Mar 12, 2019 · Systematic Review Tools Critical Appraisal: JBI Critical Appraisal Tools: (for research designs and systematic review) AMSTAR Checklist CASP (Critical Appraisals Skills Programme) Checklists Cochrane Handbook for Systematic Reviews of Interventions (V. 5.1) Software: COLANDR: open access, machine-learning assisted tool for conducting evidence ...
May 27, 2020 · Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review Data scientists often develop machine learning models to solve a variety... 02/15/2021 ∙ by Giuliano Lorenzoni , et al. ∙ 0 ∙ share
Jun 12, 2020 · Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.
During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions.
Apr 14, 2021 · Limitations of the systematic review. Besides investigating the outcome of machine learning algorithms in injury prediction and prevention, this systematic review also focused on the methodology of AI/ML studies, which makes some parts probably challenging to read for sports medicine clinicians.
While the workshop focussed on systematic reviews, for a jobbing librarian like me in a clinical setting, searches to support systematic review will make up only a small part of the workload. Nevertheless, searches still need to be conducted soundly and rigorously. Can artificial intelligence and machine learning help?
Jan 15, 2019 · The use of text-mining tools and machine learning (ML) algorithms to aid systematic review is becoming an increasingly popular approach to reduce human burden and monetary resources required and to reduce the time taken to complete such reviews [3,4,5]. ML algorithms are primarily employed at the screening stage in the systematic review process.
utomation of phase recognition based on data inputs is essential for optimization of workflow, surgical training, intraoperative assistance, patient safety, and efficiency. Methods: A systematic review was performed according to the Cochrane recommendations and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Web of Science, IEEExplore, GoogleScholar ... Protocol for a systematic review of associations of bisphenol A exposure with declining semen quality in men to support derivation of a reference dose for mixture risk assessments for male reproductive health: Systematic Review : Energy poverty and health: Systematic Review : Machine Learning in Finance: Systematic Review
Machine learning for efficiency improvements in systematic literature reviews of clinical efficacy and safety. •Systematicliteraturereviews(SLRs)playanimportantroleinthedecision makingofhealthcareagencies1,2. •Timelyadviceonhealthtechnologiesinrapidlyevolvingtreatmentlandscapes isrequiredbydecisionmakers3. •However,therateofscientificpublicationcontinuestoincreasesubstantially.
DOI: 10.1016/j.infsof.2011.09.002 Corpus ID: 15036265. Systematic literature review of machine learning based software development effort estimation models @article{Wen2012SystematicLR, title={Systematic literature review of machine learning based software development effort estimation models}, author={Jianfeng Wen and Shixian Li and Zhiyong Lin and Yong Hu and Changqin Huang}, journal={Inf ...
May 04, 2021 · Background: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.
The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.
Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms.
Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm—namely, the convolutional extreme learning machine (CELM)—we present a systematic review that investigates alternative deep learning architectures that use ...

Nov 03, 2020 · A machine learning classifier had high recall for identifying studies using text word searches for three systematic reviews of chronic pain; precision was low to moderate. Use of the machine learning classifier resulted in a small to moderate estimated time savings when conducting update searches for living systematic reviews.

A paper entitled “A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models” has been published earlier this year (2019). It has been largely shared and commented on various online platforms.

DOI: 10.1016/j.infsof.2011.09.002 Corpus ID: 15036265. Systematic literature review of machine learning based software development effort estimation models @article{Wen2012SystematicLR, title={Systematic literature review of machine learning based software development effort estimation models}, author={Jianfeng Wen and Shixian Li and Zhiyong Lin and Yong Hu and Changqin Huang}, journal={Inf ...

ers who applied machine learning technology to written patient education materials. Therefore, we conducted a systematic search and rapid review to determine whether any applications of machine learning technology to the assessment of patient education materials exist and report on their utility. The purpose of this exercise is
This review examines passive digital phenotyping across the schizophrenia spectrum and bipolar disorders, with a focus on machine-learning studies. Methods A systematic review of passive digital phenotyping literature was conducted using keywords related to severe mental illnesses, data-collection devices (e.g., smartphones, wearables ...
Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review ...
A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs. KW - bioinformatics. KW - human-pathogen interactions. KW - protein-protein interactions. KW - systematic evaluation
In this systematic review, we search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from January 1, 2020 to October 3, 2020 which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images.
Oct 04, 2019 · Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyse data from the studies that are included in the review. Typically it focuses on ...
A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs. KW - bioinformatics. KW - human-pathogen interactions. KW - protein-protein interactions. KW - systematic evaluation
Apr 27, 2021 · Breaking boundaries.Empowering researchers.Opening Science. PLOS is a nonprofit, Open Access publisher empowering researchers to accelerate progress in science and medicine by leading…
Mar 12, 2019 · Systematic Review Tools Critical Appraisal: JBI Critical Appraisal Tools: (for research designs and systematic review) AMSTAR Checklist CASP (Critical Appraisals Skills Programme) Checklists Cochrane Handbook for Systematic Reviews of Interventions (V. 5.1) Software: COLANDR: open access, machine-learning assisted tool for conducting evidence ...
DOI: 10.1007/s00134-019-05872-y Corpus ID: 210835013. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy @article{Fleuren2020MachineLF, title={Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy}, author={L. Fleuren and T. Klausch and Charlotte L. Zwager and L. Schoonmade ...
SRI has lead groundbreaking innovations including ARPANET, Siri, and Motobot. Explore the historical timeline of important technological breakthroughs.
Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral ...
Jan 21, 2020 · This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results.
Feb 10, 2021 · A Systematic Literature Review of Different Machine Learning Methods on Hate Speech Detection Hate speech is one of the most challenging problem internet is facing today. This systematic literature review examine hate speech detection problem and will be used to do an experimental approach on detecting hate speech and abusive language.
Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm—namely, the convolutional extreme learning machine (CELM)—we present a systematic review that investigates alternative deep learning architectures that use ...
A Systematic Review of Urban Navigation Systems for Visually Impaired People Sensors (Basel). 2021 Apr 29;21(9):3103. doi: 10.3390/s21093103.
This Special Communication uses a systematic literature review to update previous dollar estimates of waste in the US health care system attributable to failure of care delivery and coordination, low-value care, price inflation, fraud, and administrative complexity. Article
May 06, 2021 · Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383–400. 23. Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. The impact of machine learning techniques in the study of bipolar disorder: a systematic review.
Fingerprint Dive into the research topics of 'Brain metastasis detection using machine learning: a systematic review and meta-analysis'. Together they form a unique fingerprint.
Mar 03, 2021 · In collaboration with experts in machine learning, engineering, and information management at Utrecht University, he set out to develop a tool that would significantly speed up the process of conducting systematic reviews and meta-analyses.
May 06, 2021 · Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383–400. 23. Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. The impact of machine learning techniques in the study of bipolar disorder: a systematic review.
Oct 26, 2020 · A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications Jaskaran Singh 1,2 , Moslem Azamfar 1 , Fei Li 1 and Jay Lee 1
A systematic literature review of predicting diabetic retinopathy, nephropathy and neuropathy in patients with type 1 diabetes using machine learning Background: Diabetic retinopathy, nephropathy and neuropathy in patients with type 1 diabetes (T1D) are microvascular complications that can adversely impact disease prognosis and incur greater ...
Mar 23, 2020 · Previously Resolution Enhancement Technology (RET), Design For Manufacturability (DFM), and Design-Technology Co-optimization (DTCO) techniques were the successful response to eliminating systematic yield limiting patterns. Machine learning, with its ability to find trends and make predictions based on large volumes of data, provides a unique path towards further reduction in systematic defect levels.
chine Learning solutions, and there has recently been an increase in interest in applying NLP to this process (Marshall and Wallace,2019). In this paper, we investigate the feasibility of implement-ing the multi-stage human process of a systematic review as a Machine Learning pipeline. We con-struct a systematic review pipeline which aims to
Apr 21, 2021 · OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.
