Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study.

Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods.

Objectives: Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals.

Background: Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK.

Methods: Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000-2019).

Adapting Random Forests to Predict Obesity-Associated Gene Expression.

Random forests (RFs) are effective at predicting gene expression from genotype data. However, a comparison of RF regressors and classifiers, including feature selection and encoding, has been under-explored in the context of gene expression prediction. Specifically, we examine the role of ordinal or one-hot encoding and of data balancing via oversam-pling in the prediction of obesity-associated gene expression.

Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling.

Objectives: Psoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous-only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models.

Methods: Genome-wide meta-analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC.

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models.

Ranking sets of morbidities using hypergraph centrality.

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph.

Cross-phenotype association mapping of the MHC identifies genetic variants that differentiate psoriatic arthritis from psoriasis.

Objectives: Psoriatic arthritis (PsA) is a chronic inflammatory arthritis, with a strong heritable component, affecting patients with psoriasis. Here we attempt to identify genetic variants within the major histocompatibility complex (MHC) that differentiate patients with PsA from patients with cutaneous psoriasis alone (PsC).

Methods: 2808 patients with PsC, 1945 patients with PsA and 8920 population controls were genotyped.