A5064102556- Schizophrenia research and treatment
- Attention Deficit Hyperactivity Disorder
- Bipolar Disorder and Treatment
- Child and Adolescent Psychosocial and Emotional Development
- Treatment of Major Depression
- Tryptophan and brain disorders
- Mental Health Research Topics
- Autism Spectrum Disorder Research
- Sleep and related disorders
- Health Systems, Economic Evaluations, Quality of Life
- Child Nutrition and Feeding Issues
- Gambling Behavior and Treatments
- Mental Health Treatment and Access
- Genetics and Neurodevelopmental Disorders
- Mental Health and Psychiatry
- Circadian rhythm and melatonin
- Sleep and Wakefulness Research
- Pharmaceutical studies and practices
- Cardiac Health and Mental Health
- Obsessive-Compulsive Spectrum Disorders
- Eating Disorders and Behaviors
- Sepsis Diagnosis and Treatment
- Electroconvulsive Therapy Studies
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Inflammation biomarkers and pathways
University of Oxford
2017-2025
National Institute for Health Research
2025
Oxford BioMedica (United Kingdom)
2022-2024
Oxford Health NHS Foundation Trust
2017-2024
Warneford Hospital
2017-2024
National Health Service
2024
Bambino Gesù Children's Hospital
2012-2021
Istituti di Ricovero e Cura a Carattere Scientifico
2013-2021
Agenzia Regionale Parchi
2020-2021
Regione Lazio
2020
Background Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that information presented as free-form text and, therefore, requires time commitment clinicians to manually extract salient information. Natural language processing (NLP) methods be used automatically clinically relevant Objective Our aim use natural capture real-world data on...
Matching treatment to specific patients is too often a matter of trial and error, while efficacy should be optimised by limiting risks costs incorporating patients' preferences. Factors influencing an individual's drug response in major depressive disorder may include number clinical variables (such as previous treatments, severity illness, concomitant anxiety etc) well demographics (for instance, age, weight, social support family history). Our project, funded the National Institute Health...
Objective We summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care personalize antidepressant treatment adults with depression. This PETRUSHKA is part of trial (Personalize antidEpressant Treatment foR Unipolar depreSsion combining individual cHoices, risKs, big datA). Methods The tool: (a) based on prediction models, which use a combination advanced analytics, i.e., traditional statistics, machine...