Doknić Ilija, Mitrović Mirjana, Bukumirić Zoran, Virijević Marijana, Pantić Nikola, Sabljić Nikica, Antić Darko, Bojović Živko
Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
Clinic of Hematology, University Clinical Center of Serbia, 11000 Belgrade, Serbia.
Bioengineering (Basel). 2025 Jan 13;12(1):63. doi: 10.3390/bioengineering12010063.
Thrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombosis may carry inherent risks and complications. Consequently, the application of thrombosis prevention should be limited to patients with a reasonable risk of developing thrombosis. This thesis explores the potential of data science (DS) methods for predicting venous thrombosis in patients with acute myeloid leukemia. In order to ascertain which patients are at risk, statistical and machine-learning (ML) algorithms were employed to predict which patients with leukemia will develop thrombosis. Multilayer Perceptron (MLP) was found to be the best fit among the models evaluated, achieving the C statistic of 0.749. We examined which attributes are significant and what role they play in prediction and found six significant parameters: sex of the patient, prior history of thrombotic event, type of therapy, international normalized ratio (INR), Eastern Cooperative Oncology Group (ECOG) performance status, and Hematopoietic Cell Transplantation-specific Comorbidity. These findings suggest that subtle DS techniques can improve the prediction of Thrombosis in AML patients, thereby aiding in individual treatment planning.
血栓形成是癌症最常见的并发症之一，对发病率和死亡率有潜在影响，尤其是急性髓系白血病（AML）患者。因此，有效的血栓预防是癌症治疗的关键环节。然而，预防血栓的措施可能存在内在风险和并发症。因此，血栓预防的应用应仅限于有合理血栓形成风险的患者。本论文探讨了数据科学（DS）方法在预测急性髓系白血病患者静脉血栓形成方面的潜力。为了确定哪些患者有风险，采用了统计和机器学习（ML）算法来预测哪些白血病患者会发生血栓形成。在评估的模型中，多层感知器（MLP）被发现是最合适的，C统计量达到0.749。我们研究了哪些属性是重要的，以及它们在预测中起什么作用，发现了六个重要参数：患者性别、既往血栓形成事件史、治疗类型、国际标准化比值（INR）、东部肿瘤协作组（ECOG）体能状态以及造血细胞移植特异性合并症。这些发现表明，精细的数据科学技术可以改善AML患者血栓形成的预测，从而有助于个体化治疗规划。
