A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)

Baral, S.; Alsadoon, Abeer; Prasad, P. W. C.; Aloussi, Sarmad Al; Alsadoon, Omar Hisham

doi:10.1007/s11042-021-11176-5

Cited by 15 publications

(5 citation statements)

References 37 publications

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“…This group of AI algorithms are often applied on time-independent tabular patient information. For textual, higher dimensional data, and grid like data types such as time series data and medical images, natural language processing (NLP), deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) models are widely applied (25,43,44). It is quite common in this domain that basic classifiers such as LR and decision tree based methods are applied to simplified representations of datasets to provide baselines for comparison to more sophisticated methods (3,14).…”

Section: Common Ai Methods Applied To Clinical Data For Patient Monit...mentioning

confidence: 99%

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Moazemi

Vahdati

et al. 2023

Front. Med.

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BackgroundArtificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.MethodsStudies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.ResultsMore than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.DiscussionClinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.

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Section: Common Ai Methods Applied To Clinical Data For Patient Monit...mentioning

confidence: 99%

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Moazemi

Vahdati

et al. 2023

Front. Med.

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“…The study in [22] used a hybrid model based on random forest along with correlationbased feature selection and principal component analysis (PCA) for predicting heart disease. They achieved an accuracy of 93.39% and showed that random forest with PCA-based feature selection can be an effective tool for predicting heart disease.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are many methods to extract the relevant features from ECG signals, but they may have the problems. However, CWT is considered to be the most powerful signal processor due to its simultaneous analysis in both the time and frequency domains, making it suitable for capturing the non-stationary characteristics of ECG signals [22]. CWT evaluates constant signals f(t) by using a mother wavelet function ψ(a,b), which is given by the integral:…”

Section: Feature Extractionmentioning

confidence: 99%

WT-CNN: A Hybrid Machine Learning Model for Heart Disease Prediction

Mohammad,

Al-Ahmadi

2023

Mathematics

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Heart disease remains a predominant health challenge, being the leading cause of death worldwide. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) take an estimated 17.9 million lives each year, accounting for 32% of all global deaths. Thus, there is a global health concern necessitating accurate prediction models for timely intervention. Several data mining techniques are used by researchers to help healthcare professionals to predict heart disease. However, the traditional machine learning models for predicting heart disease often struggle with handling imbalanced datasets. Moreover, when prediction is on the bases of complex data like ECG, feature extraction and selecting the most pertinent features that accurately represent the underlying pathophysiological conditions without succumbing to overfitting is also a challenge. In this paper, a continuous wavelet transformation and convolutional neural network-based hybrid model abbreviated as WT-CNN is proposed. The key phases of WT-CNN are ECG data collection, preprocessing, RUSBoost-based data balancing, CWT-based feature extraction, and CNN-based final prediction. Through extensive experimentation and evaluation, the proposed model achieves an exceptional accuracy of 97.2% in predicting heart disease. The experimental results show that the approach improves classification accuracy compared to other classification approaches and that the presented model can be successfully used by healthcare professionals for predicting heart disease. Furthermore, this work can have a potential impact on improving heart disease prediction and ultimately enhancing patient lifestyle.

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“…Performance is improved by the idea of integrating the most recent data along with older data to predict the subjects one step ahead. With the benefits of a hidden layer selffeedback mechanism, the LSTM model solves the long-term dependence problem [21]. Gates of input state, output state, and forget state in the LSTM model are known to be three unique gates that are used to update data that are meant to be stored in the memory cell.…”

Section: Classification Using Lstmmentioning

confidence: 99%

Coronary Heart Disease Classification Using Improved Penguin Emperor Optimization-Based Long Short Term Memory Network

Maramgere Ramaiah,

Srikantegowda

2023

IIUMEJ

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Ventricular fibrillation (VF) is the most life-threatening and dangerous type of Cardiac Arrhythmia (CA), with a mortality rate of 10-15% in a year. Therefore, early detection of cardiac arrhythmia is important to reduce the mortality rate. Many machine learning algorithms have been proposed and have proven their usefulness in the classification and detection of heart problems. In this research manuscript, a novel Long Short Term Memory (LSTM) classifier with Improved Penguin Optimization (IPEO) is implemented for VF classification. The IPEO is used in finding optimal hyperparameters that overcome the overfitting problem. The presented model is tested, trained, and validated using two standard datasets that are available publicly: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and the China Physiological Signal Challenge (CPSC) 2018 dataset. Both of them consist of ECG recordings for five seconds of coronary heart disease (CHD) patients. Furthermore, Fuzzy C-Means and Enhanced Fuzzy Rough Set method (FCM-ETIFRST) are used for feature selection to extract informative features and to cluster membership degree, non-membership degree, and hesitancy degree. On the MIT-BIH dataset, the proposed model achieved accuracy, sensitivity, specificity, precision, and Matthews’s correlation coefficient (MCC) of 99.75%, 98.29%, 98.39%, 98.35%, and 97.79% respectively. On the CPSC 2018 dataset, the proposed model achieved accuracy of 99.79%, sensitivity of 99.11%, specificity of 98.20%, precision of 99.43%, and MCC of 98.57%. Hence, the results proved that the proposed method provides better results in the classification of VF. ABSTRAK: Pemfibrilan Ventrikel (VF) adalah ancaman nyawa nombor satu dan jenis Aritmia Jantung (CA) berbahaya dengan kadar kematian 10-15% setahun. Oleh itu, pengesanan awal Aritmia Jantung sangat penting bagi mengurangkan kadar kematian. Terdapat banyak algoritma pembelajaran mesin yang telah dicadangkan dan terbukti berkesan dalam pengelasan dan pengesanan sakit jantung. Kajian ini mencadangkan kaedah baru pengelasan Memori Ingatan Jangka Panjang Pendek (LSTM) dengan Pengoptimuman Penambahbaikan Penguin (IPEO) yang dilaksanakan bagi klasifikasi VF. IPEO digunakan bagi mencari hiperparameter yang dapat mengatasi masalah padanan berlebihan. Model yang dicadangkan diuji, dilatih dan disahkan menggunakan dua dataset piawai yang dapat diperoleh secara terbuka: Institut Teknologi Hospital Massachusetts-Beth Israel (MIT-BIH) dan Cabaran Signal Psikologi Cina 2018 (CPSC). Kedua-dua data ini mempunyai rakaman ECG selama lima saat daripada pesakit Penyakit Jantung Koronari (CHD). Malah, kajian itu turut menggunakan Purata-C Kabur dan Kaedah Set Kasar Kabur Dipertingkat (FCM-ETIFRST) untuk pemilihan bagi mengekstrak ciri-ciri dan mengelaskan kelompok tahap keahlian, bukan ahli dan tahap keraguan. Bagi dataset MIT-BIH, model yang dicadangkan mencapai ketepatan, tahap sensitif, tahap spesifik, kejituan dan pekali kaitan Matthews (MCC) sebanyak 99.75%, 98.29%, 98.39%, 98.35%, dan 97.79% masing-masing. Bagi dataset CPSC 2018 pula, model yang dicadangkan mencapai ketepatan sebanyak 99.79%, 99.11% tahap sensitif , 98.20% tahap spesifik, 99.43% kejituan dan 98.57% MCC. Oleh itu, dapatan kajian membuktikan kaedah yang dicadangkan menunjukkan keputusan lebih baik dalam pengelasan VF.

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A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)

Cited by 15 publications

References 37 publications

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

WT-CNN: A Hybrid Machine Learning Model for Heart Disease Prediction

Coronary Heart Disease Classification Using Improved Penguin Emperor Optimization-Based Long Short Term Memory Network

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