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Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review

MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第6期34卷 062001-062001页

作者： Baidillah, Marlin Ramadhan Busono, Pratondo Riyanto, Riyanto Natl Res & Innovat Agcy Res Ctr Elect Bandung 40135 Indonesia

Asynchronous breathing (AB) during mechanical ventilation (MV) may lead to a detrimental effect on the patient's condition. Due to the massive amount of data displayed in a large ICU, a machine learning algorithm (MLA) was proposed extensively to extract the patterns within the multiple continuous-in-time vital signs, to determine which are the variables that will predict the AB, to intervene in the MV as an early warning system, and finally to replace a highly demand of clinician's cognition. This study reviews the MLA for prediction and detection models from vital signs monitoring data for MV intervention. Publication on MLA development on MV intervention based on vital signs monitoring to support clinicians' decision-making process was extracted from the three electronic academic research databases Web of Science Core Collection (WoSCC), ScienceDirect, and PUBMED Central to February 2023. 838 papers from the electronic academic research databases are extracted. There are 14 review papers, while 25 related papers that pass with the quality assessments (QA). Few studies have been published that considered VS monitoring data along with the MV parameters waveforms for MV intervention. Vital signs monitoring data is not the only predictor in the developed MLA. Most studies suggested that developing the MLA for direct MV intervention requires more concern in the pre-processing of real-time data to avoid false positive and false detection than developing MLA itself.

关键词： mechanical ventilation multiple continuous-in-time vital signs machine learning algorithm asynchronous breathing

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Co-pyrolytic interactions and products of brominated epoxy resin and polyethylene terephthalate: TG-FTIR analysis and machine learning prediction

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JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS 2023年 175卷

作者： Xu, Ziming Cheng, Can Zhong, Jian Gao, Wenhao Li, Jinping Liu, Jingxin Wuhan Text Univ Sch Environm Engn Wuhan 430073 Peoples R China Wuhan Text Univ Engn Res Ctr Clean Prod Text Dyeing & Printing Minist Educ Wuhan 430073 Peoples R China

Pyrolysis is an effective approach for organic waste utilization, and the co-pyrolysis of multiple materials holds potential for both environmental and economic benefits. Brominated epoxy resin (BER) and polyethylene terephthalate (PET) are widely used organic materials in modern society, and the productions of their corresponding wastes are increasing annually. However, limited research has been conducted on the co-pyrolysis of BER and PET. In this study, mixtures of BER and PET were prepared in various proportions, and thermogravimetry (TG) and thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR) analyses were employed to investigate the reaction characteristics, volatile product compositions, and evolution during co-pyrolysis. Synergistic effects were observed at lower temperatures in the major reaction stage for mixtures with PET content below 40%, while inhibitory effects were noted at higher temperatures. For mixtures with PET content over 40%, the antagonistic effect was more prominent. The main volatile products during co-pyrolysis were identified as CO2, benzoic acid, and alcohols, and their evolutions exhibited complex changes due to interactions. In addition, machine learning algorithms successfully predicted real-time volatile emissions, with the random forest model proving accurate. This study enhanced understanding of organics interactions during thermal treatment and would contribute to the reduction of environmental pollution.

关键词： Brominated epoxy resin (BER) Polyethylene terephthalate (PET) Co-pyrolysis characteristics Product identification machine learning algorithm

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Electric Vehicle Battery Temperature Prediction Using LSTM algorithm 4

Electric Vehicle Battery Temperature Prediction Using LSTM A...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Sridhar, B. Allirani, S. Reshmin Shafi, R.S. Rithvik Abinav, K.R. Siddharth, A. Sri Ramakrishna Engineering College Department of Eee Coimbatore India

ISBN: (纸本)9798350383997

In a rapidly evolving world, characterized by a growing emphasis on sustainable transportation, the pursuit of enhanced Electric Vehicle (EV) efficiency and reliability has become paramount. This abstract presents an innovative and intelligent system poised to redefine the management of EV batteries, prioritizing optimal performance and user safety. Our notable framework uses the capacities of cutting-edge LSTM machine learning algorithm to precisely anticipate, screen, and control EV battery temperatures. Its core objectives are clear: maintain battery temperatures within the optimal range and pre-emptively identify critical anomalies before they escalate. At the heart of this system lies its machine learning capabilities, continuously forecasting battery temperature trends. When optimal temperature is not within the stipulated range, the system swiftly takes action with dual purposes: heating or cooling the battery to safeguard its health and extend its lifespan. Yet, this system goes beyond mere reactivity to temperature extremes;it excels in predictive prowess, identifying irregularities in battery temperature patterns before they become critical issues. This proactive approach equips the system to issue timely alerts and intervene to prevent potential hazards. Statistics, enabling them to stay well-informed, take preventive actions, and experience worry-free journeys. © 2024 IEEE.

