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Trends and Focus of Machine Learning Applications for Health Research.

ABSTRACT: Importance:The use of machine learning applications related to health is rapidly increasing and may have the potential to profoundly affect the field of health care. Objective:To analyze submissions to a popular machine learning for health venue to assess the current state of research, including areas of methodologic and clinical focus, limitations, and underexplored areas. Design, Setting, and Participants:In this data-driven qualitative analysis, 166 accepted manuscript submissions to the Third Annual Machine Learning for Health workshop at the 32nd Conference on Neural Information Processing Systems on December 8, 2018, were analyzed to understand research focus, progress, and trends. Experts reviewed each submission against a rubric to identify key data points, statistical modeling and analysis of submitting authors was performed, and research topics were quantitatively modeled. Finally, an iterative discussion of topics common in submissions and invited speakers at the workshop was held to identify key trends. Main Outcomes and Measures:Frequency and statistical measures of methods, topics, goals, and author attributes were derived from an expert review of submissions guided by a rubric. Results:Of the 166 accepted submissions, 58 (34.9%) had clinician involvement and 83 submissions (50.0%) that focused on clinical practice included clinical collaborators. A total of 97 data sets (58.4%) used in submissions were publicly available or required a standard registration process. Clinical practice was the most common application area (70 manuscripts [42.2%]), with brain and mental health (25 [15.1%]), oncology (21 [12.7%]), and cardiovascular (19 [11.4%]) being the most common specialties. Conclusions and Relevance:Trends in machine learning for health research indicate the importance of well-annotated, easily accessed data and the benefit from greater clinician involvement in the development of translational applications.

SUBMITTER: Beaulieu-Jones B

PROVIDER: S-EPMC6822089 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Trends and Focus of Machine Learning Applications for Health Research.

Beaulieu-Jones Brett B Finlayson Samuel G SG Chivers Corey C Chen Irene I McDermott Matthew M Kandola Jaz J Dalca Adrian V AV Beam Andrew A Fiterau Madalina M Naumann Tristan T

JAMA network open 20191002 10

<h4>Importance</h4>The use of machine learning applications related to health is rapidly increasing and may have the potential to profoundly affect the field of health care.<h4>Objective</h4>To analyze submissions to a popular machine learning for health venue to assess the current state of research, including areas of methodologic and clinical focus, limitations, and underexplored areas.<h4>Design, setting, and participants</h4>In this data-driven qualitative analysis, 166 accepted manuscript s ...[more]

PMID: 31651969

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Project description:BackgroundDespite advancements in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment.ObjectiveThe aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow.MethodsWe performed an extensive mapping review to capture all relevant articles published within the last 10 years in the major human factors journals and conference proceedings listed in the "Human Factors and Ergonomics" category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in health care. Studies are discussed based on the key principles such as evaluating workload, usability, trust in technology, perception, and user-centered design.ResultsForty-eight articles were included in the final review. Most of the studies emphasized user perception, the usability of AI-based devices or technologies, cognitive workload, and user's trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure.ConclusionsHuman factors researchers should actively be part of efforts in AI design and implementation, as well as dynamic assessments of AI systems' effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.

Project description:ObjectivesThe study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining.MethodsThe data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts.ResultsUp to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years.ConclusionsThe study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future.

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Trends and Focus of Machine Learning Applications for Health Research.

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Trends and Focus of Machine Learning Applications for Health Research.

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