Inaugural Artificial Intelligence for Public Health Practice (AI4PHP) Retreat: Ontario, Canada

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group activities, case studies, and interspersed breaks for networking and reflection. There were 38 attendees from across Ontario, and a guest speaker from New York. Major themes that emerged from discussions included the need for greater attention to AI applications in public health given the potential benefits and enthusiasm; rigorous data collection, data quality, and data accessibility as a foundational factor that needs urgent attention; and the need for an equitable systems-thinking approach to AI amidst the breadth of public health functions, interventions, and population-based applications. Attendees expressed a desire for continued engagement and collaboration between public health practice and AI researchers

A multi-centre, randomised controlled trial of cognitive therapy to prevent harmful compliance with command hallucinations

<p>Abstract</p> <p>Background</p> <p>Command hallucinations are among the most distressing, high risk and treatment resistant symptoms for people with psychosis; however, currently, there are no evidence-based treatment options available for this group. A cognitive therapy grounded in the principles of the Social Rank Theory, is being evaluated in terms of its effectiveness in reducing harmful compliance with command hallucinations.</p> <p>Methods/Design</p> <p>This is a single blind, intention-to-treat, multi-centre, randomized controlled trial comparing Cognitive Therapy for Command Hallucinations + Treatment as Usual with Treatment as Usual alone. Eligible participants have to fulfil the following inclusion criteria: i) ≥16 years; ii) ICD-10 diagnosis of schizophrenia or related disorder; iii) command hallucinations for at least 6 months leading to risk of harm to self or others. Following the completion of baseline assessments, eligible participants will be randomly allocated to either the Cognitive Therapy for Command Hallucinations + Treatment as Usual group or the Treatment as Usual group. Outcome will be assessed at 9 and 18 months post randomization with assessors blind to treatment allocation. The primary outcome is compliance behaviour and secondary outcomes include beliefs about voices' power, distress, psychotic symptoms together with a health economic evaluation. Qualitative interviews with services users will explore the acceptability of Cognitive Therapy for Command Hallucinations.</p> <p>Discussion</p> <p>Cognitive behaviour therapy is recommended for people with psychosis; however, its focus and evaluation has primarily revolved around the reduction of psychotic symptoms. In this trial, however, the focus of the cognitive behavioural intervention is on individuals' appraisals, behaviour and affect and not necessarily symptoms; this is also reflected in the outcome measures used. If successful, the results will mark a significant breakthrough in the evidence base for service users and clinicians and will provide a treatment option for this group where none currently exist. The trial will open the way for further breakthrough work with the 'high risk' population of individuals with psychosis, which we would intend to pursue.</p> <p>Trial registration</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN62304114">ISRCTN62304114</a></p

Oscillatory Cortical Network Involved in Auditory Verbal Hallucinations in Schizophrenia

Auditory verbal hallucinations (AVH), a prominent symptom of schizophrenia, are often highly distressing for patients. Better understanding of the pathogenesis of hallucinations could increase therapeutic options. Magnetoencephalography (MEG) provides direct measures of neuronal activity and has an excellent temporal resolution, offering a unique opportunity to study AVH pathophysiology.Twelve patients (10 paranoid schizophrenia, 2 psychosis not otherwise specified) indicated the presence of AVH by button-press while lying in a MEG scanner. As a control condition, patients performed a self-paced button-press task. AVH-state and non-AVH state were contrasted in a region-of-interest (ROI) approach. In addition, the two seconds before AVH onset were contrasted with the two seconds after AVH onset to elucidate a possible triggering mechanism.AVH correlated with a decrease in beta-band power in the left temporal cortex. A decrease in alpha-band power was observed in the right inferior frontal gyrus. AVH onset was related to a decrease in theta-band power in the right hippocampus.These results suggest that AVH are triggered by a short aberration in the theta band in a memory-related structure, followed by activity in language areas accompanying the experience of AVH itself

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Automatic Classification of Railway Complaints using Machine Learning

