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Comparing penalization methods for linear models on large observational health data.
J Am Med Inform Assoc
June 2024
Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands.
Objective: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation.
Materials And Methods: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases.
Variable selection for individualised treatment rules with discrete outcomes.
J R Stat Soc Ser C Appl Stat
March 2024
Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec H3A 0G4, Canada.
An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement.
View Article and Find Full Text PDFNovel Genomic Risk Stratification Model for Primary Gastrointestinal Stromal Tumors (GIST) in the Adjuvant Therapy Era.
Clin Cancer Res
October 2023
Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Purpose: Traditional risk stratification schemes in gastrointestinal stromal tumors (GIST) were defined in the pre-imatinib era and rely solely on clinicopathologic metrics. We hypothesize that genomic-based risk stratification is prognostically relevant in the current era of tyrosine kinase inhibitor (TKI) therapeutics.
Experimental Design: Comprehensive mutational and copy-number profiling using MSK-IMPACT was performed.
The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.
ArXiv
December 2024
Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.
View Article and Find Full Text PDFAI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules.
Chem Res Toxicol
July 2023
Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.
Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or and assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules.
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