Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required li...
This study aimed to develop a machine learning (ML)-based model to identify risk factors for postoperative pain following video-assisted thoracoscopic surgery (VATS) lobectomy in non-small cell lung cancer (NSCLC) patients. This retrospective study a...
Identifying patients at high risk of an elevated lymph node ratio (LNR) is critical for optimizing the management of nasopharyngeal carcinoma (NPC), as LNR, defined as the ratio of metastatic to examined lymph nodes, serves as a key prognostic indica...
This study aimed to construct a personalized, machine learning-driven nomogram capable of predicting refractive outcomes following small incision lenticule extraction (SMILE). A total of 1253 eyes from 632 patients who underwent SMILE to correct myop...
Introduction: precision nutritional epidemiology studies require the development of initiatives to obtain objective population data that enable the implementation of health strategies. Therefore, the integration of health determinants and risk factor...
The immune microenvironment is a crucial component of colorectal carcinoma that has been well characterized, but much less is known about the immune microenvironment of colorectal carcinoma precursors. We hypothesized that T-cell infiltrates might di...
Tree-based scan statistics (TBSS) are data mining methods that screen thousands of hierarchically related health outcomes to detect unsuspected adverse drug effects. TBSS traditionally analyze claims data with outcomes defined via diagnosis codes. TB...
Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the heritability. We aimed to develop machine learning models, capturing non-additive and interactive ef...
Algorithmic estimations of dementia status are widely used in public health and epidemiologic research, but inadequate algorithm performance across racial/ethnic groups has been a barrier. We present improvements in the accuracy of group-specific "pr...
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