Using Machine Learning to Support Resource Quality Assessment: An Adaptive Attribute-Based Approach for Health Information Portals

2011 ◽  
pp. 526-537 ◽  
Author(s):  
Jue Xie ◽  
Frada Burstein
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Health Information ◽  
Resource Quality ◽  
Support Resource

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

scholarly journals Evaluation of a staff training programme to reimplement a comprehensive health assessment

2018 ◽  
Vol 7 (4) ◽  
pp. e000353 ◽  
Author(s):  
Luke A Turcotte ◽  
Jake Tran ◽  
Joshua Moralejo ◽  
Nancy Curtin-Telegdi ◽  
Leslie Eckel ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Health Information ◽  
Health Assessment ◽  
Training Programme ◽  
Care Planning ◽  
Assessment Framework ◽  
High Quality ◽  
Data Quality Assessment ◽  
Comprehensive Health

BackgroundHealth information systems with applications in patient care planning and decision support depend on high-quality data. A postacute care hospital in Ontario, Canada, conducted data quality assessment and focus group interviews to guide the development of a cross-disciplinary training programme to reimplement the Resident Assessment Instrument–Minimum Data Set (RAI-MDS) 2.0 comprehensive health assessment into the hospital’s clinical workflows.MethodsA hospital-level data quality assessment framework based on time series comparisons against an aggregate of Ontario postacute care hospitals was used to identify areas of concern. Focus groups were used to evaluate assessment practices and the use of health information in care planning and clinical decision support. The data quality assessment and focus groups were repeated to evaluate the effectiveness of the training programme.ResultsInitial data quality assessment and focus group indicated that knowledge, practice and cultural barriers prevented both the collection and use of high-quality clinical data. Following the implementation of the training, there was an improvement in both data quality and the culture surrounding the RAI-MDS 2.0 assessment.ConclusionsIt is important for facilities to evaluate the quality of their health information to ensure that it is suitable for decision-making purposes. This study demonstrates the use of a data quality assessment framework that can be applied for quality improvement planning.

scholarly journals Quality Assessment of Online Complementary and Alternative Medicine Information Resources Relevant to Cancer

2021 ◽  
Vol 20 ◽  
pp. 153473542110660
Author(s):  
Megan E. Sansevere ◽  
Jeffrey D. White
Keyword(s):  
Alternative Medicine ◽  
Quality Assessment ◽  
Complementary And Alternative Medicine ◽  
Cancer Patients ◽  
Health Information ◽  
Cancer Center ◽  
The Internet ◽  
Healthcare Team ◽  
High Quality ◽  
Complementary And Alternative

Background: Complementary and alternative medicine (CAM) is often used by cancer patients and survivors in the US. Many people turn to the internet as their first source of information. Health information seeking through the internet can be useful for patients to gain a better understanding of specific CAM treatments to discuss with their healthcare team, but only if the information is comprehensive, high quality, and reliable. The aim of this article is to examine the content, writing/vetting processes, and visibility of cancer CAM online informational resources. Methods: Online CAM resources were identified by Google and PubMed searches, literature reviews, and through sources listed on various websites. The websites were analyzed through a modified online health information evaluation tool, DISCERN (score range = 1-5). The website’s features relevant to the quality assessment were described. Results: Eleven CAM websites were chosen for analysis. The DISCERN analysis showed a range of quality scores from 3.6 to 4.9. Lower DISCERN scores were generally due to deficiencies in describing the writing, editing, and updating processes. A lack of transparency with authorship and references was commonly present. Conclusion: Cancer patients interested in CAM need unbiased, evidence-based, reliable, high-quality, easily accessible educational materials. Individuals should use the guidelines followed in this analysis (including DISCERN and Medline Plus) to find reliable sources. Website developers can use CAM Cancer (NAFKAM), Beyond Conventional Cancer Therapies, Memorial Sloan Kettering Cancer Center, breastcancer.org , Office of Dietary Supplements, National Center for Complementary and Integrative Health, and Cancer.gov as models for trustworthy content.

scholarly journals Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review.

2020 ◽  
Author(s):  
Michael Moor ◽  
Bastian Rieck ◽  
Max Horn ◽  
Catherine Jutzeler ◽  
Karsten Borgwardt
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Quality Assessment ◽  
Biomarker Discovery ◽  
Learning Algorithms ◽  
Quality Criteria ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Synthesis Methods ◽  
Early Prediction

Background: Sepsis is among the leading causes of death in intensive care units (ICU) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. 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. Study eligibility criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study appraisal and synthesis methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from "poor" (satisfying less than 40% of the quality criteria) to "very good" (satisfying more than 90% of the quality criteria). The majority of the studies (n= 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n= 2,9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only 2 studies provided publicly-accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and key findings: A growing number of studies employs machine learning to31optimise the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic review registration number: CRD42020200133

scholarly journals Machine Learning for Non-Intrusive Speech Quality Assessment

2021 ◽  
Author(s):  
◽  
Mouna Hakami
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Latent Variable ◽  
Generative Models ◽  
Speech Quality ◽  
Speech Signals ◽  
Latent Space ◽  
Speech Quality Assessment

