scholarly journals Digital approaches to automated and machine learning assessments of hearing: a scoping review (Preprint)

10.2196/32581 ◽  
2021 ◽  
Author(s):  
Jan-Wilem Wasmann ◽  
Leontien Pragt ◽  
Robert Eikelboom ◽  
De Wet Swanepoel
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Learning Assessments

scholarly journals Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053674
Author(s):  
Enrico Glaab ◽  
Armin Rauschenberger ◽  
Rita Banzi ◽  
Chiara Gerardi ◽  
Paula Garcia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Biomarker Discovery ◽  
Biomedical Literature ◽  
Molecular Signature ◽  
Biological Knowledge ◽  
Measurement Technology ◽  
Omics Data ◽  
Patient Stratification ◽  
Complex Disorders

ObjectiveTo review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.DesignScoping review.MethodsWe searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.ResultsOverall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.ConclusionsWhile most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

Machine Learning and Medication Adherence: Scoping Review (Preprint)

2021 ◽  
Author(s):  
Aaron Bohlmann ◽  
Javed Mostafa
Keyword(s):  
Machine Learning ◽  
Medication Adherence ◽  
Scoping Review ◽  
Developmental Stages ◽  
Medication Compliance ◽  
Medication Administration ◽  
Sensor Data ◽  
Medication Monitoring ◽  
User Acceptability ◽  
Traditional Approaches

BACKGROUND This is the first scoping review broadly focused on machine learning and medication adherence. OBJECTIVE To categorize and summarize literature focused on using machine learning for medication compliance activities. METHODS PubMed, Scopus, ACM Digital library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. Study information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of medication adherence activities carried out. The protocol for this scoping review was created using the PRISMA-ScR guidelines. RESULTS Publications focused on predicting medication adherence have uncovered strong predictors that were significant across multiple studies. Studies that used machine learning to monitor medication compliance are generally still in early developmental stages and used a variety of sensor data to detect medication administration. Systems that combined medication monitoring with intervention were mostly concerned with detecting medication administration and only a few compared their system against more traditional approaches. CONCLUSIONS In general, this topic currently has relatively few publications but has been generating more interest over the last few years. Although important features for predicting adherence have been identified more work needs to be done to understand the complex interplay between these features. Systems used to monitor medication compliance also require further testing in more realistic environments and user acceptability evaluations. When interventions are attempted the effectiveness of the system should be evaluated against current systems used to encourage medication compliance. CLINICALTRIAL NONE

scholarly journals Gut microbiota and artificial intelligence approaches: A scoping review

2020 ◽  
Vol 10 (6) ◽  
pp. 1343-1358
Author(s):  
Ernesto Iadanza ◽  
Rachele Fabbri ◽  
Džana Bašić-ČiČak ◽  
Amedeo Amedei ◽  
Jasminka Hasic Telalovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Gut Microbiota ◽  
Scoping Review ◽  
Massive Data ◽  
Data Sets ◽  
Research Groups ◽  
Human Gut ◽  
Massive Data Sets

Abstract This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

Machine learning assessments of soil drying for agricultural planning

2014 ◽  
Vol 104 ◽  
pp. 93-104 ◽  
Author(s):  
Evan J. Coopersmith ◽  
Barbara S. Minsker ◽  
Craig E. Wenzel ◽  
Brian J. Gilmore
Keyword(s):  
Machine Learning ◽  
Soil Drying ◽  
Learning Assessments ◽  
Agricultural Planning

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.

Overview of the role of big data in mental health: A scoping review (Preprint)

2021 ◽  
Author(s):  
Arfan Ahmed ◽  
Sarah Aziz ◽  
Marco Angus ◽  
Mahmood Alzubaidi ◽  
Alaa Abd-Alrazaq ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Big Data ◽  
Scoping Review ◽  
Mental Health Disorders ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Great Effort ◽  
Scoping Reviews ◽  
Health Disorders

BACKGROUND Big Data offers promise in the field of mental health and plays an important part when it comes to automation, analysis and prevention of mental health disorders OBJECTIVE The purpose of this scoping review is to explore how big data was exploited in mental health. This review specifically addresses both the volume, velocity, veracity and variety of collected data as well as how data was attained, stored, managed, and kept private and secure. METHODS Six databases were searched to find relevant articles. PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used as a guideline methodology to develop a comprehensive scoping review. RESULTS General and Big Data features were extracted from the studies reviewed. Various technologies were noted when it comes to using Big Data in mental health with depression and anxiety being the focus of most of the studies. Some of these included Machine Learning (ML) models in 22 studies of which Random Forest (RF) was the most widely used. Logistic Regression (LR) was used in 4 studies, and Support Vector Machine (SVM) was used in 3 studies. CONCLUSIONS In order to utilize Big Data as a way to mitigate mental health disorders and prevent their appearance altogether a great effort is still needed. Integration and analysis of Big Data, doctors and researchers alike can find patterns in otherwise difficult to identify data by making use of AI and Machine Learning techniques. Similarly, machine learning and artificial intelligence can be used to automate the analytical process.

scholarly journals The use of machine learning in rare diseases: a scoping review

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Julia Schaefer ◽  
Moritz Lehne ◽  
Josef Schepers ◽  
Fabian Prasser ◽  
Sylvia Thun
Keyword(s):  
Machine Learning ◽  
Rare Disease ◽  
Rare Diseases ◽  
Scoping Review ◽  
Input Data ◽  
Skin Diseases ◽  
Demographic Data ◽  
Support Vector ◽  
Research Activity ◽  
Data Set

Abstract Background Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. Methods Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010–2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. Results Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and “omics” data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). Conclusion Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

scholarly journals Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e035983
Author(s):  
Rodrigo M Carrillo-Larco ◽  
Lorainne Tudor Car ◽  
Jonathan Pearson-Stuttard ◽  
Trishan Panch ◽  
J Jaime Miranda ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Scoping Review ◽  
Low Income ◽  
Clinical Medicine ◽  
Scientific Publications ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Health Related

IntroductionMachine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs.Methods and analysisThis scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations.Ethics and disseminationThe review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.

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