| CPC G01N 15/1433 (2024.01) [G01N 15/1429 (2013.01); G01N 33/48 (2013.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)] | 18 Claims |

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1. A system comprising:
a cell image analysis apparatus comprising a stage on which a smear preparation, on which a blood sample is smeared, is set, a microscope, and a camera configured to capture images of cells, enlarged by the microscope, in the blood sample smeared on the smear preparation on the stage, the cell image analysis apparatus configured to analyze the images of the cells captured by the camera, and configured to output a first parameter regarding an abnormal finding in the captured cells based on analysis results of the images, wherein the captured cells are contained in the blood sample collected from a subject;
a blood cell counter configured to measure a red blood cell count, a white blood cell count, a platelet count, a hemoglobin concentration, a hematocrit value, red blood cell indices, and white blood cell classification values, the blood cell counter comprising a flow cytometer and an electric resistance-type detector, the blood cell counter configured to analyze at least optical signals from the cells detected by the flow cytometer, and configured to output a second parameter regarding a number of the detected cells based on analysis results of the optical signals, wherein the detected cells are contained in the blood sample; and
a computer comprising a processor and a memory storing a computer program, wherein the computer program, when executed by the computer, causes the computer to perform:
obtaining the first parameter from the cell image analysis apparatus and the second parameter from the blood cell counter;
generating, by using a pre-trained computer algorithm, information supporting disease differentiation of the subject, on the basis of the first parameter and the second parameter, wherein the information supporting disease differentiation includes a plurality of values each indicating a provability of each disease, and
outputting the plurality of values.
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