关键词： Electric Vehicle (EV) Long Short Term algorithm (LSTM) machine learning algorithm

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Automatic Train Overpass Using Camera and Load Sensors and Obstacle Detection 2

Automatic Train Overpass Using Camera and Load Sensors and O...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Aravendra Kumar Dubey, Ratnesh Kumar Mishra, Shubha Sharma, Suraj Itm University Csa Department Gwalior India Mits Center for Ai Department Gwalior India

ISBN: (纸本)9798350371055

In India, the most used means of transportation is rail. Enhancements are required to modern technology in order to make it safer for the general population. In this context, the research offers suggestions for automating railway gate operations. The level crossing responds to the arrival and departure of trains nearby by opening and closing on its own using Internet of Things load sensors and warning signals (buzzers or sirens). During the buffer closure period, an automobile may become stuck on the track in between crossing gates. It will be recognized by camera sensors, and Internet of Things sensors will alert the gate closing mechanism. This might significantly reduce the number of labor-intensive tasks and incidents at level crossings. © 2024 IEEE.

关键词： Automatic railway crossing Internet of things (IoT) machine learning algorithm unmanned crossing

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Microheater Integrated Nanotube Array Gas Sensor for Parts-Per-Trillion Level Gas Detection and Single Sensor-Based Gas Discrimination

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ACS NANO 2022年第7期16卷 10968-10978页

作者： Tang, Wenying Chen, Zhesi Song, Zhilong Wang, Chen Wan, Zhu'an Chan, Chak Lam Jonathan Chen, Zhuo Ye, Wenhao Fan, Zhiyong Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Shenzhen Res Inst Shenzhen 518057 Peoples R China

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electro-chemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ~7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

关键词： gas sensor pulse-heating trace gas detection machine learning algorithm single-sensor gas discrimination

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Integrated analysis of plasma and urine reveals unique metabolomic profiles in idiopathic inflammatory myopathies subtypes

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JOURNAL OF CACHEXIA SARCOPENIA AND MUSCLE 2022年第5期13卷 2456-2472页

作者： Liu, Di Zhao, Lijuan Jiang, Yu Li, Liya Guo, Muyao Mu, Yibing Zhu, Honglin Sun Yat Sen Univ Affiliated Hosp 1 Dept Rheumatol & Clin Immunol Guangzhou Guangdong Peoples R China Cent South Univ Xiangya Hosp Dept Rheumatol & Immunol Changsha Hunan Peoples R China Cent South Univ Xiangya Hosp Natl Clin Res Ctr Geriatr Disorders Changsha Hunan Peoples R China Hunan Normal Univ Inst Emergency Med Hunan Prov Key Lab Emergency & Crit Care Metabon Hunan Prov Peoples HospAffiliated Hosp 1 Changsha Hunan Peoples R China Cent South Univ Xiangya Hosp 3 Dept Rheumatol & Immunol Changsha Hunan Peoples R China Hunan Prov Maternal & Child Hlth Care Hosp Dept Nutr Changsha Hunan Peoples R China

Objectives Idiopathic inflammatory myopathies (IIM) are a class of autoimmune diseases with high heterogeneity that can be divided into different subtypes based on clinical manifestations and myositis-specific autoantibodies (MSAs). However, even in each IIM subtype, the clinical symptoms and prognoses of patients are very different. Thus, the identification of more potential biomarkers associated with IIM classification, clinical symptoms, and prognosis is urgently needed. Methods Plasma and urine samples from 79 newly diagnosed IIM patients (mean disease duration 4 months) and 52 normal control (NC) samples were analysed by high-performance liquid chromatography of quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS)/MS-based untargeted metabolomics. Orthogonal partial least-squares discriminate analysis (OPLS-DA) were performed to measure the significance of metabolites. Pathway enrichment analysis was conducted based on the KEGG human metabolic pathways. Ten machine learning (ML) algorithms [linear support vector machine (SVM), radial basis function SVM, random forest, nearest neighbour, Gaussian processes, decision trees, neural networks, adaptive boosting (AdaBoost), Gaussian naive Bayes and quadratic discriminant analysis] were used to classify each IIM subtype and select the most important metabolites as potential biomarkers. Results OPLS-DA showed a clear separation between NC and IIM subtypes in plasma and urine metabolic profiles. KEGG pathway enrichment analysis revealed multiple unique and shared disturbed metabolic pathways in IIM main [dermatomyositis (DM), anti-synthetase syndrome (ASS), and immune-mediated necrotizing myopathy (IMNM)] and MSA-defined subtypes (anti-Mi2+, anti-MDA5+, anti-TIF1 gamma+, anti-Jo1+, anti-PL7+, anti-PL12+, anti-EJ+, and anti-SRP+), such that fatty acid biosynthesis was significantly altered in both plasma and urine in all main IIM subtypes (enrichment ratio > 1). Random forest and AdaBoost performed best in classif

