121 research outputs found
Artificial intelligence in pediatric allergy research
Get PDFAtopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.• Pediatric allergies are heterogeneous and common, inflicting substantial morbidity and societal costs. • The field of artificial intelligence is undergoing rapid development, with increasing implementation in various fields of medicine and research.• Promising applications of AI in pediatric allergy have been reported, but implementation largely lags behind other fields, particularly in regard to use of advanced algorithms and non-tabular data. Furthermore, lacking reporting on computational approaches hampers evidence synthesis and critical appraisal. • Multi-center collaborations with multi-omics and rich unstructured data as well as utilization of deep learning algorithms are lacking and will likely provide the most impactful discoveries
sj-pdf-1-jrs-10.1177_01410768221095239 - Supplemental material for Impact on emergency and elective hospital-based care in Scotland over the first 12 months of the pandemic: interrupted time-series analysis of national lockdowns
No full textSupplemental material, sj-pdf-1-jrs-10.1177_01410768221095239 for Impact on emergency and elective hospital-based care in Scotland over the first 12 months of the pandemic: interrupted time-series analysis of national lockdowns by Syed Ahmar Shah, Rachel H Mulholland, Samantha Wilkinson, Srinivasa Vittal Katikireddi, Jiafeng Pan, Ting Shi, Steven Kerr, Uktarsh Agrawal, Igor Rudan, Colin R Simpson, Sarah J Stock, John Macleod, Josephine-LK Murray, Colin McCowan, Lewis Ritchie, Mark Woolhouse and Aziz Sheikh in Royal Society of Medicine</p
Towards Augmenting Mental Health Personnel With LLM Technology To Provide More Personalized And Measurable Treatment Goals for Patients with Severe Mental Illness
Get PDFMobile health (mHealth) tools are increasingly being used in various mental health domains to monitor patients with Severe Mental Illnesses (SMI), with the aim of potentially increasing patient engagement with their treatment. Patients with SMI who are prescribed Flexible Assertive Community Treatment (FACT) create a treatment plan together with their case manager, which serves as the leading document describing the goals that will be worked on during treatment. In order to incorporate the treatment plan goals of a patient in an mHealth application, the treatment plan goals need to be measurable. However, in previous work, we discovered that on average, only 25% of the available treatment plans include measurable goals. We have developed a protocol for making measurable goals with patients with SMI to address this issue. However, we anticipate low adoption of the protocol due to the potentially time-consuming nature of the steps involved. To mitigate this, we are exploring the use of AI to generate measurable treatment plan goals for patients with SMI and introduce a new workflow. In our exploratory study, we created a prototype of a system that may enable case managers and patients with SMI to generate measurable treatment plan goals using Large Language Models
Dozzz:Exploring Voice-Based Sleep Experience Sampling for Children
Get PDFText-based digital diaries are an essential tool for sleep clinicians to assess how their patients experience sleep. However, text-entry can be challenging for children. Voice entry represents a plausible and yet unexplored alternative for supporting children’s self-report in sleep diaries. We introduce Dozzz, a voice-based digital sleep diary that empowers children to record their sleep experiences using a smartphone. We present the result of usability evaluation involving ten children aged six to twelve. This evaluation confirmed that children were able to understand and interact with Dozzz effectively. Our study demonstrates the feasibility of voice-user interfaces (VUIs) to support sleep diaries for children. Future work needs to assess the use of diaries in real-life settings and evaluate the quality of responses children provide when using the system independently at home.</p
Automated Decision-Making Systems in Precision Medicine – The Right to Good Administration at Risk
No full textAAMOS-00 Study: Predicting Asthma Attacks Using Connected Mobile Devices and Machine Learning
No full textMonitoring asthma condition is essential to asthma self-management. However, traditional methods of monitoring require high levels of active engagement and patients may regard this level of monitoring as tedious. Passive monitoring with mobile health devices, especially when combined with machine learning, provides an avenue to dramatically reduce management burden. However, data for developing machine learning algorithms are scarce, and gathering new data is expensive. A few asthma mHealth datasets are publicly available, but lack objective and passively collected data which may enhance asthma attack prediction systems. To fill this gap, we carried out the 2-phase, 7-month AAMOS-00 observational study to collect data about asthma status using three smart monitoring devices (smart peak flow meter, smart inhaler, smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air quality reports, we have collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. Conducting phase 2 of device monitoring over 12 months, from June 2021 to June 2022 and during the COVID-19 pandemic, 22 participants across the UK provided 2,054 unique patient-days of data. This valuable anonymised dataset has been made publicly available with the consent of participants. Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor was ACCORD, the University of Edinburgh. The anonymised dataset was produced with statistical advice from Aryelly Rodriguez - Statistician, Edinburgh Clinical Trials Unit, University of Edinburgh. Protocol: "Predicting asthma attacks using connected mobile devices and machine learning; the AAMOS-00 observational study protocol" - BMJ Open, DOI: 10.1136/bmjopen-2022-064166Tsang, Kevin CH; Pinnock, Hilary; Wilson, Andrew M; Salvi, Dario; Shah, Syed Ahmar. (2022). AAMOS-00 Study: Predicting Asthma Attacks Using Connected Mobile Devices and Machine Learning, 2021-2022 [dataset]. University of Edinburgh. Edinburgh Medical School. Usher Institute. https://doi.org/10.7488/ds/3775
