Psychiatry Algorithms for Primary Care is a practical, quick reference guide to psychiatric assessment and mental healthcare in general practice. Providing algorithms informed by evidence-based guidelines, this easy-to-use resource helps busy medical and healthcare professionals quickly assess mental health problems, make informed treatment decisions, and understand when referrals to specialist mental health services are appropriate. Drawing from their extensive experience in general practice and psychiatry, the authors provide clear and authoritative guidance on a wide range of common psychiatric disorders, complex scenarios, and special considerations. Unique visual management algorithms define assessment, diagnosis, investigations and management for each condition, including Bipolar Affective Disorder, Psychosis, Depression, Dementia, and Attention Deficit Hyperactivity Disorder. Up-to-date information on medication choices and counselling strategies is found throughout the text. Designed for informing swift clinical decisions in demanding primary care settings, this indispensable reference guide: Conforms to the diagnostic criteria in the current edition of the World Health Organization’s International Classification of Diseases Contains algorithms informed by the Royal College of General Practitioners (RCGP), Royal College of Psychiatrists (RCPsych), and the National Institute for Health and Care Excellence (NICE) guidelines Explores common complaints that can suggest psychological or psychiatric disorders, such as insomnia and fatigue Outlines special mental health considerations related to children, intellectual disability, autism, the elderly, and pregnancy Includes appendices covering commonly prescribed drugs and physical examinations for patients with severe mental illness Features numerous self-assessment questions and links to online reference tools for General Practitioners Psychiatry Algorithms for Primary Care is a much-needed resource for medical students and trainees, physicians and healthcare professionals in general practice, nurse practitioners, and practitioners in other fields such as urgent care and emergency medicine.

Algorithms serve an important purpose in the field of psychopharmacology as heuristics for avoiding the biases and cognitive lapses that are common when prescribing for many conditions whose treatment is based on complex data. Unique in the field, this title compiles twelve papers from the Psychopharmacology Algorithm Project at the Harvard South Shore Psychiatry Residency Training Program and presents practical ways to adopt evidence-based practices into the day-to-day treatment of patients. Psychopharmacology Algorithms is a useful resource for practicing psychiatrists, residents, and fellows, as well as psychiatric nurse practitioners, psychiatric physician assistants who prescribe, advanced practice pharmacists who prescribe, and primary care clinicians. Teachers of psychopharmacology may find it particularly valuable. Researchers in clinical psychopharmacology may find it helpful in identifying important practice areas that are in need of further study.

From the leading psychiatry department in the world, comes the second edition of this unique, symptom-oriented approach to the diagnosis and treatment of psychiatric diseases. Features coverage of all the salient features of psychiatric diseases as well as new emphasis on evidence-based algorithms, psychopharmacological advances, and the pediatric patient.

Psychopharmacology is the study of drugs used to treat psychiatric disorders. This textbook looks at the use of clinical algorithms in relation to clinical psychopharmacology, especially the nature and current use of algorithms, and their future potential for the medical community.

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.