A novel infection screening method using a neural network and k-means clustering algorithm which can be applied for screening of unknown or unexpected infectious diseases

Sun, Guanghao; Hakozaki, Yukiya; Abe, Shigeto; Vịnh, Nguyễn Quang; Matsui, Takemi

doi:10.1016/j.jinf.2012.10.010

Cited by 23 publications

(15 citation statements)

References 5 publications

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“…These results are comparable to our previous work, in which we used a k-means clustering algorithm. 13 More importantly, the proposed optimal neural network and fuzzy clustering method were used to classify the multiple-dimensional vital-sign data to detect higher-risk influenza patients. The high level of accuracy of the Table 2 The vital signs and reference data (SpO 2 and axillary temperatures) of higher-risk influenza (HR-I) group, a lower-risk influenza (LR-I) group, and a non-influenza (Non-I) group are shown in part in this Table. Screening parameters References Figure 5 Heart rate, respiration rate, facial temperature, and SpO 2 compared within the HR-I group, LR-I group, and Non-I group.…”

Section: Discussionmentioning

confidence: 99%

“…This method was developed in our previous study, which uses Kohonen's self-organizing map 11 (SOM) and the k-means clustering algorithm 12 (a non-linear clustering algorithm). 13 The advantage of using SOM together with the non-linear clustering algorithm is that it allows the specification of any number of classification groups, not just two. Therefore, it is rational to increase the number of groups to three to investigate whether the higher-risk patients can be gathered together into a newly created group.…”

Section: Introductionmentioning

confidence: 99%

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An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

Sun

Matsui

Hakozaki

et al. 2015

Journal of Infection

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

Sun

Matsui

Hakozaki

et al. 2015

Journal of Infection

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“…To address these problems, we proposed a novel infection-screening system which remotely monitored vital signs for human medical inspections within 15 s in our previous studies [11][12][13]. The idea of developing the infection-screening system based on vital signs comes from the fact that infectious diseases are associated with inflammation when infected individuals become symptomatic [14].…”

Section: Introductionmentioning

confidence: 99%

A compact and hand-held infection-screening system for use in rapid medical inspection at airport quarantine stations: system design and preliminary validation

Sun

Miyata

Matsuoka

et al. 2015

Journal of Medical Engineering & Technology

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To conduct mass screening and thereby reduce the spread of infection, a compact (13.5 cm × 8.5 cm × 2.5 cm), highly-mobile and hand-held infection-screening system was developed for rapid medical inspection in mass gathering places such as airports. The system is capable of non-contact vital-sign monitoring using two integrated sensors: a 24-GHz microwave radar for measuring heart and respiration rates and a thermopile array for capturing facial temperature. Subsequently, the system detects infected individuals using a linear discriminant function (LDA) from the derived vital-signs data. The system was tested on 10 subjects under two conditions (resting as normal and exercising as pseudo-infected, i.e. a 10-min bicycle ergometer at 100 W exercise); the normal and pseudo-infected conditions were classified successfully via LDA for all subjects (p < 0.01; classification error rate < 5%). The proposed non-contact system can be applied for preventing secondary exposure of medical doctors at the outbreak of highly pathogenic infectious diseases such as the Ebola virus.

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“…Therefore, our screening system integrates multiple physiological parameters for rapid detection of influenza, which provides sensitivity that is superior to the fever-based screening system. We employed a neural network-based nonlinear discriminant function, which has been already reported in our previous paper [12]. This discriminant function allows for distinguishing of patients with influenza from normal control individuals using 4 physiological parameters: SpO 2 , heart rate, respiration rate, and facial temperature.…”

Section: Introductionmentioning

confidence: 99%

A neural network-based infection screening system that uses vital signs and percutaneous oxygen saturation for rapid screening of patients with influenza

Sun¹,

Hakozaki²,

Abe³

et al. 2013

Health

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Objective: Influenza is a highly infectious viral disease, which occurs epidemically almost every winter in Japan. Rapid screening of patients with suspected influenza in places of mass gathering is important to delay or prevent transmission of the infection. The aim of this study was to assess the effectiveness of our newly developed infection screening system that employed vital signs and percutaneous oxygen saturation (SpO 2) as parameters in a clinical setting. Methods: Since SpO 2 accurately reflects respiratory status during influenza virus infection, we upgraded our previous system by adding SpO 2 as a new parameter to improve the screening accuracy. This system instantly measures SpO 2 and vital signs (i.e., heart rate, respiration rate, and facial temperature), which automatically detects infected individuals via a neural network-based nonlinear discriminant function using these derived parameters. We tested the system on 45 patients with seasonal influenza (35.8˚C < body temperature < 40.0˚C, 18-35 years) and 64 normal control subjects (35.0˚C < body temperature < 37.5˚C, 18-30 years) at Japan Self-Defense Central Hospital in 2012. Results: The system identified 40/45 patients with influenza and 60/64 normal control subjects, and provided sensitivity, specificity, and positive and negative predictive value (PPV, NPV) of 88.8%, 93.8%, 90.9%, and 92.3%, respectively. By including SpO 2 as a screening parameter, we achieved superior sensitivity and NPV compared to that reported in our previous paper (sensitivity = 88%; NPV = 82%). Conclusions: Our results suggest that SpO 2 is a good screening parameter that improves the accuracy of infection screening. The proposed system has the potential to efficiently identify infected individuals, thereby delaying or preventing the spread of infection during epidemic seasons.

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A novel infection screening method using a neural network and k-means clustering algorithm which can be applied for screening of unknown or unexpected infectious diseases

Cited by 23 publications

References 5 publications

An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

A compact and hand-held infection-screening system for use in rapid medical inspection at airport quarantine stations: system design and preliminary validation

A neural network-based infection screening system that uses vital signs and percutaneous oxygen saturation for rapid screening of patients with influenza

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