scholarly journals User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e055525
Author(s):  
Yik-Ki Jacob Wan ◽  
Guilherme Del Fiol ◽  
Mary M McFarland ◽  
Melanie C Wright
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Adverse Events ◽  
Scoping Review ◽  
English Language ◽  
Learning Algorithms ◽  
Sudden Cardiac Arrest ◽  
Machine Learning Algorithms ◽  
Cochrane Library ◽  
Patient Deterioration

IntroductionEarly identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians’ decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.Methods and analysisWe will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool’s user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.Ethics and disseminationEthics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

scholarly journals Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Tamer A. Mesallam ◽  
Mohamed Farahat ◽  
Khalid H. Malki ◽  
Mansour Alsulaiman ◽  
Zulfiqar Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
English Language ◽  
Language Disorder ◽  
Learning Algorithms ◽  
Voice Disorders ◽  
Essential Element ◽  
Machine Learning Algorithms ◽  
Voice Disorder ◽  
Speech Features ◽  
The Voice

A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database.

scholarly journals Machine Learning for Pattern Detection in Cochlear Implant FDA Adverse Event Reports

2020 ◽  
Author(s):  
Matthew G. Crowson ◽  
Amr Hamour ◽  
Vincent Lin ◽  
Joseph M. Chen ◽  
Timothy C. Y. Chan
Keyword(s):  
Machine Learning ◽  
Adverse Event ◽  
Adverse Events ◽  
Cochlear Implant ◽  
Medical Device ◽  
Learning Algorithms ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Event Type

ABSTRACTImportanceThe United States Food & Drug Administration (FDA) passively monitors medical device performance and safety through submitted medical device reports (MDRs) in the Manufacturer and User Facility Device Experience (MAUDE) database. These databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design.ObjectivesThe objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants.DesignThe MDRs for the top three CI manufacturers by volume from January 1st 2009 to August 30th 2019 were retained for the analysis. Natural language processing was used to measure the importance of specific words. Four supervised machine learning algorithms were used to predict which adverse event narrative description pattern corresponded with a specific cochlear implant manufacturer and adverse event type - injury, malfunction, or death.SettingU.S. government public database.ParticipantsAdult and pediatric cochlear patients.ExposureSurgical placement of a cochlear implant.Main Outcome MeasureMachine learning model classification prediction accuracy (% correct predictions).Results27,511 adverse events related to cochlear implant devices were submitted to the MAUDE database during the study period. Most adverse events involved patient injury (n = 16,736), followed by device malfunction (n = 10,760), and death (n = 16). Submissions to the database were dominated by Cochlear Corporation (n = 13,897), followed by MedEL (n = 7,125), and Advanced Bionics (n = 6,489). The random forest, linear SVC, naïve Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively.Conclusions & RelevanceUsing supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.Level of evidence3

scholarly journals A Machine Learning-Based Method for Predicting unknown Pharmacointeractions

2019 ◽  
Vol 9 (2S) ◽  
pp. 662-665
Keyword(s):  
Machine Learning ◽  
Adverse Events ◽  
Drug Interactions ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Potential Harm ◽  
Impacting Factors

A lot of research has been done on the efficacy of machine learning algorithms in predicting the pharmacological interference between two drugs. Ordinarily, this interference depends on many factors such as the taxonomical, chemical, pharmacological or genomic similarities between the two drugs. Nevertheless, a lot of adverse events (AEs) are reported every year, due to the simultaneous consumption of two or more drugs. Much research has been conducted on the accuracy of the interference prediction based on these factors, each differing in the algorithms and factors used. In this publication, we propose a machine learning-based approach to predict undiscovered drug-drug interactions based on a few of the impacting factors, for better results and thus, help minimize the potential harm that can be caused to society.

Time Domain Features Based Sudden Cardiac Arrest Prediction Using Machine Learning Algorithms

2015 ◽  
Vol 5 (6) ◽  
pp. 1267-1271 ◽  
Author(s):  
M. Murugappan ◽  
L. Murukesan ◽  
Iqbal Omar ◽  
Sabira Khatun ◽  
Subbulakshmi Murugappan
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Time Domain ◽  
Learning Algorithms ◽  
Sudden Cardiac Arrest ◽  
Machine Learning Algorithms

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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