There were few studies of individual differences in prognostic decision-making from the psychological point of view; most of them focused on the differences between novices and experts making the prognoses. In this study, we suggested a new task that matched the criteria of a prognostic one, was computerized, and did not require expertise in any field of knowledge. Thus, the proposed method investigated how people processed information and controlled uncertainty in prognostic tasks. On a sample of 78 people aged 17-66, we used a quasi-experimental design to find the patterns of the proposed task parameters and how they correlated with personality and cognitive variables. Five well-known personality questionnaires accessing traits, known to be included in decision-making regulation, were used along with a cognitive abilities test to measure those variables. Two patterns were identified via cluster analysis. Differences in intolerance for uncertainty were demonstrated for the people from two identified clusters. Those patterns could be interpreted as uncertainty control strategies for decision-making grounding in prognostic tasks.
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http://dx.doi.org/10.3390/ejihpe10010016 | DOI Listing |
J Clin Exp Neuropsychol
January 2025
Department of Neurology, Medical University of South Carolina, Charleston, USA.
Objective: To examine neuropsychological characteristic differences between typical and atypical language dominance in adult persons with epilepsy (PWE) and mesial temporal sclerosis (MTS), including exploring the impact of selected clinical variables on detection of atypical language and neuropsychological performance.
Methods: Adults with intractable epilepsy and MTS ( = 39) underwent comprehensive, pre-surgical evaluation including fMRI and neuropsychological assessment. Participants with concordant lateralization of MTS and seizure onset were included.
Int J Mol Sci
December 2024
School of Computer Science and Technology, Xidian University, Xi'an 710126, China.
Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughput technology in the biomedical field has made it possible to obtain multi-omics data, whose integration can compensate for missing or unreliable information in a single data source. In this study, we integrated clinical data and two omics data, i.
View Article and Find Full Text PDFInt J Cardiovasc Imaging
January 2025
Shanxi Cardiovascular Hospital, 18 Yifen Street, Taiyuan, 030024, Shanxi, China.
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality.
View Article and Find Full Text PDFPathologica
December 2024
Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Italy.
The search for reliable prognostic markers in oral squamous cell carcinoma (OSCC) remains a critical need. Tumor-infiltrating lymphocytes (TILs), particularly T lymphocytes, play a pivotal role in the immune response against tumors and are strongly correlated with favorable prognoses. Computational pathology has proven highly effective for histopathological image analysis, automating tasks such as cell detection, classification, and segmentation.
View Article and Find Full Text PDFArXiv
December 2024
Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
Purpose: A reliable and comprehensive cancer prognosis model for clear cell renal cell carcinoma (ccRCC) could better assist in personalizing treatment. In this work, we developed a multi-modal ensemble model (MMEM) which integrates pretreatment clinical information, multi-omics data, and histopathology whole slide image (WSI) data to learn complementary information to predict overall survival (OS) and disease-free survival (DFS) for patients with ccRCC.
Methods And Materials: We collected 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC).
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