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- W2886096952 abstract "The prevalence of wearable devices and smart phones, together with increasingly sophisticated computational approaches such as machine learning, can make extraordinarily large data sets available to psychiatrists, caregivers, and researchers. Beyond merely documenting compliance and representing methods of communicating with patients, they hold the promise of being able to identify antecedents with meaningful advance notice—minutes, hours, days—that allow patients to modify their behavior, or caregivers to intervene. Although important ethical and validation concerns remain, the ability to predict future behaviors on the basis of objective data would be transformative for conditions such as addiction, depression, and psychosis, where support systems are often effective in preventing catastrophic outcomes such as overdose and suicide. Areas of active research include understanding the ways in which behavioral events can predict one another, and the corresponding timescales for which the predictions are meaningful. Alignment of end points in humans and laboratory animals should be prioritized so that translationally relevant working hypotheses can be developed, tested, and validated, thereby ushering in a new era of more effective diagnoses, treatment, and prevention of psychiatric illness while eliminating the types of anthropomorphic interpretation of rudimentary behavior that promote broad skepticism regarding the utility of animal models. The application of new technologies to psychiatry and neuroscience has already enabled appreciation of cross-species homologies in neuroanatomy, neurophysiology, and neurogenetics. We are entering an era in which establishing the corresponding existence of behavioral homologies could have transformative impact on our ability to diagnose, treat, and prevent psychiatric illness." @default.
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- W2886096952 date "2018-08-02" @default.
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- W2886096952 title "Digital devices and continuous telemetry: opportunities for aligning psychiatry and neuroscience" @default.
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- W2886096952 doi "https://doi.org/10.1038/s41386-018-0172-z" @default.
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