著者

Mikio Nakajima Yohei Okada Tomohiro Sonoo Tadahiro Goto

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

pp.JE20220147, (Released:2022-07-16)

参考文献数

15

被引用文献数

14

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Background: The Japan Coma Scale (JCS) is the most frequently adopted method for evaluating level of consciousness in Japan. However, no validated method for converting the JCS to Glasgow Coma Scale (GCS) exists. The aims of the present study were to develop and validate a method to convert the JCS to GCS.Methods: This is a multicenter retrospective cohort study involving three emergency departments (EDs) in Japan. We included all adult patients who visited the ED between 2017 and 2020. The participating facilities were divided into two cohorts—one cohort to develop a table to convert the JCS to GCS (development cohort), and the other cohort to validate the conversion table (validation cohort). The conversion table of the JCS to GCS was developed based on the median values of the GCS. The outcome was the concordance rate between the JCS and GCS.Results: We identified 8,194 eligible patients. The development cohort included 7,373 patients and the validation cohort included 821 patients. In the validation cohort, the absolute and relative concordance rates were 80.3% (95% confidence interval, 77.4–82.9%) and 93.2% (95% confidence interval, 91.2–94.8%), respectively.Conclusions: This study developed and validated a novel method for converting the JCS to GCS. Assuming the offset by a single category between the JCS and GCS is acceptable, the concordance rate was over 90% in the general adult patient population visiting the ED. The conversion method may assist researchers to convert JCS into GCS, which is more commonly recognized among global audiences.

著者

Mikio Nakajima Yohei Okada Tomohiro Sonoo Tadahiro Goto

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

vol.33, no.10, pp.531-535, 2023-10-05 (Released:2023-10-05)

参考文献数

15

被引用文献数

14

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Background: The Japan Coma Scale (JCS) is the most frequently adopted method for evaluating level of consciousness in Japan. However, no validated method for converting the JCS to the Glasgow Coma Scale (GCS) exists. The aims of the present study were to develop and validate a method to convert the JCS to GCS.Methods: This is a multicenter retrospective analysis involving three emergency departments (EDs) in Japan. We included all adult patients who visited the ED between 2017 and 2020. The participating facilities were divided into two cohorts—one cohort to develop a table to convert the JCS to GCS (development cohort), and the other cohort to validate the conversion table (validation cohort). The conversion table of the JCS to GCS was developed based on the median values of the GCS. The outcome was the concordance rate between the JCS and GCS.Results: We identified 8,194 eligible patients. The development cohort included 7,373 patients and the validation cohort included 821 patients. In the validation cohort, the absolute and relative concordance rates were 80.3% (95% confidence interval, 77.4–82.9%) and 93.2% (95% confidence interval, 91.2–94.8%), respectively.Conclusion: This study developed and validated a novel method for converting the JCS to GCS. Assuming the offset by a single category between the JCS and GCS is acceptable, the concordance rate was over 90% in the general adult patient population visiting the ED. The conversion method may assist researchers to convert JCS scores into GCS scores, which are more commonly recognized among global audiences.

著者

Mikio Nakajima Yohei Okada Tomohiro Sonoo Tadahiro Goto

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

vol.33, no.10, pp.544, 2023-10-05 (Released:2023-10-05)

参考文献数

4

著者

Sachiko Ono Tadahiro Goto

出版者

Society for Clinical Epidemiology

雑誌

Annals of Clinical Epidemiology (ISSN:24344338)

巻号頁・発行日

vol.4, no.3, pp.63-71, 2022 (Released:2022-07-01)

参考文献数

46

被引用文献数

5

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Machine learning refers to a series of processes in which a computer finds rules from a vast amount of data. With recent advances in computer technology and the availability of a wide variety of health data, machine learning has rapidly developed and been applied in medical research. Currently, there are three types of machine learning: supervised, unsupervised, and reinforcement learning. In medical research, supervised learning is commonly used for diagnoses and prognoses, while unsupervised learning is used for phenotyping a disease, and reinforcement learning for maximizing favorable results, such as optimization of total patients’ waiting time in the emergency department. The present article focuses on the concept and application of supervised learning in medicine, the most commonly used machine learning approach in medicine, and provides a brief explanation of four algorithms widely used for prediction (random forests, gradient-boosted decision tree, support vector machine, and neural network). Among these algorithms, the neural network has further developed into deep learning algorithms to solve more complex tasks. Along with simple classification problems, deep learning is commonly used to process medical imaging, such as retinal fundus photographs for diabetic retinopathy diagnosis. Although machine learning can bring new insights into medicine by processing a vast amount of data that are often beyond human capacity, algorithms can also fail when domain knowledge is neglected. The combination of algorithms and human cognitive ability is a key to the successful application of machine learning in medicine.