<p><b>This thesis presents two studies on non-intrusive speech quality assessment methods. The first applies supervised learning methods to speech quality assessment, which is a common approach in machine learning based quality assessment. To outperform existing methods, we concentrate on enhancing the feature set. In the second study, we analyse quality assessment from a different point of view inspired by the biological brain and present the first unsupervised learning based non-intrusive quality assessment that removes the need for labelled training data.</b></p> <p>Supervised learning based, non-intrusive quality predictors generally involve the development of a regressor that maps signal features to a representation of perceived quality. The performance of the predictor largely depends on 1) how sensitive the features are to the different types of distortion, and 2) how well the model learns the relation between the features and the quality score. We improve the performance of the quality estimation by enhancing the feature set and using a contemporary machine learning model that fits this objective. We propose an augmented feature set that includes raw features that are presumably redundant. The speech quality assessment system benefits from this redundancy as it results in reducing the impact of unwanted noise in the input. Feature set augmentation generally leads to the inclusion of features that have non-smooth distributions. We introduce a new pre-processing method and re-distribute the features to facilitate the training. The evaluation of the system on the ITU-T Supplement23 database illustrates that the proposed system outperforms the popular standards and contemporary methods in the literature.</p> <p>The unsupervised learning quality assessment approach presented in this thesis is based on a model that is learnt from clean speech signals. Consequently, it does not need to learn the statistics of any corruption that exists in the degraded speech signals and is trained only with unlabelled clean speech samples. The quality has a new definition, which is based on the divergence between 1) the distribution of the spectrograms of test signals, and 2) the pre-existing model that represents the distribution of the spectrograms of good quality speech. The distribution of the spectrogram of the speech is complex, and hence comparing them is not trivial. To tackle this problem, we propose to map the spectrograms of speech signals to a simple latent space.</p> <p>Generative models that map simple latent distributions into complex distributions are excellent platforms for our work. Generative models that are trained on the spectrograms of clean speech signals learned to map the latent variable $Z$ from a simple distribution $P_Z$ into a spectrogram $X$ from the distribution of good quality speech.</p> <p>Consequently, an inference model is developed by inverting the pre-trained generator, which maps spectrograms of the signal under the test, $X_t$, into its relevant latent variable, $Z_t$, in the latent space. We postulate the divergence between the distribution of the latent variable and the prior distribution $P_Z$ is a good measure of the quality of speech.</p> <p>Generative adversarial nets (GAN) are an effective training method and work well in this application. The proposed system is a novel application for a GAN. The experimental results with the TIMIT and NOIZEUS databases show that the proposed measure correlates positively with the objective quality scores.</p>

scholarly journals Predicting Health Material Cognitive Accessibility Using Multidimensional Semantic Features and Readability Tools as Predicators (Preprint)

2021 ◽  
Author(s):  
Meng Ji ◽  
Yanmeng Liu ◽  
Tianyong Hao
Keyword(s):  
Machine Learning ◽  
Health Education ◽  
Health Information ◽  
Domain Knowledge ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Semantic Features ◽  
Integrated Models ◽  
Advanced Education ◽  
Cognitive Accessibility

BACKGROUND Much of current health information understandability research uses medical readability formula (MRF) to assess the cognitive difficulty of health education resources. This is based on an implicit assumption that medical domain knowledge represented by uncommon words or jargons form the sole barriers to health information access among the public. Our study challenged this by showing that for readers from non-English speaking backgrounds with higher education attainment, semantic features of English health texts rather than medical jargons can explain the lack of cognitive access of health materials among readers with better understanding of health terms, yet limited exposure to English health education materials. OBJECTIVE Our study explored combined MRF and multidimensional semantic features (MSF) for developing machine learning algorithms to predict the actual level of cognitive accessibility of English health materials on health risks and diseases for specific populations. We compare algorithms to evaluate the cognitive accessibility of specialised health information for non-native English speaker with advanced education levels yet very limited exposure to English health education environments. METHODS We used 108 semantic features to measure the content complexity and accessibility of original English resources. Using 1000 English health texts collected from international health organization websites, rated by international tertiary students, we compared machine learning (decision tree, SVM, discriminant analysis, ensemble tree and logistic regression) after automatic hyperparameter optimization (grid search for the best combination of hyperparameters of minimal classification errors). We applied 10-fold cross-validation on the whole dataset for the model training and testing, calculated the AUC, sensitivity, specificity, and accuracy as the measured of the model performance. RESULTS Using two sets of predictor features: widely tested MRF and MSF proposed in our study, we developed and compared three sets of machine learning algorithms: the first set of algorithms used MRF as predictors only, the second set of algorithms used MSF as predictors only, and the last set of algorithms used both MRF and MSF as integrated models. The results showed that the integrated models outperformed in terms of AUC, sensitivity, accuracy, and specificity. CONCLUSIONS Our study showed that cognitive accessibility of English health texts is not limited to word length and sentence length conventionally measured by MRF. We compared machine learning algorithms combing MRF and MSF to explore the cognitive accessibility of health information from syntactic and semantic perspectives. The results showed the strength of integrated models in terms of statistically increased AUC, sensitivity, and accuracy to predict health resource accessibility for the target readership, indicating that both MRF and MSF contribute to the comprehension of health information, and that for readers with advanced education, semantic features outweigh syntax and domain knowledge.

scholarly journals No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Peng Xu ◽  
Man Guo ◽  
Lei Chen ◽  
Weifeng Hu ◽  
Qingshan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Statistical Features ◽  
Statistical Regression ◽  
Stereoscopic Image ◽  
Machine Learning Technique ◽  
Stereoscopic Images ◽  
Structure Representation ◽  
Learning Technique

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

scholarly journals Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features

2021 ◽  
Vol 18 (19) ◽  
pp. 10048
Author(s):  
Meng Ji ◽  
Wenxiu Xie ◽  
Riliu Huang ◽  
Xiaobo Qian
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Information ◽  
Self Care ◽  
Health Resources ◽  
Health Issues ◽  
Mental Health Information ◽  
Learning Classifier ◽  
Mental Health Issues ◽  
Bayesian Machine Learning

Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.

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