A5068686133- Spaceflight effects on biology
- Sepsis Diagnosis and Treatment
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- Respiratory Support and Mechanisms
- Cardiac Arrest and Resuscitation
- Space Exploration and Technology
- Hemodynamic Monitoring and Therapy
- Cardiovascular and Diving-Related Complications
- High Altitude and Hypoxia
- Explainable Artificial Intelligence (XAI)
- Airway Management and Intubation Techniques
- Clinical Reasoning and Diagnostic Skills
- COVID-19 diagnosis using AI
- Anesthesia and Neurotoxicity Research
- Intensive Care Unit Cognitive Disorders
- Cardiac, Anesthesia and Surgical Outcomes
- Health Systems, Economic Evaluations, Quality of Life
- Advanced Causal Inference Techniques
- Radiomics and Machine Learning in Medical Imaging
- Telemedicine and Telehealth Implementation
- Machine Learning and Algorithms
- Medical History and Innovations
- Travel-related health issues
- COVID-19 epidemiological studies
Imperial College London
2016-2025
Imperial College Healthcare NHS Trust
2016-2024
European Astronaut Centre
2018-2023
Charing Cross Hospital
2016-2023
Bristol Royal Infirmary
2023
Kenya Medical Training College
2023
British Association of Dermatologists
2023
Delft University of Technology
2023
Intensive Care Society
2021
Centre Hospitalier de Bretagne Sud
2021
The use of artificial intelligence (AI) in a variety research fields is speeding up multiple digital revolutions, from shifting paradigms healthcare, precision medicine and wearable sensing, to public services education offered the masses around world, future cities made optimally efficient by autonomous driving. When revolution happens, consequences are not obvious straight away, date, there no uniformly adapted framework guide AI ensure sustainable societal transition. To answer this need,...
The trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration dynamic course in context applied therapeutic interventions.We included adult undergoing invasive mechanical ventilation (IMV) within 48 h intensive care unit (ICU) complete data until ICU death or discharge. We examined importance factors associated progression over first week,...
Sepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating septic patient highly challenging, because individual patients respond very differently to medical interventions there no universally agreed-upon treatment for sepsis. Understanding more about patient's physiological state at given time could hold the key effective policies. In this work, we propose new approach deduce optimal policies by using continuous state-space models...
Much attention has been devoted recently to the development of machine learning algorithms with goal improving treatment policies in healthcare. Reinforcement (RL) is a sub-field within that concerned how make sequences decisions so as optimize long-term effects. Already, RL have proposed identify decision-making strategies for mechanical ventilation, sepsis management and schizophrenia. However, before implementing learned by black-box high-stakes clinical decision problems, special care...
To investigate patients' characteristics, management, and outcomes in the critically ill population admitted to ICU for severe acute respiratory syndrome coronavirus disease 2019 pneumonia causing an distress syndrome.Retrospective case-control study.A 34-bed of a tertiary hospital.The first 44 patients were compared with historical control group 39 consecutive just before crisis.None.Obesity was most frequent comorbidity exhibited by (n = 32, 73% vs n 11, 28% controls; p < 0.001). Despite...
Whether critical care improvements over the last 10 years extend to all hospitals has not been described.
Abstract The influence of AI recommendations on physician behaviour remains poorly characterised. We assess how clinicians’ decisions may be influenced by additional information more broadly, and this can modified either the source (human peers or AI) presence absence an explanation (XAI, here using simple feature importance). used a between-subjects design where intensive care doctors ( N = 86) were presented computer for each 16 trials with patient case prompted to prescribe continuous...
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating septic patient highly challenging, because individual patients respond very differently to medical interventions there no universally agreed-upon treatment for sepsis. In this work, we propose an approach deduce policies by using continuous state-space models deep reinforcement learning. Our model learns clinically interpretable policies, similar important aspects the physicians....