scholarly journals Machine Learning in Medical Emergencies: a Systematic Review and Analysis

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
Inés Robles Mendo ◽  
Gonçalo Marques ◽  
Isabel de la Torre Díez ◽  
Miguel López-Coronado ◽  
Francisco Martín-Rodríguez
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Emergency Services ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
The Other ◽  
Other Hand ◽  
Increasing Demand ◽  
Commercial Applications

AbstractDespite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

Machine learning methods applied to triage in emergency services: A systematic review

2022 ◽  
Vol 60 ◽  
pp. 101109
Author(s):  
Rocío Sánchez-Salmerón ◽  
José L. Gómez-Urquiza ◽  
Luis Albendín-García ◽  
María Correa-Rodríguez ◽  
María Begoña Martos-Cabrera ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Emergency Services ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

2020 ◽  
Vol 84 (4) ◽  
pp. 305-314
Author(s):  
Daniel Vietze ◽  
Michael Hein ◽  
Karsten Stahl
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Service Life ◽  
Operating Time ◽  
The Other ◽  
Learning Approaches ◽  
State Of Health ◽  
Remaining Service Life ◽  
Other Hand ◽  
The One

AbstractMost vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

scholarly journals Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

2019 ◽  
Vol 1 (1) ◽  
pp. 116 ◽  
Author(s):  
Jalal Nouri ◽  
Ken Larsson ◽  
Mohammed Saqr
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Data Systems ◽  
Academic Institutions ◽  
Other Hand ◽  
Desirable Outcome ◽  
The One

<p class="0abstractCxSpLast">The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research. On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.</p>

Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data

2021 ◽  
Vol 11 (1) ◽  
pp. 7-14
Author(s):  
Uzair Aslam Bhatti ◽  
Linwang Yuan ◽  
Zhaoyuan Yu ◽  
Saqib Ali Nawaz ◽  
Anum Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Eye Diseases ◽  
Machine Learning Algorithms ◽  
Medical Resources ◽  
Individual Level ◽  
Healthcare Data ◽  
Increasing Demand

Healthcare diseases are spreading all around the globe day to day. Hospital datasets are full from the data with much information. It's an urgent requirement to use that data perfectly and efficiently. We propose a novel algorithm for predictive model for eye diseases using KNN with machine learning algorithms and artificial intelligence (AI). The aims are to evaluate the connection between the accumulated preoperative risk variables and different eye diseases and to manufacture a model that can anticipate the results on an individual level, thus giving relevance to impactful factors and geographic and demographic features. Risk factors of the desired diseases were calculated and machine learning algorithm applied to provide the prediction of the diseases. Health monitoring is an economic discipline that focuses on the effective allocation of medical resources, mainly to maximize the benefits of society to health through the available resources. With the increasing demand for medical services and the limited allocation of medical resources, the application of health economics in clinical practice has been paid more and more attention, and it has gradually played an important role in clinical decision-making.

scholarly journals Sero-prevalence of syphilis and associated factors among pregnant women in Ethiopia: a systematic review and meta-analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Habtamu Geremew ◽  
Demeke Geremew
Keyword(s):  
Systematic Review ◽  
Pregnant Women ◽  
Associated Factors ◽  
Meta Analysis ◽  
Regional Analysis ◽  
The Other ◽  
Transmitted Infection ◽  
Other Hand ◽  
Pooled Prevalence ◽  
History Of

Abstract Background Syphilis remained a major cause of reproductive morbidity and poor pregnancy outcomes in developing countries. Previously, studies showed inconsistent results and failed to show the actual picture of the diseases in Ethiopia. Thus, the aim of this meta-analysis was, first, to determine the updated pooled prevalence of syphilis among pregnant women in Ethiopia and, second, to assess its associated factors. Methods A comprehensive search was made on PubMed, Google scholar, Science Direct, and African Journals Online databases to identify relevant articles. A random effects model was used to estimate pooled syphilis prevalence and odds ratio (OR) with the respective 95% confidence intervals (CIs) using STATA 14 statistical software. I2 statistics and Egger’s regression test in conjunction with funnel plot was used to determine heterogeneity and publication bias among included studies respectively. Result We identified 13 suitable studies in this analysis. Accordingly, the pooled prevalence of syphilis among pregnant women in Ethiopia was 2.32% (95% CI, 1.68–2.97). Specifically, syphilis prevalence was 2.53% (95% CI, 1.92–3.14%) and 1.90% (95% CI, 0.40–3.40%) as per the treponemal and non-ytreponemal diagnostic test, respectively. On the other hand, regional analysis indicated that 4.06% (95% CI, 2.86–5.26) in Southern Nations Nationalities and Peoples (SNNP), 2.16% (95% CI, 1.57–2.75) in Amhara and 1.46% (95% CI, 0.69–2.23) in Oromia region. Being married (OR, 0.37 (95% CI, 0.12–0.91%)) was less likely to develop syphilis. On the other hand, women with history of multiple sexual partner (OR, 2.98 (95% CI, 1.15–7.70)) and women with history of previous sexually transmitted infection (STI) (OR, 4.88 (95% CI, 1.35–17.62)) have higher risk to develop syphilis. Besides, the pooled syphilis-HIV coinfection was 0.80% (95% CI, 0.60–1.01%). Conclusion This study provides evidence of relatively high prevalence of syphilis among pregnant women in Ethiopia. Therefore, it is recommended to further ramping up of current intervention measures to prevent future generations. Systematic review registration PROSPERO CRD42020211650

scholarly journals A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

2021 ◽  
Vol 4 (1) ◽  
pp. 01-26
Author(s):  
Muhammad Arif
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Social Media Networks ◽  
Current State ◽  
Art Research ◽  
Cyberbullying Detection

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

scholarly journals Natural Language Processing in Serious Games: A state of the art.

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Davide Picca ◽  
Dominique Jaccard ◽  
Gérald Eberlé
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Serious Games ◽  
State Of The Art ◽  
Serious Game ◽  
The Other ◽  
Other Hand ◽  
The One ◽  
High Level

In the last decades, Natural Language Processing (NLP) has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure:  first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.

scholarly journals Investigating Organizational Commitment among Medical Doctors, Hospital Nurses and Two Other Professional Jobs: A Systematic Review

2017 ◽  
Vol 9 (12) ◽  
pp. 99 ◽  
Author(s):  
Retno Setyowati ◽  
Priyotomo Priyotomo ◽  
Suharnomo Suharnomo
Keyword(s):  
Systematic Review ◽  
Organizational Commitment ◽  
The Other ◽  
Hospital Nurses ◽  
Medical Doctors ◽  
Organizational Goals ◽  
Hospital Nurse ◽  
Other Hand ◽  
The Common

This article describes the common characteristics of organizational commitment among professional jobs, such as medical doctors and hospital nurse, that have considerable important roles in creating company relationships with external parties. Those who work in these jobs bridge organizations and consumers on behalf of their companies, and are likely to bring in benefits to the development and achievement of organizational goals on one hand, and contrastly, also could be a harmful impact to the organization on the other hand. This study conceptually outlines the characteristics of organizational commitment among lawyers, accountants, and medical doctors. Results reveal some valuable considerations regarding organizational commitment in these professions.

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