Patterns of Mental Health Service Utilisation: A Population-Based Linkage of Over 17 Years of Health Administrative Records.

A cross-sectoral partnership was formed in 2021 in support of the recommendations in an audit on access to state-funded mental health services. In this first paper, we aimed to describe the demographic and service utilisation of adults with a mental health diagnosis in the Western Australian state-funded health system from 2005 to 2021. Inpatient, emergency department, specialised (ambulatory) community mental health service, and death records were linked in individuals aged ≥ 18 years with a mental health diagnosis in Western Australia. Altogether, 392,238 individuals with at least one mental health service contact between 1st January 2005 and 31st December 2021 were included for analysis. Females, Aboriginal and/or Torres Strait Islander people, and those who lived outside major cities or in the most disadvantaged areas were more likely to access state-funded mental health services. While the number of individuals who accessed community mental health services increased over time (from 28,769 in 2005 to 50,690 in 2021), the percentage increase relative to 2005 was notably greater for emergency department attendances (127% for emergency department; 76% for community; and 63% for inpatient). Conditions that contributed to the increase for emergency department were mainly alcohol disorder, reaction to severe stress and adjustment disorders, and anxiety disorders. Sex differences were observed between conditions. The pattern of access increased for emergency department and the community plus emergency department combination. This study confirmed that the patterns of access of state-funded mental health services have changed markedly over time and the potential drivers underlying these changes warrant further investigation.

Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review.

Traumatic intracranial hypertension (tIH) is a common and potentially lethal complication of moderate to severe traumatic brain injury (m-sTBI). It often develops with little warning and is managed reactively with the tiered application of intracranial pressure (ICP)-lowering interventions administered in response to an ICP rising above a set threshold. For over 45 years, a variety of research groups have worked toward the development of technology to allow for the preemptive management of tIH in the hope of improving patient outcomes. In 2022, the first operationalizable tIH prediction system became a reality. With such a system, ICP lowering interventions could be administered prior to the rise in ICP, thus protecting the patient from potentially damaging tIH episodes and limiting the overall ICP burden experienced. In this review, we discuss related approaches to ICP forecasting and IH prediction algorithms, which collectively provide the foundation for the successful development of an operational tIH prediction system. We also discuss operationalization and the statistical assessment of tIH algorithms. This review will be of relevance to clinicians and researchers interested in development of this technology as well as those with a general interest in the bedside application of machine learning (ML) technology.

Applications of natural language processing at emergency department triage: A narrative review.

INTRODUCTION: Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data. METHODS: All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided. RESULTS: In total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice. CONCLUSION: Unstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.

The Monitoring with Advanced Sensors, Transmission and E-Resuscitation in Traumatic Brain Injury (MASTER-TBI) collaborative: bringing data science to the ICU bedside.

Background: With the adoption of multimodal neuromonitoring techniques, a large amount of high resolution neurophysiological data is generated during the treatment of patients with moderate to severe traumatic brain injury (m-sTBI) that is available for further analysis. The Monitoring with Advanced Sensors, Transmission and E-Resuscitation in Traumatic Brain Injury (MASTER-TBI) collaborative was formed in 2020 to facilitate analysis of these data. Objective: The MASTER-TBI collaborative curates m-sTBI patient data for the purposes of comparative effectiveness research, machine learning algorithm development, and neuropathophysiological phenomena analysis. Design, setting and participants: The MASTER-TBI collaborative is a multicentre longitudinal cohort study which utilises a novel hybrid cloud platform and other data science-informed techniques to collect and analyse data from patients with m-sTBI in whom both intracranial pressure monitoring and ICM+ (Cambridge Enterprise, Cambridge, UK) neuromonitoring software are utilised. MASTER-TBI enrols patients with m-sTBI from three participating Australian trauma intensive care units (ICUs). Main outcome measures: Captured outcome measures available for analysis include pathophysiological events (intracranial hypertension, cerebral perfusion pressure variations etc), surgical interventions, ICU and hospital length of stay, patient discharge status, and, where available, Glasgow Outcome Score-Extended (GOS-E) at 6 months. Results and conclusion: MASTER-TBI continues to develop data science-informed systems and techniques to maximise the use of captured high resolution m-sTBI patient neuromonitoring data. The highly innovative systems provide a world-class platform which aims to enhance the search for improved m-sTBI care and outcomes. This article provides an overview of the MASTER-TBI project's developed systems and techniques as well as a rationale for the approaches taken.

Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds.

Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. Clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in the lucibufagin class, compounds important for defense against predation. We aimed to 1) provide a workflow for hierarchically clustering MS/MS spectra for metabolomics data enabling users to cluster, visualise and easily interrogate the identification of underlying cluster ion profiles, and 2) use the workflow to identify key fragmentation patterns for lucibufagins in the hemolymph of P. pyralis. Features were aligned to their respective MS/MS spectra, then product ions were dynamically binned and resulting spectra were hierarchically clustered and grouped based on a cutoff distance threshold. Using the simplified visualization and the interrogation of cluster ion tables the number of lucibufagins was expanded from 17 to a total of 29.