Mark Horowitz

The Maudsley® Deprescribing Guidelines Comprehensive resource describing guidelines for safely reducing or stopping (deprescribing) antidepressants, benzodiazepines, gabapentinoids and z-drugs for patients, including step-by-step guidance for all commonly used medications, covering common pitfalls, troubleshooting, supportive strategies, and more. Most formal guidance on psychiatric medication relates to starting or switching medications with minimal guidance on deprescribing medication. In 2023, the World Health Organisation and the United Nations called for patients, as a human right, to be informed of their right to discontinue treatment and to receive support to do so. The Maudsley Deprescribing Guidelines fills a significant gap in guidance for clinicians by providing comprehensive and authoritative information on this important aspect of treatment. This evidence-based handbook provides an overview of principles to be used in deprescribing. This is derived from fundamental scientific principles and the latest research on this topic, combined with emerging insights from clinical practice (including from patient experts). Building on the recognised brand of The Maudsley Prescribing Guidelines, and the prominence of the authors’ work, including in The Lancet Psychiatry on tapering antidepressants (the most read article across all Lancet titles when it was released). The Maudsley Deprescribing Guidelines covers topics such as: Why and when to deprescribe antidepressants, benzodiazepines, gabapentinoids and z-drugs Barriers and enablers to deprescribing including physical dependence, social circumstances, and knowledge about the discontinuation process Distinguishing withdrawal symptoms, such as poor mood, anxiety, insomnia, and a variety of physical symptoms from symptoms of the underlying disorder that medication was intended to treat The difference between physical dependence and addiction/substance use disorder Explanation of why and how to implement hyperbolic tapering in clinical practice Specific guidance on formulations of medication and techniques for making gradual reductions, including using liquid forms of medication, and other approachesStep-by-step guidance for safely stopping all commonly used antidepressants, benzodiazepines, gabapentinoids and z-drugs, including fast, moderate and slow tapering regimens or schedules for each drug, and guidance on how to tailor these to an individual Troubleshooting issues which can arise on stopping these medications, including akathisia, withdrawal symptoms, acute or protracted, and relapse. Written for anyone interested in safe deprescribing of psychiatric medications including psychiatrists, GPs, pharmacists, nurses, medical trainees, and interested members of the public. The Maudsley Deprescribing Guidelines is an essential resource on the subject that provides practical guidance on how to improve patient outcomes in this field of medicine.

Most emerging applications in imaging and machine learning must perform immense amounts of computation while holding to strict limits on energy and power. To meet these goals, architects are building increasingly specialized compute engines tailored for these specific tasks. The resulting computer systems are heterogeneous, containing multiple processing cores with wildly different execution models. Unfortunately, the cost of producing this specialized hardware—and the software to control it—is astronomical. Moreover, the task of porting algorithms to these heterogeneous machines typically requires that the algorithm be partitioned across the machine and rewritten for each specific architecture, which is time consuming and prone to error. Over the last several years, the authors have approached this problem using domain-specific languages (DSLs): high-level programming languages customized for specific domains, such as database manipulation, machine learning, or image processing. By giving up generality, these languages are able to provide high-level abstractions to the developer while producing high-performance output. The purpose of this book is to spur the adoption and the creation of domain-specific languages, especially for the task of creating hardware designs. In the first chapter, a short historical journey explains the forces driving computer architecture today. Chapter 2 describes the various methods for producing designs for accelerators, outlining the push for more abstraction and the tools that enable designers to work at a higher conceptual level. From there, Chapter 3 provides a brief introduction to image processing algorithms and hardware design patterns for implementing them. Chapters 4 and 5 describe and compare Darkroom and Halide, two domain-specific languages created for image processing that produce high-performance designs for both FPGAs and CPUs from the same source code, enabling rapid design cycles and quick porting of algorithms. The final section describes how the DSL approach also simplifies the problem of interfacing between application code and the accelerator by generating the driver stack in addition to the accelerator configuration. This book should serve as a useful introduction to domain-specialized computing for computer architecture students and as a primer on domain-specific languages and image processing hardware for those with more experience in the field.