especially for studies were focused on COVID-19. Our results can improve the understanding of prediction methods for COVID-19.

scholarly journals Clinical prediction models for hospital falls: a scoping review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e051047
Author(s):  
Rex Parsons ◽  
Susanna M Cramb ◽  
Steven M McPhail
Keyword(s):  
Real Time ◽  
Scoping Review ◽  
Fall Risk ◽  
Prediction Models ◽  
Reporting Quality ◽  
Reference List ◽  
Fall Prediction ◽  
Scoping Reviews ◽  
Ethical Approval ◽  
Meta Analyses

IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysisThis scoping review will follow the Arksey and O’Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and disseminationEthical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.

The Use of Repetitive Transcranial Magnetic Stimulation for Treatment of Obsessive-Compulsive Disorder: A Scoping Review. (Preprint)

2021 ◽  
Author(s):  
Medard Adu ◽  
Ejemai Eboreime ◽  
Adegboyega Sapara ◽  
Andrew J. Greenshaw ◽  
Pierre Chue ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Obsessive Compulsive Disorder ◽  
Scoping Review ◽  
Magnetic Stimulation ◽  
Depressive Disorders ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Relevant Literature ◽  
Invasive Procedure ◽  
Obsessive Compulsive ◽  
Compulsive Disorder

BACKGROUND Background: Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive procedure in which brain neural activity is stimulated by direct application of a magnetic field to the scalp. rTMS is considered a therapeutic tool in various neuropsychiatric conditions. Since its approval in Canada in 2002 and despite its wide and continuous usage for the management of depressive disorders, knowledge on the use of rTMS for Obsessive-Compulsive Disorder (OCD) is sparse. OBJECTIVE Objectives: This scoping review seeks to; (i) explore the relevant literature available regarding the use of rTMS as a mode of treatment for OCD; (ii) To evaluate the evidence to support the use of rTMS as a treatment option for OCD. METHODS Method: We electronically conducted data search in five research databases (MEDLINE, CINAHL, Psych INFO, SCOPUS, and EMBASE) using all identified keywords and index terms across all the data bases to identify empirical studies and randomized controlled trials. We included articles published with randomized control designs which aimed at the treatment of OCD with rTMS. Only full-text published articles written in English were reviewed. Review articles on treatment for conditions other than OCD were excluded. RESULTS NA CONCLUSIONS Conclusion: The application of rTMS as a treatment intervention for OCD looks promising despite diversity in terms of outcomes and clinical significance. Further studies with well-defined stimulation parameters are needed in order to be able to draw a definite conclusion of its clinical effectiveness in the treatment of OCD.

Auto-pipeline

2021 ◽  
Vol 14 (11) ◽  
pp. 2563-2575
Author(s):  
Junwen Yang ◽  
Yeye He ◽  
Surajit Chaudhuri
Keyword(s):  
Reinforcement Learning ◽  
Recent Work ◽  
Pipeline System ◽  
Complex Data ◽  
Data Preparation ◽  
Significant Progress ◽  
Database Table ◽  
End To End

Recent work has made significant progress in helping users to automate single data preparation steps, such as string-transformations and table-manipulation operators (e.g., Join, GroupBy, Pivot, etc.). We in this work propose to automate multiple such steps end-to-end, by synthesizing complex data-pipelines with both string-transformations and table-manipulation operators. We propose a novel by-target paradigm that allows users to easily specify the desired pipeline, which is a significant departure from the traditional by-example paradigm. Using by-target, users would provide input tables (e.g., csv or json files), and point us to a "target table" (e.g., an existing database table or BI dashboard) to demonstrate how the output from the desired pipeline would schematically "look like". While the problem is seemingly under-specified, our unique insight is that implicit table constraints such as FDs and keys can be exploited to significantly constrain the space and make the problem tractable. We develop an AUTO-PIPELINE system that learns to synthesize pipelines using deep reinforcement-learning (DRL) and search. Experiments using a benchmark of 700 real pipelines crawled from GitHub and commercial vendors suggest that AUTO-PIPELINE can successfully synthesize around 70% of complex pipelines with up to 10 steps.

Integrating Quality Criteria and Methods of Evaluation for Software Models

2009 ◽  
pp. 2728-2743
Author(s):  
Anna E. Bobkowska
Keyword(s):  
Software Development ◽  
Quality Criteria ◽  
Integrated Approach ◽  
Specific Situation ◽  
Modeling Languages ◽  
Model Driven ◽  
Software Models ◽  
Methods Of Evaluation ◽  
Model Driven Software Development

Successful realization of the model-driven software development visions in practice requires high quality models. This chapter focuses on the quality of models themselves. It discusses context-free and context-dependent quality criteria for models and then moves on to methods of evaluation which facilitate checking whether a model is good enough. We use linguistic theories to understand groups of criteria and their impact on other models, software product and the process of software development. We propose a strict distinction of the impacts of visual modeling languages, models of the system and tools for quality criteria. This distinction is helpful when designing the methods of evaluation and making decision about the point in time, scope and personnel responsible for quality assessment. As the quality criteria and several methods of evaluation has usually been considered separately we propose a methodology which integrates them. Such an integrated approach provides the following benefits. It allows for designing methods of evaluation based on quality criteria and elements of the model (or modeling language) in the context of specific needs. It can be applied for management of the scope of evaluation with quality criteria as well as configuration of the method to a specific situation. It allows for flexible and efficient conduct of the evaluation with selection of the methods of evaluation. Finally, this chapter presents case studies which illustrate the approach.

Integrating Projects from Multiple Open Source Code Forges

2009 ◽  
pp. 2301-2312
Author(s):  
Megan Squire
Keyword(s):  
Software Development ◽  
Open Source ◽  
Relevant Literature ◽  
Source Code ◽  
Scoring Systems ◽  
Open Source Code ◽  
Multiple Code ◽  
Future Work

Much of the data about free, libre, and open source (FLOSS) software development comes from studies of code forges or code repositories used for managing projects. This paper presents a method for integrating data about open source projects by way of matching projects (entities) across multiple code forges. After a review of the relevant literature, a few of the methods are chosen and applied to the FLOSS domain, including a comparison of some simple scoring systems for pairwise project matches. Finally, the paper describes limitations of this approach and recommendations for future work.

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