Silveraggerholm3357

Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from unstructured clinical notes in electronic health record (EHR) systems. Weakly supervised machine learning methods leverage imperfect labels for training, alleviating the burden of creating a large manually annotated dataset. After identifying a cohort of 600 patients at risk for suicidal ideation, we used a rule-based natural language processing approach (NLP) approach to label the training and validation notes (n = 17,978). Using this large corpus of clinical notes, we trained several statistical machine learning models-logistic classifier, support vector machines (SVM), Naive Bayes classifier-and one deep learning model, namely a text classification convolutional neural network (CNN), to be evaluated on a manually-reviewed test set (n = 837). The CNN model outperformed all other methods, achieving an overall accuracy of 94% and a F1-score of 0.82 on documents with "current" suicidal ideation. This algorithm correctly identified an additional 42 encounters and 9 patients indicative of suicidal ideation but missing a structured diagnosis code. When applied to a random subset of 5,000 clinical notes, the algorithm classified 0.46% (n = 23) for "current" suicidal ideation, of which 87% were truly indicative via manual review. Implementation of this approach for large-scale document screening may play an important role in point-of-care clinical information systems for targeted suicide prevention interventions and improve research on the pathways from ideation to attempt.

It is unclear if childhood trauma (CT) is an independent risk factor of adult chronic physical disorders or whether its impact is (also) due to underlying poorer mental health.

Data were obtained from baseline measurements among 13,489 respondents of the Netherlands Mental Health Survey and Incidence Study-1 and -2, cohort studies of the Dutch general population aged 18-64 years. We used a childhood trauma questionnaire measuring emotional, psychological, physical or sexual trauma before the age of 16. Lifetime mood, anxiety and substance use disorders were assessed with the Composite International Diagnostic Interview version 1.1 and 3.0. A standard self-report checklist was used to assess a broad range of chronic physical disorders treated by a medical doctor in the previous 12 months.

Respondents with a history of CT (N=4054) suffered significantly more often from digestive (OR 1.89-2.95), musculoskeletal (OR 1.21-1.75) and respiratory disorders (OR 1.39-1.91) and migraine (OR 1.42-1.66). We found indirect associations between CT and digestive, musculoskeletal and respiratory disorders through lifetime mood (54%, 52% and 48% respectively), anxiety (44%, 55% and 44% respectively) and substance use disorders (33%, 23% and 38% respectively). Mood (69%) and anxiety disorders (67%) also impacted the relationship with migraine.

CT predicts the development of adult physical disorders, even after controlling for sociodemographic and lifestyle factors. This association is substantially influenced by mental health disorders. Treatment programs for CT should include interventions aimed at enhancing both mental and physical health.

CT predicts the development of adult physical disorders, even after controlling for sociodemographic and lifestyle factors. This association is substantially influenced by mental health disorders. Treatment programs for CT should include interventions aimed at enhancing both mental and physical health.In this paper, using Lebanon's capital, Beirut, as a case study, a methodology is proposed to assess the potential for solar photovoltaics (PV) in urban areas incorporating both economic and non-economic factors. check details Utilizing a rich spatial dataset of solar irradiation augmented with electricity bills at the building level, the cost and benefit of installing rooftop PV systems for each building is estimated. Additionally, incentives and barriers for adopting those systems are investigated using a probabilistic choice model. The results show that Beirut city has a potential for distributed rooftop solar PV to be between 195 and 295 MWp. However, adoption rates are low at 0.49% and 1.23% for residential and commercial buildings, respectively, reflecting the limitation of financial incentives alone to promote the deployment of distributed renewable energy systems in transition economies. The impact of different incentive policy instruments and the role of solar PV in today's economic crisis in Lebanon is analyzed. The biggest impact was achieved through removing (or lowering) electricity tariff subsidy, although this option remains highly constrained by political calculus. We argue that the Lebanese government should fast-track and implement the required legal framework to facilitate and incentivize distributed power generation from renewable sources to promote both green energy and its financial resilience. The proposed modeling framework together with the results obtained in this study will have important implications for energy policy makers in Lebanon and other transition economies.Herein we report a successful degradation of highly concentrated formaldehyde (HCHO, 900 ppm less then ) effluent from a petrochemical industry using sono-catalytic reaction on highly porous (BET surface of 128 m2 g-1) copper iodide (CuI) nanocrystals as the adsorbent. In this regard, the designed experiments for optimization indicated that the ultrasonic wave (40 kHz) and mass of adsorbent (30 g/L) were significant in HCHO removal so that the combination of the adsorption under ultrasonic degradation resulted in approaching the eliminating efficiency of more than 99%. In this way, GC-MS analysis confirmed the CO2 production during HCHO degradation. Although the physisorption mechanism (-15.56 kJ/mol) limited the HCHO concentration (~100 ppm) for removal, addition of ultrasonic irradiation significantly improved the process to eliminate 986 ppm of HCHO from the real petrochemical effluent. Moreover, the mechanisms of HCHO decomposition were scrutinized through theoretical studies (density functional theory (DFT)), as well as thermodynamic and kinetics theories.