Making sense of violence risk predictions using clinical notes

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in psychiatric patients using such notes, with acceptable performance. However, they do not explain why classification works and how it can be improved. We explore two methods to better understand the quality of a classifier in the context of clinical note analysis: random forests using topic models, and choice of evaluation metric. These methods allow us to understand both our data and our methodology more profoundly, setting up the groundwork for improved models that build upon this understanding. This is particularly important when it comes to the generalizability of evaluated classifiers to new data, a trustworthiness problem that is of great interest due to the increased availability of new data in electronic format

Smoking, self-regulation and moral positioning: a focus group study with British smokers from a disadvantaged community

Smoking in many Western societies has become a both moral aand health issue in recent years, but little is known about how smokers position themselves and regulate their behaviour in this context. In this article, we report the findings from a study investigating how smokers from an economically disadvantaged community in the East Midlands (UK) respond to concerns about the health impact of smoking on others. We conducted ten focus group (FG) discussions with mixed groups (by smoking status and gender; N = 58 participants) covering a range of topics, including smoking norms, self-regulation, and smoking in diverse contexts. We transcribed all FG discussions before analysing the data using techniques from discourse anlysis. Smokers in general positioned themselves as socially responsible smokers and morally upstanding citizens. This position was bolstered in two main ways: ‘everyday accommodation', whereby everyday efforts to accommodate the needs of non-smokers were referenced, and ‘taking a stand', whereby proactive interventions to prevent smoking in (young) others were cited. We suggest that smoking cessation campaigns could usefully be informed by this ethic of care for others

Placing relationships in the foreground: the role of workplace friendships in engagement

We explore the role of workplace friendships as a lens for understanding the emotional element and relational context for personal engagement (Kahn, 1990). The review of engagement theory differentiates personal engagement, recognising the role emotions play in enabling individuals’ ‘preferred selves’. Workplace relationships and friendship provide a conceptual discussion of individuals in social and workplace roles in engagement, drawing on friendship, emotion, attachment theories, particularly Kahn’s work. A case study drawn from recent research illustrates our discussion before concluding with ideas for the development of a future research agenda in answer to recent calls for work on the social context of engagement

Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments

Pearl millet [Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) Morrone], is a staple food for over 90 million poor farmers in arid and semi-arid regions of sub-Saharan Africa and South Asia. We report the ~1.79 Gb genome sequence of reference genotype Tift 23D2B1-P1-P5, which contains an estimated 38,579 genes. Resequencing analysis of 994 (963 inbreds of the highly cross-pollinated cultigen, and 31 wild accessions) provides insights into population structure, genetic diversity, evolution and domestication history. In addition we demonstrated the use of re-sequence data for establishing marker trait associations, genomic selection and prediction of hybrid performance and defining heterotic pools. The genome wide variations and abiotic stress proteome data are useful resources for pearl millet improvement through deploying modern breeding tools for accelerating genetic gains in pearl millet.publishersversionPeer reviewe

Pleiotropy of genetic variants on obesity and smoking phenotypes: Results from the Oncoarray Project of The International Lung Cancer Consortium

Obesity and cigarette smoking are correlated through complex relationships. Common genetic causes may contribute to these correlations. In this study, we selected 241 loci potentially associated with body mass index (BMI) based on the Genetic Investigation of ANthropometric Traits (GIANT) consortium data and calculated a BMI genetic risk score (BMI-GRS) for 17,037 individuals of European descent from the Oncoarray Project of the International Lung Cancer Consortium (ILCCO). Smokers had a significantly higher BMI-GRS than never-smokers (p = 0.016 and 0.010 before and after adjustment for BMI, respectively). The BMI-GRS was also positively correlated with pack-years of smoking (p<0.001) in smokers. Based on causal network inference analyses, seven and five of 241 SNPs were classified to pleiotropic models for BMI/smoking status and BMI/pack-years, respectively. Among them, three and four SNPs associated with smoking status and pack-years (p<0.05), respectively, were followed up in the ever-smoking data of the Tobacco, Alcohol and Genetics (TAG) consortium. Among these seven candidate SNPs, one SNP (rs11030104, BDNF) achieved statistical significance after Bonferroni correction for multiple testing, and three suggestive SNPs (rs13021737, TMEM18; rs11583200, ELAVL4; and rs6990042, SGCZ) achieved a nominal statistical significance. Our results suggest that there is a common genetic component between BMI and smoking, and pleiotropy analysis can be useful to identify novel genetic loci of complex phenotypes

The Prevalence of Fetal Alcohol Spectrum Disorders in An American Indian Community.

The prevalence of fetal alcohol spectrum disorders (FASD) differs among populations and is largely unknown among minority populations. Prevalence and characterization of FASD is necessary for prevention efforts and allocation of resources for treatment and support. However, prevalence data are lacking, including among many minority populations. The aim of this study was to obtain an FASD prevalence estimate in a Southern California American Indian community employing active case-ascertainment. In 2016, American Indian children aged 5-7 years and their caregivers were recruited in collaboration with Southern California Tribal Health Clinic. Children were assessed using physical examinations and neurobehavioral testing. Parent or guardian interviews assessed child behavior and prenatal exposures including alcohol. Of 488 children identified as eligible to participate, 119 families consented and 94 completed assessments to allow a classification for FASD. Participating children (n = 94) were an average of 6.61 ± 0.91 years old and half were female. Most interviews were conducted with biological mothers (85.1%). Less than one third (29.8%) of mothers reported consuming any alcohol in pregnancy and 19.1% met study criteria for risky alcohol exposure prior to pregnancy recognition. Overall 20 children met criteria for FASD, resulting in an estimated minimum prevalence of 41.0 per 1000 (4.1%). No cases of fetal alcohol syndrome (FAS) were identified; 14 (70.0%) met criteria for alcohol related neuro- developmental disorder (ARND). Minimum prevalence estimates found in this sample are consistent with those noted in the general population

Making sense of violence risk predictions using clinical notes

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in psychiatric patients using such notes, with acceptable performance. However, they do not explain why classification works and how it can be improved. We explore two methods to better understand the quality of a classifier in the context of clinical note analysis: random forests using topic models, and choice of evaluation metric. These methods allow us to understand both our data and our methodology more profoundly, setting up the groundwork for improved models that build upon this understanding. This is particularly important when it comes to the generalizability of evaluated classifiers to new data, a trustworthiness problem that is of great interest due to the increased availability of new data in electronic format