Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods

Li, Pengpai; Hu, Yongmei; Liu, Zhiping

doi:10.1016/j.bspc.2021.102474

Cited by 42 publications

(15 citation statements)

References 23 publications

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“…A typical clinical practice uses a diverse set of information formats contained within the patient electronic health record (EHR) such as tabular data (e.g., age, demographics, procedures, history, billing codes), image data (e.g., photographs, x-rays, computerized-tomography scans, magnetic resonance imaging, pathology slides), time-series data (e.g., intermittent pulse oximetry, blood chemistry, respiratory analysis, electrocardiograms, ultra-sounds, in-vitro tests, wearable sensors), structured sequence data (e.g., genomics, proteomics, metabolomics) and unstructured sequence data (e.g., notes, forms, written reports, voice recordings, video) among other sources 6 . Recently, AI/ML models leveraging multiple data modalities have been demonstrated for the domains of cardiology 7 – 9 , dermatology 10 , gastroenterology 11 , gynecology 12 , hematology 13 , immunology 14 , nephrology 15 , neurology 16 , 17 , oncology 18 – 20 , ophthalmology 21 , psychiatry 22 , radiology 23 – 25 , public health 26 and healthcare operational analytics (i.e., mortality, length-of-stay, and discharge predictions) 27 – 30 . Furthermore, it has been shown that multimodality in most of these domains can increase the performance of AI/ML systems (accuracy: 1.2–27.7%) compared to single-modality approaches for the same task 2 .…”

Section: Introductionmentioning

confidence: 99%

Integrated multimodal artificial intelligence framework for healthcare applications

Soenksen

Zeng

et al. 2022

npj Digit. Med.

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Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N = 34,537 samples) containing 7279 unique hospitalizations and 6485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6–33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48 h mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.

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Section: Introductionmentioning

confidence: 99%

Integrated multimodal artificial intelligence framework for healthcare applications

Soenksen

Zeng

et al. 2022

npj Digit. Med.

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“…Pengpai Li et al created a multi-modal feature based on PCG and ECG signals to diagnose cardiovascular diseases (CVDs). They used SVM as the classifier, and the AUC value of their model's highest performance is 0.936 [ 24 ]. Miguel et al developed a method for separating PCG signals into silences and basic heart sounds.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method

Yang

Xiang

et al. 2022

BMC Med Inform Decis Mak

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Background The diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases. Methods For the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals. Results As a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories. Conclusion It can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task.

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“…Li et al 2021 [ 76 ] presented multi-modal machine learning techniques related to heart diseases. From Figure 3 , it can be seen that one or more components of the contactless HR framework can be achieved by using deep learning.…”

Section: Contactless Ppg Methods Based On Deep Learningmentioning

confidence: 99%

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Azarang

Kehtarnavaz

2021

Sensors

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The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods

Cited by 42 publications

References 23 publications

Integrated multimodal artificial intelligence framework for healthcare applications

Integrated multimodal artificial intelligence framework for healthcare applications

Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

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