A novel machine-learning-based infection screening system via 2013–2017 seasonal influenza patients' vital signs as training datasets

Dagdanpurev, Sumiyakhand; Abe, Shigeto; Sun, Guanghao; Nishimura, Hidekazu; Choimaa, Lodoiravsal; Hakozaki, Yukiya; Matsui, Takemi

doi:10.1016/j.jinf.2019.02.008

Cited by 12 publications

(14 citation statements)

References 7 publications

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“…Their system is especially interesting as it uses contactless technologies that make it particularly suitable for clinical application with contagious patients [8]. More recently, Dagdanpurev et al [9] have developed a similar screening system using the same three clinical features. Their system uses a random tree algorithm to predict the patient's infection status and is easily understood by physicians because it can be expressed as a flow chart [9].…”

Section: An Early Illustration Of Ml-based Decision Support Systemsmentioning

confidence: 99%

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How machine learning could be used in clinical practice during an epidemic

Verdonk

Dreyfus

2020

Crit Care

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The COVID-19 epidemic is the cause of a crisis that is confronting the healthcare community with an unprecedented situation: emergency and intensive care units (ICU) are saturated, compelling physicians to make extremely hard decisions (triage). In such a resourceconstrained situation, physicians need decision support systems that could help them to optimally stratify patient risk. In the present paper, we explain how machine learning (ML) could help clinical practitioners during the epidemic (Fig. 1), and we describe some challenges and perspectives that could direct future efforts in the field. Why might machine learning be of particular interest?The first point to note is that ML encompasses a wide range of data analysis algorithms that run along a continuum from partially to fully machine-guided [1, 2]. For instance, deep learning is a fully machine-guided approach that uses large combinations of mathematical functions called "artificial neurons". Although recent contributions are promising [3], deep learning is not seen as particularly useful by some of the healthcare community because it produces black box models that are not readily interpretable by physicians [4]. Therefore, efforts to develop ML-based decision support systems for clinical practice should focus on models that incorporate as much prior medical knowledge as possible.A controversial step in traditional decision-making algorithms is the selection of variables (often termed "features") that are included in the predictive model.

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Section: An Early Illustration Of Ml-based Decision Support Systemsmentioning

confidence: 99%

“…More recently, Dagdanpurev et al [9] have developed a similar screening system using the same three clinical features. Their system uses a random tree algorithm to predict the patient's infection status and is easily understood by physicians because it can be expressed as a flow chart [9].…”

Section: An Early Illustration Of Ml-based Decision Support Systemsmentioning

confidence: 99%

How machine learning could be used in clinical practice during an epidemic

Verdonk

Dreyfus

2020

Crit Care

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“…Matsui et al developed a non-contact influenza screening system within 30 s using the newly developed testers for vital signs [ 20 ]. Dagdanpurev et al then developed an AI-based screening model for influenza using those patients’ vital signs [ 21 ]. Lopez et al studied AI-based influenza detection systems using natural language processing technology and concluded that a free-text record would help detect influenza [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, “I Think I Have Influenza,” Contributes to the Positivity Rate

Katsuki¹,

Matsuo²

2021

Cureus

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IntroductionRapid influenza diagnostic tests (RIDTs) are considered essential for determining when to start influenza treatment using anti-influenza drugs, but their accuracy is about 70%. Under the COVID-19 pandemic, we hope to refrain from performing unnecessary RIDTs considering droplet infection of COVID-19 and influenza. We re-examined the medical questionnaire's importance and its relationship to the positivity of RIDTs. Then we built a positivity prediction model for RIDTs using automated artificial intelligence (AI). MethodsWe retrospectively investigated 96 patients who underwent RIDTs at the outpatient department from December 2019 to March 2020. We used a questionnaire sheet with 24 items before conducting RIDTs. The factors associated with the positivity of RIDTs were statistically analyzed. We then used an automated AI framework to produce the positivity prediction model using the 24 items, sex, and age, with five-fold crossvalidation. ResultsOf the 47 women and 49 men (median age was 39 years), 56 patients were RIDT positive with influenza A. The AI-based model using 26 variables had an area under the curve (AUC) of 0.980. The stronger variables are subjective pretest probability, which is a numerically described score ranging from 0% to 100% of "I think I have influenza," cough, past hours after the onset, muscle pain, and maximum body temperature in order. ConclusionWe easily built the RIDT positivity prediction model using automated AI. Its AUC was satisfactory, and it suggested the importance of a detailed medical interview. Both the univariate analysis and AI-based model suggested that subjective pretest probability, "I think I have influenza," might be useful.

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“…A random tree algorithm is quick to implement because the algorithm chooses variables randomly to construct the tree. Previously, we used a random tree algorithm for influenza detection [19]. In addition, random tree algorithms have recently been used in various fields (e.g., industry, space, and medicine) because implementation is easy and rapid [20].…”

Section: Random Tree Algorithmmentioning

confidence: 99%

Random tree algorithm-based pediatric pneumonia screening using heart rate, respiration rate and temperature

Dagdanpurev¹,

Tsogzolmaa²,

Sun³

et al. 2019

JBRCI

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Despite a reduction in pneumonia-related mortality, pneumonia remains a leading cause of death among children aged 0-5 years. Most of these deaths occur in developing countries. However, more than half of pneumonia-related deaths are preventable with improved facilities and health strategies. Early rapid diagnosis is important to decrease pneumonia mortality. We developed a portable, cost-effective, rapid pneumonia screening system using a random tree algorithm to support early detection of pneumonia in children. We enrolled 105 participants: 57 patients aged 1-13 years (33 boys, 24 girls) diagnosed with pneumonia by chest radiograph and 48 normal volunteers aged 2-14 years (25 boys, 22 girls). We conducted a clinical trial in the Bayangol District Geriatric and Pediatric Hospital, Ulaanbaatar, Mongolia from January 12-19, 2019. Our screening system measured heart rate, respiration rate and skin temperature within 10 seconds and used a random tree algorithm to distinguish patients with pneumonia and normal volunteers. The system uses a photosensor, Doppler radar, and infrared thermophile to determine vital signs and an Arduino Nano microprocessor to perform computations. Paired t-tests were used to compare vital signs between patients with pneumonia and normal volunteers. The random tree algorithm achieved sensitivity of 96.5%, specificity of 81.3%, positive predictive value of 85.9%, and negative predictive value of 95.1%. The paired t-tests showed strong statistically significant differences in all three vital signs between patients with pneumonia and normal volunteers. Our random tree algorithm-based screening system offers an effective, rapid, and convenient tool for early detection of pneumonia in children. Its cost-effectiveness enables application in low-income countries. The system measures multiple vital signs simultaneously within 10 seconds, which may be useful for initial physical examinations in pediatric hospitals.

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A novel machine-learning-based infection screening system via 2013–2017 seasonal influenza patients' vital signs as training datasets

Cited by 12 publications

References 7 publications

How machine learning could be used in clinical practice during an epidemic

How machine learning could be used in clinical practice during an epidemic

Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, “I Think I Have Influenza,” Contributes to the Positivity Rate

Random tree algorithm-based pediatric pneumonia screening using heart rate, respiration rate and temperature

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