Vital sign monitoring and data fusion for paediatric triage
Get PDFAccurate assessment of a child’s health is critical for appropriate allocation of medical resources and timely delivery of healthcare in both primary care (GP consultations) and secondary care (ED consultations). Serious illnesses such as meningitis and pneumonia account for 20% of deaths in childhood and require early recognition and treatment in order to maximize the chances of survival of affected children. Due to time constraints, poorly defined normal ranges, difficulty in achieving accurate readings and the difficulties faced by clinicians in interpreting combinations of vital signs, vital signs are rarely measured in primary care and their utility is limited in emergency departments. This thesis aims to develop a monitoring and data fusion system, to be used in both primary care and emergency department settings during the initial assessment of children suspected of having a serious infection. The proposed system relies on the photoplethysmogram (PPG) which is routinely recorded in different clinical settings with a pulse oximeter using a small finger probe. The most difficult vital sign to measure accurately is respiratory rate which has been found to be predictive of serious infection. An automated method is developed to estimate the respiratory rate from the PPG waveform using both the amplitude modulation caused by changes in thoracic pressure during the respiratory cycle and the phenomenon of respiratory sinus arrhythmia, the heart rate variability associated with respiration. The performance of such automated methods deteriorates when monitoring children as a result of frequent motion artefact. A method is developed that automatically identifies high-quality PPG segments mitigating the effects of motion on the estimation of respiratory rate. In the final part of the thesis, the four vital signs (heart rate, temperature, oxygen saturation and respiratory rate) are combined using a probabilistic framework to provide a novelty score for ranking various diagnostic groups, and predicting the severity of infection in two independent data sets from two different clinical settings
Is poor asthma self-management due to failure to recognise symptoms or failure to act? Novel insights from mining large-scale clinical observational study
No full textVital sign monitoring and data fusion for paediatric triage
No full textAccurate assessment of a child’s health is critical for appropriate allocation of medical resources and timely delivery of healthcare in both primary care (GP consultations) and secondary care (ED consultations). Serious illnesses such as meningitis and pneumonia account for 20% of deaths in childhood and require early recognition and treatment in order to maximize the chances of survival of affected children. Due to time constraints, poorly defined normal ranges, difficulty in achieving accurate readings and the difficulties faced by clinicians in interpreting combinations of vital signs, vital signs are rarely measured in primary care and their utility is limited in emergency departments.
This thesis aims to develop a monitoring and data fusion system, to be used in both primary care and emergency department settings during the initial assessment of children suspected of having a serious infection. The proposed system relies on the photoplethysmogram (PPG) which is routinely recorded in different clinical settings with a pulse oximeter using a small finger probe. The most difficult vital sign to measure accurately is respiratory rate which has been found to be predictive of serious infection. An automated method is developed to estimate the respiratory rate from the PPG waveform using both the amplitude modulation caused by changes in thoracic pressure during the respiratory cycle and the phenomenon of respiratory sinus arrhythmia, the heart rate variability associated with respiration. The performance of such automated methods deteriorates when monitoring children as a result of frequent motion artefact. A method is developed that automatically identifies high-quality PPG segments mitigating the effects of motion on the estimation of respiratory rate.
In the final part of the thesis, the four vital signs (heart rate, temperature, oxygen saturation and respiratory rate) are combined using a probabilistic framework to provide a novelty score for ranking various diagnostic groups, and predicting the severity of infection in two independent data sets from two different clinical settings.This thesis is not currently available in ORA
Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort
No full textIntroduction Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.Methods and analysis Current prognostic tools use logistic regression to develop a risk scoring model for asthma attacks. We propose to build on this by systematically applying various well-known machine learning techniques to a large longitudinal deidentified primary care database, the Optimum Patient Care Research Database, and comparatively evaluate their performance with the existing logistic regression model and against each other. Machine learning algorithms vary in their predictive abilities based on the dataset and the approach to analysis employed. We will undertake feature selection, classification (both one-class and two-class classifiers) and performance evaluation. Patients who have had actively treated clinician-diagnosed asthma, aged 8–80 years and with 3 years of continuous data, from 2016 to 2018, will be selected. Risk factors will be obtained from the first year, while the next 2 years will form the outcome period, in which the primary endpoint will be the occurrence of an asthma attack.Ethics and dissemination We have obtained approval from OPCRD’s Anonymous Data Ethics Protocols and Transparency (ADEPT) Committee. We will seek ethics approval from The University of Edinburgh’s Research Ethics Group (UREG). We aim to present our findings at scientific conferences and in peer-reviewed journals
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