Machine learning and artificial intelligence in neuroscience: A primer for researchers

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: No data was used for the research described in the article.Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML

Regional variations in inpatient decompensated cirrhosis mortality may be associated with access to specialist care: results from a multicentre retrospective study

Introduction Specialist centres have been developed to deliver high-quality Hepatology care. However, there is geographical inequity in accessing these centres in the United Kingdom (UK). We aimed to assess the impact of these centres on decompensated cirrhosis patient outcomes and understand which patients transfer to specialist centres. Methods A UK multicentred retrospective observational study was performed including emergency admissions for patients with decompensated cirrhosis in November 2019. Admissions were grouped by specialist/non-specialist centre designation, National Health Service region and whether a transfer to a more specialist centre occurred or not. Univariable and multivariable comparisons were made. Results 1224 admissions (1168 patients) from 104 acute hospitals were included in this analysis. Patients at specialist centres were more likely to be managed by a Consultant Gastroenterologist/Hepatologist on a Gastroenterology/Hepatology ward. Only 24 patients were transferred to a more specialist centre. These patients were more likely to be admitted for gastrointestinal bleeding and were not using alcohol. Specialist centres eliminated regional variations in mortality which were present at non-specialist centres. Low specialist Consultant staffing numbers impacted mortality at non-specialist centres (aOR 2.15 (95% CI 1.18 to 4.07)) but not at specialist centres. Hospitals within areas of high prevalence of deprivation were more likely to have lower specialist Consultant staffing numbers. Conclusions Specialist Hepatology centres improve patient care and standardise outcomes for patients with decompensated cirrhosis. There is a need to support service development and care delivery at non-specialist centres. Formal referral pathways are required to ensure all patients receive access to specialist interventions