Enhancing Breast Cancer Prediction with LLM-Inferred Confounders

A recent study highlights the challenges of estimating prediction uncertainty in active learning campaigns for materials discovery, indicating that uncalibrated out-of-distribution uncertainty quantification may hinder model performance. The research evaluates various uncertainty estimation methods using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network architectures, focusing on tasks related to solubility, bandgap, and formation energy predictions.

A new study introduces Upstream Probabilistic Meta-Imputation (UPMI) as a novel strategy for classifying pediatric pancreatitis, a complex inflammatory condition. This method leverages machine learning techniques to enhance diagnostic accuracy by utilizing a low-dimensional meta-feature space, addressing challenges posed by limited sample sizes and the intricacies of multimodal imaging.

A new study introduces the Extracted Morphological Feature Engineered (EMFE) pipeline, a lightweight machine learning approach for malaria classification that achieves performance levels comparable to deep learning models while requiring significantly less computational power. This method utilizes the NIH Malaria Cell Images dataset, focusing on simple cell morphology features such as non-background pixels and holes within cells.

A recent study utilized the METABRIC breast cancer cohort to explore the effectiveness of copula-based fusion of clinical and genomic machine learning risk scores for predicting 5-year cancer-specific mortality. By modeling the joint relationship between clinical variables and genomic data, researchers aimed to enhance risk stratification beyond traditional linear methods.

A new study has introduced a synthetic data generation framework aimed at identifying pre-injury patterns in triathletes by analyzing lifestyle, recovery, and load dynamics features. This framework generates physiologically plausible athlete profiles and simulates individualized training programs while considering factors such as sleep quality and stress levels, which are often overlooked in injury prediction models.

A recent study evaluated the association between pretreatment MRI descriptors and the pathologic complete response (pCR) of breast cancer patients undergoing neoadjuvant chemotherapy. The retrospective analysis included 129 patients treated between 2016 and 2020, revealing that 46% achieved pCR, with significant differences noted across breast cancer subtypes.

A recent study analyzed heart failure patient trajectories using sequence modeling, focusing on the performance of six sequence models, including Transformers and the newly introduced Mamba architecture, within a large Swedish cohort of 42,820 patients. The models were evaluated on their ability to predict clinical instability and other outcomes based on electronic health records (EHRs).

A recent study introduces a framework for feature selection in supervised tabular data using Kolmogorov-Arnold Networks (KANs), which utilize splines to derive feature importance scores. The research compares KAN-based selection criteria against traditional methods like LASSO and Random Forest, demonstrating competitive performance in classification and regression tasks across various datasets.