Application of fuzzy logic to the Apgar scoring system

Shono, Hiroshi; Oga, Masayuki; Shimomura, K.; Yamasaki, Mari; Ito, Yuji; Muro, Masami; Sugimori, Hajime

doi:10.1016/0020-7101(92)90074-3

Cited by 13 publications

(4 citation statements)

References 10 publications

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“…If one is interested in a crisp output it is possible to find it with a defuzzification method, like a Center of Area. 14,15 The fuzzy linguistic model to evaluate a risk of neonatal death has two antecedents: birth weight and Ortega NRS gestational age. The model was developed from one expert knowledge, who elaborated four fuzzy sets to the variable birth weight: very low birth weight (VLBW), low birth weight (LBW), insufficient birth weight (IBW), and normal birth weight (N); and three fuzzy sets to the variable gestational age: very preterm (VPT), preterm (PT) and term (T).…”

Section: Methodsmentioning

confidence: 99%

“…12,14 In fact, the theory of Fuzzy Sets has become an important mathematical approach in diagnosis systems, 2 treatment of medical images 4 and, more recently, in epidemiology 6,9,15 and public health. 7 The capability of working with linguistic variables, easiness of understanding, low computational cost, and its ability to incorporate to the systems the human expert experience, are attributes that make this approach an extremely interesting option to elaborate medical models.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy linguistic model for evaluating the risk of neonatal death

Nascimento

Ortega

2002

Rev. Saúde Pública

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OBJECTIVE: To introduce a fuzzy linguistic model for evaluating the risk of neonatal death. METHODS: The study is based on the fuzziness of the variables newborn birth weight and gestational age at delivery. The inference used was Mamdani's method. Neonatologists were interviewed to estimate the risk of neonatal death under certain conditions and to allow comparing their opinions and the model values. RESULTS: The results were compared with experts' opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r=0.96). CONCLUSIONS: The linguistic model was able to estimate the risk of neonatal death when compared to experts' performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy linguistic model for evaluating the risk of neonatal death

Nascimento

Ortega

2002

Rev. Saúde Pública

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“…Within the included studies, the first reported publication was on the use of ML in cardiology in 1992. 19 In the 3 decades since the evidence on the use of ML in children and adolescents has been available, specialties such as neonatology, psychiatry, and neurology have been the major contributors. The reasons for such preferential explorations are not clear, but the interest in use of ML in neonatology may be contributed by the large amount of observational data obtained from NICU monitoring.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Child and Adolescent Health: A Systematic Review

Hoodbhoy¹,

Jeelani²,

Aziz³

et al. 2020

Pediatrics

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“…Several studies have shown that the use of representations of medical data based on FSs (e.g., fuzzy Apgar score [9], Cadiag-2 [10]) is valuable when uncertainty is present.…”

Section: Introductionmentioning

confidence: 99%

Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication

et al. 2016

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Abstract-In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patientcompleted questionnaires which employ words to capture aspects of patients well-being are the norm. As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals. In this paper, we capture and model patients perceptions and associated uncertainty about the words used to describe the level of their physical function used in the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS) questionnaire. The paper provides detail about the interval-valued data capture as well as the subsequent modelling of the data using fuzzy sets. Based on an initial sample of participants, we use Jaccard similarity on the resulting words models to show that there may be considerable differences in the interpretation of commonly used questionnaire terms, thus presenting a very real risk of miscommunication between patients and medical professionals as well as within the group of medical professionals.

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Application of fuzzy logic to the Apgar scoring system

Cited by 13 publications

References 10 publications

Fuzzy linguistic model for evaluating the risk of neonatal death

Fuzzy linguistic model for evaluating the risk of neonatal death

Machine Learning for Child and Adolescent Health: A Systematic Review

Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication

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