FCM Algorithm: Analysis of the Membership Function Influence and Its Consequences for Fuzzy Clustering

Mantilla, Luis; Yari, Yessenia

doi:10.1007/978-3-030-69774-7_9

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Each element has a membership value for each category. The degree of membership depends on the distance from the element to the center of the cluster, and they are inversely proportional [ 33 ]. In this study, the number of clusters for each disease was set to 10.…”

Section: Methodsmentioning

confidence: 99%

Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership

Feng

Zhang

et al. 2022

JMIR Form Res

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Background Due to the development of medical data, a large amount of clinical data has been generated. These unstructured data contain substantial information. Extracting useful knowledge from this data and making scientific decisions for diagnosing and treating diseases have become increasingly necessary. Unstructured data, such as in the Marketplace for Medical Information in Intensive Care III (MIMIC-III) data set, contain several ambiguous words that demonstrate the subjectivity of doctors, such as descriptions of patient symptoms. These data could be used to further improve the accuracy of medical diagnostic system assessments. To the best of our knowledge, there is currently no method for extracting subjective words that express the extent of these symptoms (hereinafter, “degree words”). Objective Therefore, we propose using the fuzzy c-means (FCM) method and Gaussian membership to quantify the degree words in the clinical medical data set MIMIC-III. Methods First, we preprocessed the 381,091 radiology reports collected in MIMIC-III, and then we used the FCM method to extract degree words from unstructured text. Thereafter, we used the Gaussian membership method to quantify the extracted degree words, which transform the fuzzy words extracted from the medical text into computer-recognizable numbers. Results The results showed that the digitization of ambiguous words in medical texts is feasible. The words representing each degree of each disease had a range of corresponding values. Examples of membership medians were 2.971 (atelectasis), 3.121 (pneumonia), 2.899 (pneumothorax), 3.051 (pulmonary edema), and 2.435 (pulmonary embolus). Additionally, all extracted words contained the same subjective words (low, high, etc), which allows for an objective evaluation method. Furthermore, we will verify the specific impact of the quantification results of ambiguous words such as symptom words and degree words on the use of medical texts in subsequent studies. These same ambiguous words may be used as a new set of feature values to represent the disorders. Conclusions This study proposes an innovative method for handling subjective words. We used the FCM method to extract the subjective degree words in the English-interpreted report of the MIMIC-III and then used the Gaussian functions to quantify the subjective degree words. In this method, words containing subjectivity in unstructured texts can be automatically processed and transformed into numerical ranges by digital processing. It was concluded that the digitization of ambiguous words in medical texts is feasible.

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Section: Methodsmentioning

confidence: 99%

Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership

Feng

Zhang

et al. 2022

JMIR Form Res

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Optical Stealth Algorithm Under Photoelectric Countermeasures and Its Effectiveness Evaluation

Yang

Jiang²,

et al. 2023

Smart Innovation, Systems and Technologies

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Fuzzy c-means for extraction and quantification of words representing degrees of diseases (Preprint)

Feng¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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BACKGROUND Modern medicine generates unstructured data containing a large amount of information. Extracting useful knowledge from this data and making scientific decisions for diagnosing and treating diseases have become increasingly necessary. Unstructured data, such as in the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, contain several ambiguous words demonstrating the subjectivity of doctors. These data can be used to further improve the accuracy of medical support system assessments. OBJECTIVE We propose using fuzzy c-means (FCM) method and Gauss membership to quantify the subjective words in the clinical medical dataset MIMIC-III. METHODS Using 381,091 radiology reports collected from MIMIC-III, we extracted words representing the subjective degree from the text and converted them into corresponding membership intervals based on the words. RESULTS Consequently, the words representing each degree of each disease had a range of corresponding values. Examples of membership medians were atelectasis (2.971), pneumonia (3.121), pneumothorax (2.899), pulmonary edema (3.051), and pulmonary embolus (2.435). These membership sections can determine the symptoms of each disease. CONCLUSIONS In this study, we used the FCM and Gaussian functions to extract words from the MIMIC-III, which represent a subjective degree and cannot be processed by a computer, and performed fuzzy processing on them. It was concluded that words representing the degree in an English interpreted report can be extracted and quantified. The use of these words in medical support systems may improve diagnostic accuracy.

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FCM Algorithm: Analysis of the Membership Function Influence and Its Consequences for Fuzzy Clustering

Cited by 3 publications

References 10 publications

Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership

Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership

Optical Stealth Algorithm Under Photoelectric Countermeasures and Its Effectiveness Evaluation

Fuzzy c-means for extraction and quantification of words representing degrees of diseases (Preprint)

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