Estimation device, estimation system, estimation method and program for jet lag symptoms
A syndrome and jet lag technology, which is applied in the field of estimation devices for jet lag symptoms, can solve problems such as difficulty in objective determination or quantitative evaluation
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example 1
[0108] In the above description, the estimation standard EC is set to classify the feature quantities of the physiological indexes into two stages of the feature quantities of the "normal" sample Sa and the feature quantities of the "abnormal" sample Sa. However, the estimation standard EC may be set to classify the characteristic quantities of physiological indexes into three or more stages. Thus, the symptoms of subject Su are estimated in more detail than the two-stage classification. The following description will be made through an example in which the feature quantities of physiological indexes are classified into four stages based on sample information or statistical processing.
[0109] First, an example of classifying the feature quantities of physiological indexes into four stages based on sample information will be described. The sample information acquisition unit 14 acquires sample information indicating the degree of symptoms from each sample Sa using the above-...
example 2
[0113] Next, when it is difficult to classify feature amounts based on sample information, an example of classifying feature amounts of physiological indexes into four stages based on statistical processing will be described. Similar to the example based on sample information, the sample information acquisition unit 14 acquires sample information indicating the degree of symptoms from each sample Sa using the above-mentioned questionnaire. The standard setting unit 15 classifies the feature quantities of the physiological indexes in the plurality of samples Sa into two stages, ie, "normal" and "abnormal", based on the sample information.
[0114] The criterion setting unit 15 sets the estimation criterion EC using the feature quantity space for each physiological index based on the classification result of the feature quantity. First, a machine learning algorithm is used to specify the boundary B between the range Rn of the feature quantity of the "normal" sample Sa (the "norm...
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