Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis

Ao, Chunyan; Jin, Shunshan; Ding, Hui; Zou, Quan; Yu, Liang

doi:10.2174/1381612826666200331091156

Cited by 24 publications

(12 citation statements)

References 58 publications

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“…We use four common measurements to illuminate the performance quality, that is Sensitivity (Sn), Specificity (Sp), Accuracy (Acc) and Matthew’s correlation coefficient (MCC)were adopted to evaluate the above three methods and four classifiers. These methods are formulated as follows ( Wei et al, 2014 , 2017a , b , 2018c ; Zhao et al, 2017 ; Wang G. et al, 2018 ; Cheng, 2019 ; Cheng et al, 2019 ; Yang et al, 2019 ; Ao et al, 2020a ; Hasan et al, 2020 ; Qiang et al, 2020 ; Tang et al, 2020 ):…”

Section: Methodsmentioning

confidence: 99%

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

Bai

Chen

Lin

et al. 2021

Front. Cell Dev. Biol.

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To distinguish Methicillin-Resistant Staphylococcus aureus (MRSA) from Methicillin-Sensitive Staphylococcus aureus (MSSA) in the protein sequences level, test the susceptibility to antibiotic of all Staphylococcus aureus isolates from Quanzhou hospitals, define the virulence factor and molecular characteristics of the MRSA isolates. MRSA and MSSA Pfam protein sequences were used to extract feature vectors of 188D, n-gram and 400D. Weka software was applied to classify the two Staphylococcus aureus and performance effect was evaluated. Antibiotic susceptibility testing of the 81 Staphylococcus aureus was performed by the Mérieux Microbial Analysis Instrument. The 65 MRSA isolates were characterized by Panton-Valentine leukocidin (PVL), X polymorphic region of Protein A (spa), multilocus sequence typing test (MLST), staphylococcus chromosomal cassette mec (SCCmec) typing. After comparing the results of Weka six classifiers, the highest correctly classified rates were 91.94, 70.16, and 62.90% from 188D, n-gram and 400D, respectively. Antimicrobial susceptibility test of the 81 Staphylococcus aureus: Penicillin-resistant rate was 100%. No resistance to teicoplanin, linezolid, and vancomycin. The resistance rate of the MRSA isolates to clindamycin, erythromycin and tetracycline was higher than that of the MSSAs. Among the 65 MRSA isolates, the positive rate of PVL gene was 47.7% (31/65). Seventeen sequence types (STs) were identified among the 65 isolates, and ST59 was the most prevalent. SCCmec type III and IV were observed at 24.6 and 72.3%, respectively. Two isolates did not be typed. Twenty-one spa types were identified, spa t437 (34/65, 52.3%) was the most predominant type. MRSA major clone type of molecular typing was CC59-ST59-spa t437-IV (28/65, 43.1%). Overall, 188D feature vectors can be applied to successfully distinguish MRSA from MSSA. In Quanzhou, the detection rate of PVL virulence factor was high, suggesting a high pathogenic risk of MRSA infection. The cross-infection of CA-MRSA and HA-MRSA was presented, the molecular characteristics were increasingly blurred, HA-MRSA with typical CA-MRSA molecular characteristics has become an important cause of healthcare-related infections. CC59-ST59-spa t437-IV was the main clone type in Quanzhou, which was rare in other parts of mainland China.

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Section: Methodsmentioning

confidence: 99%

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

Bai

Chen

Lin

et al. 2021

Front. Cell Dev. Biol.

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“…first, big data analysis will be more intelligent. It will be easier to find epidemics at the early stage, track close contacts, improve diagnosis and treatment efficiency, predict the possible evolution of viruses in the future and develop more effective and long-lasting vaccines by analysing massive and real-time data with machine learning and deep learning [ 124 – 126 ]. Second, epidemic prediction will be more accurate.…”

Section: Discussionmentioning

confidence: 99%

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

Dong

Zhou

et al. 2021

J Med Syst

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COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

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“…The harm of distributional imbalance is that, estimated uncertainty tends to be low even for incorrect classifications. It would cause the neural network to give wrong predictions confidently, which is unaccept- able in some risk-sensitive applications like disease diagnosis (Ao et al, 2020).…”

Section: Concept Of Distributional Imbalancementioning

confidence: 99%

“…2012), natural language processing (Deng and Liu, 2018) and data mining (Han et al, 2011). However, in some risk-sensitive applications, deploying traditional deep-learning models may bring disastrous outcomes since their predictions are not trustworthy (Floridi, 2019), such as disease diagnosis (Ao et al, 2020), automatic driving (Yasunobu and Sasaki, 2003) and robotics (Davies, 2000). Deep-learning models need to be interpretable (Molnar, 2020) and trustworthy (Abdar et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Identifying Incorrect Classifications with Balanced Uncertainty

Li¹,

Zheng²,

Zhang³

2021

Preprint

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Uncertainty estimation is critical for costsensitive deep-learning applications (i.e. disease diagnosis).It is very challenging partly due to the inaccessibility of uncertainty groundtruth in most datasets. Previous works proposed to estimate the uncertainty from softmax calibration, Monte Carlo sampling, subjective logic and so on. However, these existing methods tend to be overconfident about their predictions with unreasonably low overall uncertainty, which originates from the imbalance between positive (correct classifications) and negative (incorrect classifications) samples. For this issue, we firstly propose the distributional imbalance to model the imbalance in uncertainty estimation as two kinds of distribution biases, and secondly propose Balanced True Class Probability (BT CP ) framework, which learns an uncertainty estimator with a novel Distributional Focal Loss (DF L) objective. Finally, we evaluate the BT CP in terms of failure prediction and out-of-distribution (OOD) detection on multiple datasets. The experimental results show that BT CP outperforms other uncertainty estimation methods especially in identifying incorrect classifications.

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Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis

Cited by 24 publications

References 58 publications

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

Identifying Incorrect Classifications with Balanced Uncertainty

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