People may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of issues on Twitter by sharing their ideas and interests. Sentiment analysis is a useful method for analysing such data, which involves identifying the sentiment of the source text and classifying it as positive, neutral, or negative. However, due to the vast amount of data, it can be challenging for businesses to address every customer’s question or complaint in a timely manner. Some issues may be urgent but delayed due to the volume of information. In order to prioritize emergency tweets, a system is proposed that utilizes machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes to identify tweets based on their urgency. The proposed system gathers and preprocesses unstructured data, performs feature extraction, trains, assesses and compares multiple machine learning models to determine the best classifier with the highest accuracy, and uses vectorization via a pipeline to determine the sentiment of a new tweet provided as input

Women’s health report for Belgium: addressing the information gap

Issue In the last years, Belgian policy makers have become increasingly concerned about health problems affecting only women (e.g. endometriosis) or affecting women in different ways than men (e.g. cardiovascular diseases). The current monitoring tools on the health status in Belgium routinely present information disaggregated by sex. Nevertheless, there is a need to go further than a mere comparison of men and women and strive for gender-sensitive health reporting. Description of the problem To address the information gap pertaining to the health of girls and women, a women&#8217;s health report for Belgium was developed. The goal of this report is to identify and highlight health issues specific to women or affecting them differently and possible knowledge and data gaps. Results The report highlighted several data gaps, e.g. prevalence of endometriosis and polycystic ovary syndrome, and several opportunities to fill them. The process also uncovered available but underused data on women-specific issues, including fertility treatments, abortions, and contraception. Among the main results, an analysis on girls (11-18 years old) showed an alarming difference in health status compared to boys, starting from a young age and increasing throughout adolescence. For example, girls reported experiencing more psychosomatic symptoms more often than boys with the difference increasing with age. Girls reported more often a negative perception of their health (22%) compared to boys (15%) and more often depressive symptoms (47%) than boys (31%). Conversely, boys were twice as likely to meet WHO recommendations on physical activity. Lessons This first report on women&#8217;s health in Belgium highlighted the need to collect better information on women-specific issues and the need to promote the use of existing data. Results showed that gender differences in health emerge and increase during adolescence. We strived to put results into context to produce knowledge and recommendations for policymakers. Key messages • Developing a women’s health report allowed to highlight data gaps and underuse of existing data. • Specific interventions should target teenage&nbsp;girls.</p

French version validation of the psychotic symptom rating scales (PSYRATS) for outpatients with persistent psychotic symptoms.

ABSTRACT: BACKGROUND: Most scales that assess the presence and severity of psychotic symptoms often measure a broad range of experiences and behaviours, something that restricts the detailed measurement of specific symptoms such as delusions or hallucinations. The Psychotic Symptom Rating Scales (PSYRATS) is a clinical assessment tool that focuses on the detailed measurement of these core symptoms. The goal of this study was to examine the psychometric properties of the French version of the PSYRATS. METHODS: A sample of 103 outpatients suffering from schizophrenia or schizoaffective disorders and presenting persistent psychotic symptoms over the previous three months was assessed using the PSYRATS. Seventy-five sample participants were also assessed with the Positive And Negative Syndrome Scale (PANSS). RESULTS: ICCs were superior to .90 for all items of the PSYRATS. Factor analysis replicated the factorial structure of the original version of the delusions scale. Similar to previous replications, the factor structure of the hallucinations scale was partially replicated. Convergent validity indicated that some specific PSYRATS items do not correlate with the PANSS delusions or hallucinations. The distress items of the PSYRATS are negatively correlated with the grandiosity scale of the PANSS. CONCLUSIONS: The results of this study are limited by the relatively small sample size as well as the selection of participants with persistent symptoms. The French version of the PSYRATS partially replicates previously published results. Differences in factor structure of the hallucinations scale might be explained by greater variability of its elements. The future development of the scale should take into account the presence of grandiosity in order to better capture details of the psychotic experience