Relevance Vector Machine as Data-Driven Method for Medical Decision Making

Haddi, Z.; Ananou, Bouchra; Trardi, Youssef; Ouladsine, Mustapha; Pons, Jean-Francois; Delliaux, Stéphane; Deharo, J. C.

doi:10.23919/ecc.2019.8796141

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Usually, authors trained their algorithms on one of these datasets and tested them on the remaining. We believe that if the four datasets are merged (and categorized into AF and NSR groups), this could give rise to huge variability which results in good AF characterisation as previously demonstrated by the group research works [18][19][20].…”

Section: Databasesmentioning

confidence: 78%

Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation

Haddi¹,

Ananou

Trardi

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Automatic and fast atrial fibrillation (AF) diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. Although the published results do show satisfactory detection accuracy, computational complexity of such methods is still questionable. This study proposes an alternative way to diagnosis AF arrhythmia which is based on the combination of seven univariate data analysis-based detectors followed by a majority voting in order to build a digital fingerprint of AF. Four publicly-accessible sets of clinical data were used for AF assessment. The time series were segmented in 10 s RR interval window. The features of the four databases were merged in order to give rise huge variability and therefore to better characterize AF arrhythmia. Afterwards, a receiver operating characteristic curve analysis has been conducted to fix optimal thresholds for AF detection. Finally, the seven obtained detectors have been concatenated and then a majority rule was applied to yield a final decision on AF diagnosis. The results showed that this strategy performed better than some existing algorithms do, with 98.50% for sensitivity and 95.1 % specificity.

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Section: Databasesmentioning

confidence: 78%

Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation

Haddi¹,

Ananou

Trardi

et al. 2020

2020 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

“…Currently, data-driven unsupervised clustering is more widely used in the medical field, for example, in electronic medical records [44,45], medical decision making [46,47], and medical image analysis [8,9,48]. Additionally, several digital pathology studies have adopted unsupervised clustering to represent various features [49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering

Chen

Hussein

Xing

et al. 2022

Cancers

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Identifying the progression of chronic lymphocytic leukemia (CLL) to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications as it prompts a major change in patient management. However, the differentiation between these disease phases may be challenging in routine practice. Unsupervised learning has gained increased attention because of its substantial potential in data intrinsic pattern discovery. Here, we demonstrate that cellular feature engineering, identifying cellular phenotypes via unsupervised clustering, provides the most robust analytic performance in analyzing digitized pathology slides (accuracy = 0.925, AUC = 0.978) when compared to alternative approaches, such as mixed features, supervised features, unsupervised/mixed/supervised feature fusion and selection, as well as patch-based convolutional neural network (CNN) feature extraction. We further validate the reproducibility and robustness of unsupervised feature extraction via stability and repeated splitting analysis, supporting its utility as a diagnostic aid in identifying CLL patients with histologic evidence of disease progression. The outcome of this study serves as proof of principle using an unsupervised machine learning scheme to enhance the diagnostic accuracy of the heterogeneous histology patterns that pathologists might not easily see.

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“…Refs. [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ] are cited in supplementary materials.…”

mentioning

confidence: 99%

A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications

Wang

Chen

Hershkovich

et al. 2022

Sensors

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Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.

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Relevance Vector Machine as Data-Driven Method for Medical Decision Making

Cited by 5 publications

References 19 publications

Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation

Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering

A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications

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