关键词： Idiopathic inflammatory myopathies machine learning algorithm Metabolomics Biomarkers

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Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Wang, Wei Wang, Wenhui Zhang, Dongdong Zeng, Peiji Wang, Yue Lei, Min Hong, Yongjun Cai, Chengfu Xiamen Univ Zhongshan Hosp Sch Med Dept Otolaryngol Head & Neck Surg Xiamen Peoples R China Xiamen Univ Sch Med Xiamen Peoples R China Haicang Hosp Xiamen Med Coll Otorhinolaryngol Head & Neck Surg Xiamen Peoples R China

Depending on the source of the blastophore, there are various subtypes of laryngeal cancer, each with a unique metastatic risk and prognosis. The forecasting of their prognosis is a pressing issue that needs to be resolved. This study comprised 5953 patients with glottic carcinoma and 4465 individuals with non-glottic type (supraglottic and subglottic). Five clinicopathological characteristics of glottic and non-glottic carcinoma were screened using univariate and multivariate regression for CoxPH (Cox proportional hazards);for other models, 10 (glottic) and 11 (non-glottic) clinicopathological characteristics were selected using least absolute shrinkage and selection operator (LASSO) regression analysis, respectively;the corresponding survival models were established;and the best model was evaluated. We discovered that RSF (Random survival forest) was a superior model for both glottic and non-glottic carcinoma, with a projected concordance index (C-index) of 0.687 for glottic and 0.657 for non-glottic, respectively. The integrated Brier score (IBS) of their 1-year, 3-year, and 5-year time points is, respectively, 0.116, 0.182, 0.195 (glottic), and 0.130, 0.215, 0.220 (non-glottic), demonstrating the model's effective correction. We represented significant variables in a Shapley Additive Explanations (SHAP) plot. The two models are then combined to predict the prognosis for two distinct individuals, which has some effectiveness in predicting prognosis. For our investigation, we established separate models for glottic carcinoma and non-glottic carcinoma that were most effective at predicting survival. RSF is used to evaluate both glottic and non-glottic cancer, and it has a considerable impact on patient prognosis and risk factor prediction.

关键词： Laryngeal carcinoma Survival analysis machine learning algorithm Individual prediction

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Performance Analysis of Support Vector machine (SVM) for Diabetes Disease Detection 5

Performance Analysis of Support Vector Machine (SVM) for Dia...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Krisdianto, Ricky Apriani, Ivana Masada, Hossey Hidayaturrahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331528553

Diabetes is an important health problem and has a significant mortality rate in Indonesia, so it is one of the diseases that need further attention. In medical diagnosis, classification methods are extensively utilized to build models using machine learning algorithms for predicting medical outcomes based on patient data. Support Vector machine (SVM) is one of the machine learning algorithms used in predicting diseases including diabetes. The purpose of this study was to discover the optimal performance achieved by the SVM kernel model in predicting diabetes. Two datasets containing 9 and 22 features are being utilized in this research to identify the relationships influencing the model's accuracy performance. Also applying preprocessing methods based on the needs of each dataset to improve the accuracy of the model. The result of this research shows that the Radial Basis Function is the best kernel, achieving 97% accuracy in the first dataset and 87% accuracy in the second dataset. © 2024 IEEE.

关键词： Diabetes Disease Kernel Functions machine learning algorithm Radial Basis Function Support Vector machine (SVM)

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Intelligent LoRa-Based Positioning System

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第9期16卷 2961-2975页

作者： Chen, Jiann-Liang Chen, Hsin-Yun Ma, Yi-Wei Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (***) revealed that AI@LBS system can reduce average distance error to 0.48m.

关键词： Location-Based Service LoRa Fingerprint Mechanism machine learning algorithm Synthetic Minority Over-sampling Technique (SMOTE) Density-Based Spatial Clustering of Applications with Noise (DBSCAB)

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Classification of plant diseases using machine and deep learning

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OPEN COMPUTER SCIENCE 2021年第1期11卷 491-508页

作者： Lamba, Monika Gigras, Yogita Dhull, Anuradha NorthCap Univ Dept Comp Sci & Engn Sect 23 A Gurugram 122001 Haryana India

Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naive Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases;however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function soft-max and soft-sign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

关键词： deep learning plant disease auto-color correlogram machine learning algorithm ACC

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