Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

High-performance medicine: the convergence of human and artificial intelligence

This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.

The Answer Is 17 Years, What Is the Question

Background: Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus.
Objective: Our study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students.
Methods: We conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods.
Results: Of the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms.
Conclusions: Due to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students.

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Effects of COVID-19 on College Students' Mental Health in the United States: Interview Survey Study.

There are currently many tools available for capturing and defining the context of one's environment. Digital phenotyping, the use of technology and sensors to capture moment-to-moment behavior, has shown potential in quantifying the lived experience of mental illness and in the identification of individualized targets related to recovery. Environmental data suggests that greenspace may have a restorative capacity on mental health. In this paper, we explore the relationship of greenspace derived from geolocation with self-reported symptomatology from individuals with schizophrenia as well as healthy controls. Individuals with schizophrenia had less exposure to greenspace than controls, but their exposure demonstrated a dosage effect: high greenspace environments were associated with lower symptoms for anxiety (Cohen's d = -0.70), depression (d = -0.97), and psychosis (d = -0.94), whereas effect sizes for healthy controls were all negligible or small (d < 0.38). The notion that greenspace may have a more pronounced effect on individuals with mental illness presents both potential areas for recovery as well as implications for health care policy, especially in cities with a broad range of greenspace environments.

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Impact of dynamic greenspace exposure on symptomatology in individuals with schizophrenia.

The working alliance may be relevant in unguided smartphone-based interventions, but no validated measure exists. We evaluated the psychometric properties of the six-item Digital Working Alliance Inventory (DWAI) using a cross-sectional survey of meditation app users (n = 290) and the intervention arm of a randomized trial testing a smartphone-based meditation app (n = 314). Exploratory factor analysis suggested a single-factor solution which was replicated using longitudinal confirmatory factor analysis. The DWAI showed adequate internal consistency and test-retest reliability. Discriminant validity was supported by a lack of association with social desirability, psychological distress, and preference for a waitlist condition. Convergent validity was supported by positive associations with perceived app effectiveness and preference for an app condition. Supporting predictive validity, DWAI scores positively predicted self-reported and objective app utilization. When assessed at Weeks 3 or 4 of the intervention, but not earlier, DWAI scores predicted pre-post reductions in psychological distress.

Alliance With an Unguided Smartphone App: Validation of the Digital Working Alliance Inventory:

Research is increasing on the use of asynchronous technologies like e-mail, text, e-consultation, and asynchronous telepsychiatry by primary care and specialist clinicians. Clinicians need measurable skills, knowledge, and attitudes for these technologies to ensure quality care outcomes. This narrative review used a literature search of Medline, American Psychological Association PsycNET, PsycINFO, Embase, Cochrane, and Web of Science, using subject headings and keywords along with a manual search of reference lists of articles published by December 2019. Articles were included if they discussed and compared asynchronous technologies (e.g., asynchronous telepsychiatry), synchronous telepsychiatric (video including telemental and telebehavioral health), and in-person care. The review compares approaches, effectiveness, skills, and outcomes of these three modes of service delivery in behavioral health and primary care, since those clinicians interface with these technologies. Few studies have evaluated the efficacy and effectiveness of asynchronous technologies in behavioral, or compared them directly to synchronous and in-person care. Publications comparing synchronous telepsychiatry, asynchronous telepsychiatry, and other asynchronous technologies to in-person care focus on the technology used, how to do it, workflow processes, and medico-legal issues, but there is less written about clinical issues like the therapeutic frame, communication, boundaries, and trust. No studies specifically discuss behaviors or competencies for asynchronous care, though telepsychiatric, telebehavioral health, social media, and mobile health competencies have relevant elements. Users of asynchronous technologies (i.e., patients, primary care teams, and psychiatrists) need to adjust clinical skills, work as team members, and integrate technological and administrative factors into workflow. Implications for clinicians, trainees, faculty, administrators, and institutions are discussed. Asynchronous technologies improve access, reduce costs, and compliment other care options. Health systems must appraise how to help individuals and interprofessional participants best interface with a wide range of technologies. This requires adjustments in clinical and administrative workflow. Research in measurable competency sets, implementation, and outcomes is needed.

John Torous

Papers

Impact of dynamic greenspace exposure on symptomatology in individuals with schizophrenia.

Alliance With an Unguided Smartphone App: Validation of the Digital Working Alliance Inventory:

A comparison of In-Person, Synchronous and Asynchronous Telepsychiatry: Skills/Competencies, Teamwork, and Administrative Workflow

Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care.

Mental Health Apps: What to Tell Patients: An Evaluation Model Created Specifically for Such Apps Can Help Guide Your Discussions