Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression.

Background: Multimorbidity, the co-occurrence of two or more conditions within an individual, is a growing challenge for health and care delivery as well as for research. Combinations of physical and mental health conditions are highlighted as particularly important. Here, we investigated associations between physical multimorbidity and subsequent depression.

Comparing Care Pathways Between COVID-19 Pandemic Waves Using Electronic Health Records: A Process Mining Case Study.

Unlabelled: The COVID-19 pandemic caused rapid shifts in the workflow of many health services, but evidence of how this affected multidisciplinary care settings is limited. In this data study, we propose a process mining approach that utilises timestamped data from electronic health records to compare care provider patterns across pandemic waves. To investigate healthcare patterns during the pandemic, we collected routine events from Scottish hospital episodes in adults with COVID-19 status, generating treatment logs based on care provider input.

Predicting incident dementia in community-dwelling older adults using primary and secondary care data from electronic health records.

Predicting risk of future dementia is essential for primary prevention strategies, particularly in the era of novel immunotherapies. However, few studies have developed population-level prediction models using existing routine healthcare data. In this longitudinal retrospective cohort study, we predicted incident dementia using primary and secondary care health records at 5, 10 and 13 years in 144 113 Scottish older adults who were dementia-free prior to 1st April 2009.

Neurosymbolic AI for Reasoning Over Knowledge Graphs: A Survey.

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that combines symbolic reasoning methods with deep learning to leverage their complementary benefits. As knowledge graphs (KGs) are becoming a popular way to represent heterogeneous and multirelational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted.

Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models.

Introduction: Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults.

Methods: We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups.

Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.

Methods: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007.

Performance of models for predicting 1-year to 3-year mortality in older adults: a systematic review of externally validated models.

Mortality prediction models support identifying older adults with short life expectancy for whom clinical care might need modifications. We systematically reviewed external validations of mortality prediction models in older adults (ie, aged 65 years and older) with up to 3 years of follow-up. In March, 2023, we conducted a literature search resulting in 36 studies reporting 74 validations of 64 unique models.

Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability.

Background And Objective: Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and treatment response. Advances in machine learning have led to the development of clinical prognostic models, but due to the lack of model interpretability, integration into clinical practice is almost non-existent. In this retrospective study, we compare five classification models with varying degrees of interpretability for the prediction of brain tumour survival greater than one year following diagnosis.