A case for developing domain-specific vocabularies for extracting suicide factors from healthcare notes

The onset and persistence of life events (LE) such as housing instability, job instability, and reduced social connection have been shown to increase risk of suicide. Predictive models for suicide risk have low sensitivity to many of these factors due to under-reporting in structured electronic health records (EHR) data. In this study, we show how natural language processing (NLP) can help identify LE in clinical notes at higher rates than reported medical codes. We compare domain-specific lexicons formulated from Unified Medical Language System (UMLS) selection, content analysis by subject matter experts (SME) and the Gravity Project, to data-driven expansion through contextual word embedding using Word2Vec. Our analysis covers EHR from the Veterans Affairs (VA) Corporate Data Warehouse (CDW) and measures the prevalence of LE across time for patients with known underlying cause of death in the National Death Index (NDI). We found that NLP methods had higher sensitivity of detecting LE relative to structured EHR (S-EHR) variables. We observed that, on average, suicide cases had higher rates of LE over time when compared to patients who died of non-suicide related causes with no previous history of diagnosed mental illness. When used to discriminate these outcomes, the inclusion of NLP derived variables increased the concentration of LE along the top 0.1%, 0.5% and 1% of predicted risk. LE were less informative when discriminating suicide death from non-suicide related death for patients with diagnosed mental illness

Switch 2.0: A Modern Platform for Planning High-Renewable Power Systems

This paper describes Switch 2.0, an open-source modeling platform for planning transitions to low-emission electric power grids, designed to satisfy 21st century grid planning requirements. Switch is capable of long-, medium- and short-term planning of investments and operations with conventional or smart grids, integrating large shares of renewable power, storage and/or demand response. Applications include integrated resource planning, investment planning, economic and policy analyses as well as basic research. Potential users include researchers, educators, industry and regulators. Switch formulates generation and transmission capacity planning as a mixed integer linear program where investment and operation are co-optimized across sampled time series during multiple investment periods. High-resolution production cost modeling is supported by freezing investment decisions and including longer time series and more operational details. Modeling features include unit commitment, part-load efficiency, planning and operating reserves, fuel supply curves, storage, hydroelectric networks, policy constraints and demand response. Switch has a modular architecture that allows users to flexibly compose models by choosing built-in modules 'a la carte' or writing custom modules. This paper describes the software architecture and model formulation of Switch 2.0 and provides a case study in which the model was used to identify the best options for obtaining load-shifting and reserve services from batteries and demand response in a 100% renewable power system

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders

Delivery of maternal health care in Indigenous primary care services: baseline data for an ongoing quality improvement initiative

Extent: 10p.BACKGROUND: Australia's Aboriginal and Torres Strait Islander (Indigenous) populations have disproportionately high rates of adverse perinatal outcomes relative to other Australians. Poorer access to good quality maternal health care is a key driver of this disparity. The aim of this study was to describe patterns of delivery of maternity care and service gaps in primary care services in Australian Indigenous communities. METHODS: We undertook a cross-sectional baseline audit for a quality improvement intervention. Medical records of 535 women from 34 Indigenous community health centres in five regions (Top End of Northern Territory 13, Central Australia 2, Far West New South Wales 6, Western Australia 9, and North Queensland 4) were audited. The main outcome measures included: adherence to recommended protocols and procedures in the antenatal and postnatal periods including: clinical, laboratory and ultrasound investigations; screening for gestational diabetes and Group B Streptococcus; brief intervention/advice on health-related behaviours and risks; and follow up of identified health problems. RESULTS: The proportion of women presenting for their first antenatal visit in the first trimester ranged from 34% to 49% between regions; consequently, documentation of care early in pregnancy was poor. Overall, documentation of routine antenatal investigations and brief interventions/advice regarding health behaviours varied, and generally indicated that these services were underutilised. For example, 46% of known smokers received smoking cessation advice/counselling; 52% of all women received antenatal education and 51% had investigation for gestational diabetes. Overall, there was relatively good documentation of follow up of identified problems related to hypertension or diabetes, with over 70% of identified women being referred to a GP/Obstetrician. CONCLUSION: Participating services had both strengths and weaknesses in the delivery of maternal health care. Increasing access to evidence-based screening and health information (most notably around smoking cessation) were consistently identified as opportunities for improvement across services.Alice R. Rumbold, Ross S. Bailie, Damin Si, Michelle C. Dowden, Catherine M. Kennedy, Rhonda J. Cox, Lynette O’Donoghue, Helen E. Liddle, Ru K. Kwedza, Sandra C. Thompson, Hugh P. Burke, Alex D. H. Brown, Tarun Weeramanthri and Christine M. Connor

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders

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