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Measurement Uncertainty Evaluation of a Leukemia Recognition Algorithm Based on Patient Clinical Data
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摘要: 随着数字时代的到来，越来越多的算法主导着各种产业的发展和新型产业的兴起，以数字经济为主导的产业应运而生。计量作为测量及其应用的科学，正在加速步入数字时代，推动计量行业的数字化转型也成为计量工作的重中之重。因此，如何给出算法软件输出结果的准确性和可靠性的评价成为目前研究的热点。采用基于参考数据的数字计量方法，以患者临床检验数据的白血病识别算法软件的输出量为测量结果，建立白血病识别算法的评价模型，给出其测量不确定度评定的具体流程，计算该被测量的合成标准不确定度。Abstract: With the advent of the digital age, algorithms increasingly dominate the development of various industries and the emergence of new sectors, giving rise to a digital economy-led industrial landscape. Metrology, as the science of measurement and its application, is rapidly entering the digital era, with the promotion of digital transformation in the metrology industry becoming a top priority. Consequently, evaluating the accuracy and reliability of algorithmic software outputs has become a focal point of current research. This paper employs a digital metrology method based on reference data, using the output of a leukemia recognition algorithm software applied to patient clinical test data as the measurement result. It establishes an evaluation model for the leukemia recognition algorithm, outlines the specific process for assessing measurement uncertainty, and calculates the combined standard uncertainty of the measurand.
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Key words:
- metrology /
- digital transformation /
- reference data /
- digital metrology /
- algorithm software /
- algorithm uncertainty
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表 1 未患和患白血病数据的输入量的不确定度分量
Table 1. Uncertainty components of input data for non-leukemia and leukemia cases
维度 未患白血病数据 患白血病数据 $ {u}_{\mathrm{A}}\left({\bar{X}}_{i}\right) $ $ {u}_{\mathrm{B}}\left({\bar{X}}_{i}\right) $ $ {u}_{\mathrm{A}}\left({\bar{X}}_{i}\right) $ $ {u}_{\mathrm{B}}\left({\bar{X}}_{i}\right) $ SEX 0.0015 0.0380 0.0084 0.0446 AGE 0.0531 3.5528 0.3569 3.8150 RBC 0.0020 0.3878 0.0161 0.2872 MCV 0.0214 7.8101 0.1164 8.1927 PDW 0.0086 1.0544 0.0559 1.0582 WBC 0.0106 0.5933 0.5275 1.0647 NEUT% 0.0380 5.0399 0.3831 3.9555 LYMPH%% 0.0343 2.8046 0.3523 3.3618 EO% 0.0067 0.1603 0.0790 0.1579 BASO% 0.0011 0.0257 0.0329 0.0395 NEUT# 0.0082 0.3599 0.3029 0.4563 LYMPH# 0.0040 0.1798 0.3099 0.3837 BASO# 0.0002 0.0016 0.0209 0.0116 HGB 0.0634 11.4502 0.4978 8.9636 HCT 0.0002 0.0348 0.0014 0.0270 MCH 0.0085 2.5639 0.0426 2.7106 MCHC 0.0432 28.4066 0.2543 28.6660 R-CV 0.0063 1.1706 0.0514 1.3533 PLT 0.2567 19.8323 2.1301 14.4304 MPV 0.0034 0.8272 0.0246 0.8348 PCT 0.0002 0.0187 0.0020 0.0135 MONO# 0.0031 0.0413 0.2050 0.2012 MONO% 0.0096 0.6292 0.2537 1.1446 EO# 0.0006 0.0104 0.0092 0.0118 
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表 2 中间计算数据
Table 2. Intermediate calculation data
$ {Y}_{1} $ $ {Y}_{2} $ $ {Y}_{3} $ $ {Y}_{4} $ $ u\left({Y}_{1}\right) $ $ u\left({Y}_{2}\right) $ 89.28% 95.40% 95.10% 89.90% 0.07% 0.76%
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