基於人工智慧模型的細胞人口數據篩查血統疾病

Artificial Intelligence Based Models For Screening Of Hematologic Malignancies Using Cell Population Data

Firdani Rianda Putra(Firdani Rianda Putra)

指導教授 ： Shabbir Syed-Abdul ； Chia-Yu Su ； Hsuan-Chia Yang

共同指導教授 ： Anton Gradišek(Anton Gradišek)

臺北醫學大學/醫學科技學院/醫學資訊研究所/碩士(2020年)

摘要

Introduction: The Cell Population Data (CPD) generated from a modern blood analyzer can produce various blood cell parameters which can support the screening and diagnosis of many cellular abnormalities, including hematologic malignancies. CPD parameters provide the morphological and functional characteristics of the leukocytes which can be beneficial to help hematology experts in determining differential diagnosis. Recent advances Machine Learning (ML) technology offers the opportunity to deal with complex medical diagnostics fields using hematological parameters.
Objective: This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD parameters.
Method: CPD parameters were collected from 882 subjects (457 with hematologic malignancy and 425 with hematologic non-malignancy) at Konkuk University Medical Center, Seoul. Seven machine learning models, (i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN) were performed and model performance were evaluated using stratified 10-fold cross validation. The classification accuracy, precision, recall, and AUC were then compared.
Result: We obtained outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5%±2.6, respectively. Conclusion: ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.

關鍵字

Artificial Intelligence ； Machine Learning ； Hematologic Malignancies ； Cell Population Data ； Patient Screening

並列摘要

Introduction: The Cell Population Data (CPD) generated from a modern blood analyzer can produce various blood cell parameters which can support the screening and diagnosis of many cellular abnormalities, including hematologic malignancies. CPD parameters provide the morphological and functional characteristics of the leukocytes which can be beneficial to help hematology experts in determining differential diagnosis. Recent advances Machine Learning (ML) technology offers the opportunity to deal with complex medical diagnostics fields using hematological parameters.
Objective: This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD parameters.
Method: CPD parameters were collected from 882 subjects (457 with hematologic malignancy and 425 with hematologic non-malignancy) at Konkuk University Medical Center, Seoul. Seven machine learning models, (i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN) were performed and model performance were evaluated using stratified 10-fold cross validation. The classification accuracy, precision, recall, and AUC were then compared.
Result: We obtained outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5%±2.6, respectively. Conclusion: ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.

並列關鍵字

Artificial Intelligence ； Machine Learning ； Hematologic Malignancies ； Cell Population Data ； Patient Screening

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