Machine learning improves prediction of clinical outcomes for invasive breast cancers.
et al. Nat Med 2023 11
From the article: " A prognostic model for invasive breast cancer that is based on interpretable measurements of epithelial, stromal, and immune components outperforms histologic grading by expert pathologists. This model could improve clinical management of patients diagnosed with invasive breast cancer and address the concerns of pathologists about artificial intelligence (AI) trustworthiness by providing transparent and explainable predictions."
Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations.
Betzaida L Maldonado et al. J Community Genet 2023 11
From the abstract: "Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. "
A scoping review of artificial intelligence-based methods for diabetes risk prediction.
Farida Mohsen et al. NPJ Digit Med 2023 10 (1) 197
From the abstract: "The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. "
Power of inclusion: Enhancing polygenic prediction with admixed individuals.
Yosuke Tanigawa et al. Am J Hum Genet 2023 10
From the abstract: "Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data. "