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Sep 02, 2020 · Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. May 06, 2021 · Murtagh P, Greene G, O’Brien C. Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and meta-analysis. Int J Ophthalmol. 2020;13:149–62.
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Feb 15, 2021 · This paper presents a systematic review on anomaly detection in audio using machine learning techniques. This research had as main objective to obtain the state of the art, enabling an organization of ideas and summarization of information. In total, 31 studies were selected to study machine learning techniques for anomaly sound detection. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method .
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The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018. In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. May 06, 2021 · Murtagh P, Greene G, O’Brien C. Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and meta-analysis. Int J Ophthalmol. 2020;13:149–62. Sep 05, 2019 · What are the ethical issues that organizations may potentially face as they work to design and adopt Machine Learning systems in their processes? The systematic literature format was selected to serve as the foundation for this study. Kitchenham’s (2004) seminal work for systematic literature reviews functioned as the basis for this study.
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Feb 10, 2020 · A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. Aug 24, 2020 · Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review JMIR Med Inform 2021;9(1):e23811 doi: 10.2196/23811 PMID: 33326405 PMCID: 7806275
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May 06, 2021 · Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383–400. 23. Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. The impact of machine learning techniques in the study of bipolar disorder: a systematic review. May 05, 2015 · Machine learning Systematic review Naive Bays classifier Text classification Software engineering Metrics Recall: Abstract: Objective : To investigate whether machine learning and text-based data mining can be used to support the primary studies selection process and decrease the needed efforts in systematic reviews conducted in the context of SE.
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Bad Smell Detection Using Machine Learning Techniques: A Systematic Literature Review Ahmed Al-Shaaby, Hamoud Aljamaan and Mohammad Alshayeb 7 January 2020 | Arabian Journal for Science and Engineering, Vol. 45, No. 4 the use of machine learning techniques in securing IoT devices. In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was
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Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Conventional machine-learning techniques were limited in their A systematic literature review of predicting diabetic retinopathy, nephropathy and neuropathy in patients with type 1 diabetes using machine learning Background: Diabetic retinopathy, nephropathy and neuropathy in patients with type 1 diabetes (T1D) are microvascular complications that can adversely impact disease prognosis and incur greater ...
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Assess need for a systematic review 2. Assemble the systematic review team 3. Develop a research question 4. Define inclusion and exclusion criteria 5. Develop the protocol for the systematic review 6. Locate studies 7. Title/abstract & full-text review The Systematic Review Process A presentation from the New approaches to risk prediction session at ESC CONGRESS 2019
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Please join Sarah Towner Wright, MLS and Jennifer Walker Bissram, MSIS, from the Health Sciences Library, University of North Carolina-Chapel Hill, for a discussion about support of systematic reviews, from support of an institutional systematic review service to using machine learning to enhance literature searches. chine Learning solutions, and there has recently been an increase in interest in applying NLP to this process (Marshall and Wallace,2019). In this paper, we investigate the feasibility of implement-ing the multi-stage human process of a systematic review as a Machine Learning pipeline. We con-struct a systematic review pipeline which aims to
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Machine Learning with Applications (MLWA) is a peer reviewed, open access journal focused on research related to machine learning.The journal encompasses all aspects of research and development in ML, including but not limited to data mining, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, bioinformatics and their ... Jun 12, 2020 · Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.
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Machine Learning Algorithms and Techniques for Sentiment Analysis in Scientific Paper Reviews: A Systematic Literature Review Samuel Machado, University of Minho, Portugal, [email protected] Ana Carolina Ribeiro, University of Minho, Centro Algoritmi, Portugal, [email protected] Mar 31, 2020 · Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP.
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Aug 25, 2020 · Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of
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Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. May 19, 2019 · After years of development, machine learning methods have matured enough to be used in clinical medicine. In 2018 the FDA approved software to screen patients for diabetic retinopathy, and the methods are rapidly making their way into other applications for image analysis, natural language processing, EHR data mining, drug discovery, and more.
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