Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications

Rjoob, Khaled; Bond, Raymond; Finlay, Dewar; McGilligan, Victoria; Leslie, Stephen J; Rababah, Ali; Iftikhar, Aleeha; Guldenring, Daniel; Knoery, Charles; McShane, Anne; Peace, Aaron; Macfarlane, Peter W.

doi:10.1016/j.artmed.2022.102381

Cited by 25 publications

(10 citation statements)

References 28 publications

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“…The Support Vector Machine (SVM) is a well-established supervised machine learning approach that is commonly employed for classification and regression tasks. On the other hand, the Recursive Feature Elimination (RFE) algorithm is utilized to identify the most optimal combination of variables that maximizes the performance of the model [ 16 ]. Hence, the current investigation utilized the Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm to ascertain feature biomarkers possessing exceptional discriminative capacity.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

Chen,

Hua,

2024

BMC Genomics

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Background Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation. Methods The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes. Results A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes. Conclusion The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

Chen,

Hua,

2024

BMC Genomics

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“…RF uses several decision trees for classification or regression prediction [ 23 ]. Using the SVM technique, one may create a hyperplane with a maximum margin to discriminate between negative and positive examples [ 24 ]. In order to examine the association between normally distributed dependent characteristics and categorical or continuous independent data, GLM was developed as an extension of multiple linear regression models [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children

Chen,

Hua,

2023

BMC Genomics

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Background Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients. Method The expression profiles of cuproptosis regulators and immune characteristics in pediatric patients with ATB and LTBI were analyzed based on GSE39939 downloaded from the Gene Expression Omnibus. From the 52 ATB samples, we investigated the molecular subtypes based on differentially expressed cuproptosis-related genes (DE-CRGs) via consensus clustering and related immune cell infiltration. Subtype-specific differentially expressed genes (DEGs) were found using the weighted gene co-expression network analysis. The optimum machine model was then determined by comparing the performance of the eXtreme Gradient Boost (XGB), the random forest model (RF), the general linear model (GLM), and the support vector machine model (SVM). Nomogram and test datasets (GSE39940) were used to verify the prediction accuracy. Results Nine DE-CRGs (NFE2L2, NLRP3, FDX1, LIPT1, PDHB, MTF1, GLS, DBT, and DLST) associated with active immune responses were ascertained between ATB and LTBI patients. Two cuproptosis-related molecular subtypes were defined in ATB pediatrics. Single sample gene set enrichment analysis suggested that compared with Subtype 2, Subtype 1 was characterized by decreased lymphocytes and increased inflammatory activation. Gene set variation analysis showed that cluster-specific DEGs in Subtype 1 were closely associated with immune and inflammation responses and energy and amino acids metabolism. The SVM model exhibited the best discriminative performance with a higher area under the curve (AUC = 0.983) and relatively lower root mean square and residual error. A final 5-gene-based (MAN1C1, DKFZP434N035, SIRT4, BPGM, and APBA2) SVM model was created, demonstrating satisfactory performance in the test datasets (AUC = 0.905). The decision curve analysis and nomogram calibration curve also revealed the accuracy of differentiating ATB from LTBI in children. Conclusion Our study suggested that cuproptosis might be associated with the immunopathology of Mycobacterium tuberculosis infection in children. Additionally, we built a satisfactory prediction model to assess the cuproptosis subtype risk in ATB, which can be used as a reliable biomarker for the distinguishment between pediatric ATB and LTBI.

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“…Based on the results of this keyword-matching process, we calculated classification metrics, including accuracy, sensitivity, specificity, precision, and F1 score. Accuracy is the most commonly used metric in classification tasks, representing the proportion of correctly predicted classifications out of the total predictions 38 . Sensitivity measures the proportion of actual positives correctly identified by the model.…”

Section: Automatic Evaluation Of Ffa-gptmentioning

confidence: 99%

FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer

Chen,

Zhang,

et al. 2024

npj Digit. Med.

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Fundus fluorescein angiography (FFA) is a crucial diagnostic tool for chorioretinal diseases, but its interpretation requires significant expertise and time. Prior studies have used Artificial Intelligence (AI)-based systems to assist FFA interpretation, but these systems lack user interaction and comprehensive evaluation by ophthalmologists. Here, we used large language models (LLMs) to develop an automated interpretation pipeline for both report generation and medical question-answering (QA) for FFA images. The pipeline comprises two parts: an image-text alignment module (Bootstrapping Language-Image Pre-training) for report generation and an LLM (Llama 2) for interactive QA. The model was developed using 654,343 FFA images with 9392 reports. It was evaluated both automatically, using language-based and classification-based metrics, and manually by three experienced ophthalmologists. The automatic evaluation of the generated reports demonstrated that the system can generate coherent and comprehensible free-text reports, achieving a BERTScore of 0.70 and F1 scores ranging from 0.64 to 0.82 for detecting top-5 retinal conditions. The manual evaluation revealed acceptable accuracy (68.3%, Kappa 0.746) and completeness (62.3%, Kappa 0.739) of the generated reports. The generated free-form answers were evaluated manually, with the majority meeting the ophthalmologists’ criteria (error-free: 70.7%, complete: 84.0%, harmless: 93.7%, satisfied: 65.3%, Kappa: 0.762–0.834). This study introduces an innovative framework that combines multi-modal transformers and LLMs, enhancing ophthalmic image interpretation, and facilitating interactive communications during medical consultation.

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Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications

Cited by 25 publications

References 28 publications

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children

FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer

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