21 research outputs found
Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction
Worldwide, many millions of people die suddenly and unexpectedly each year,
either with or without a prior history of cardiovascular disease. Such events
are sparse (once in a lifetime), many victims will not have had prior
investigations for cardiac disease and many different definitions of sudden
death exist. Accordingly, sudden death is hard to predict.
This analysis used NHS Electronic Health Records (EHRs) for people aged
50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010
(n = 380,000) to try to overcome these challenges. We investigated whether
medical history, blood tests, prescription of medicines, and hospitalisations
might, in combination, predict a heightened risk of sudden death.
We compared the performance of models trained to predict either sudden death
or all-cause mortality. We built six models for each outcome of interest: three
taken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three
of our own creation. We trained these using two different data representations:
a language-based representation, and a sparse temporal matrix.
We used global interpretability to understand the most important features of
each model, and compare how much agreement there was amongst models using Rank
Biased Overlap. It is challenging to account for correlated variables without
increasing the complexity of the interpretability technique. We overcame this
by clustering features into groups and comparing the most important groups for
each model. We found the agreement between models to be much higher when
accounting for correlated variables.
Our analysis emphasises the challenge of predicting sudden death and
emphasises the need for better understanding and interpretation of machine
learning models applied to healthcare applications
Improving ECG Classification Interpretability Using Saliency Maps
Cardiovascular disease is a large worldwide healthcare issue; symptoms often present suddenly with minimal warning. The electrocardiogram (ECG) is a fast, simple and reliable method of evaluating the health of the heart, by measuring electrical activity recorded through electrodes placed on the skin. ECGs often need to be analyzed by a cardiologist, taking time which could be spent on improving patient care and outcomes.Because of this, automatic ECG classification systems using machine learning have been proposed, which can learn complex interactions between ECG features and use this to detect abnormalities. However, algorithms built for this purpose often fail to generalize well to unseen data, reporting initially impressive results which drop dramatically when applied to new environments. Additionally, machine learning algorithms suffer a ‘black-box’ issue, in which it is difficult to determine how a decision has been made. This is vital for applications in healthcare, as clinicians need to be able to verify the process of evaluation in order to trust the algorithm.This paper proposes a method for visualizing model decisions across each class in the MIT-BIH arrhythmia dataset, using adapted saliency maps averaged across complete classes to determine what patterns are being learned. We do this by building two algorithms based on state-of-the-art models. This paper highlights how these maps can be used to find problems in the model which could be affecting generalizability and model performance. Comparing saliency maps across complete classes gives an overall impression of confounding variables or other biases in the model, unlike what would be highlighted when comparing saliency maps on an ECG-by-ECG basis
Facile synthesis of B/g-C3N4 composite materials for the continuous-flow selective photo-production of acetone
In this work versatile boron–carbon nitride composite materials were synthesized and utilized in a sustainable process using sunlight as the energy source for the continuous-flow selective photocatalytic production of acetone from 2-propanol. It is worth highlighting that the sample preparation was carried out
by an environmentally friendly strategy, without a solvent or additional reagents. Samples containing
boron in 1–10 wt% were subjected to physico-chemical characterization using XRD, porosimetry, UVvisible spectroscopy, TEM, energy-dispersive X-ray spectroscopy and XPS. The reaction output was analyzed on the basis of the reaction rate, selectivity and quantum efficiency of the process. A correlation
analysis between catalytic properties with two observables, the boron phase distribution in the materials
and charge handling efficiency (measured using photoluminescence), rationalizes photoactivity. Such an
analysis indicates that the presence of an amorphous boron metallic phase and its contact with the
carbon nitride component are key to setting up a renewable and easily scalable chemical process to
obtain acetone.MINECO (Spain)
ENE2016-77798-C4-1-RConsejo Superior de Investigaciones Cientificas (CSIC)Secretaria de Ciencia Tecnologia e Innovacion of CDMX (SECTEI, Mexico)MINECO
CTQ2016-78289-PEuropean Union (EU)RUDN University Program 5-10
Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study
Background: Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. // Methods: We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung's disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. // Findings: We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung's disease) from 264 hospitals (89 in high-income countries, 166 in middle-income countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in low-income countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. // Interpretation: Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between low-income, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030
Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study
Summary
Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally.
Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies
have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of
the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income
countries globally, and identified factors associated with mortality.
Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to
hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis,
exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a
minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical
status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary
intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause,
in-hospital mortality for all conditions combined and each condition individually, stratified by country income status.
We did a complete case analysis.
Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital
diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal
malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome
countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male.
Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3).
Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income
countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups).
Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome
countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries;
p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients
combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11],
p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20
[1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention
(ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety
checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed
(ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of
parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65
[0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality.
Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome,
middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will
be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger
than 5 years by 2030
Remote laser-speckle sensing of heart sounds for health assessment and biometric identification
The paper describes a contactless, machine-learning assisted method for heart-sound identification and quantification based on the remote measurement of the reflected laser speckle from the human neck skin surface. For more details see Readme.rt
Remote laser-speckle sensing of heart sounds for health assessment and biometric identification
Assessment of heart sounds is the cornerstone of cardiac examination, but it requires a stethoscope, skills and experience, and a direct contact with the patient. We developed a contactless, machine-learning assisted method for heart-sound identification and quantification based on the remote measurement of the reflected laser speckle from the neck skin surface in healthy individuals. We compare the performance of this method to standard digital stethoscope recordings on an example task of heart-beat sound biometric identification. We show that our method outperforms the stethoscope even allowing identification on the test data taken on different days. This method might allow development of devices for remote monitoring of cardiovascular health in different settings
SACRO:Semi-Automated Checking of Research Outputs
This project aimed to address a major bottleneck in conducting research on confidential data - the final stage of "Output Statistical Disclosure Control" (OSDC). This is where staff in a Trusted Research Environment (TRE) conduct manual checks to ensure that things a researcher wishes to take out - such as tables, plots, statistical and/or AI models- do not cause risk to any individual's privacy. To tackle this bottleneck, we proposed to:Produce a consolidated framework with a rigorous statistical basis that provides guidance for TREs to agree consistent, standard processes to assist in Quality Assurance.Design and implement a semi-automated system for checks on common research outputs, with increasing levels of support for other types such as AI.Work with a range of different types of TRE in different sectors and organisations to ensure wide applicability.Work with public and patients to explore what is needed for public trust, e.g., that any automation is acting as "an extra pair of eyes": supporting not supplanting TRE staff.Supported by funding from DARE UK (Data and Analytics Research Environments UK), we met these aims through production of documentation, open-source code repositories, and a 'Consensus' statement embodying principles organisations should uphold when deploying any sort of automated disclosure control.Looking forward, we are now ready for extensive user testing and refinement of the resources produced. Following a series of presentations to national and international audiences, a range of different organisations arein the process of trialling the SACRO toolkits. We are delighted that DARE UK has awarded funding to support a Community of Interest group (CoI). This will address ongoing support and the user-led creation of 'soft' resources (such as user guides, 'help desks', and mentoring schemes) to remove blocks to adoption: both for TREs, and crucially for researchers.There are two other areas where we are now ready to make significant advances: applying SACRO to allow principles-based OSDC for 'conceptual data spaces (e.g. via data pooling or federated analytics) and expanding the scope of risk assessment of AI/Machine Learning models to more complex models and types of data. This work is funded by UK research and Innovation, [Grant Number MC_PC_23006], as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK
SACRO:Semi-Automated Checking of Research Outputs
This project aimed to address a major bottleneck in conducting research on confidential data - the final stage of "Output Statistical Disclosure Control" (OSDC). This is where staff in a Trusted Research Environment (TRE) conduct manual checks to ensure that things a researcher wishes to take out - such as tables, plots, statistical and/or AI models- do not cause risk to any individual's privacy. To tackle this bottleneck, we proposed to:Produce a consolidated framework with a rigorous statistical basis that provides guidance for TREs to agree consistent, standard processes to assist in Quality Assurance.Design and implement a semi-automated system for checks on common research outputs, with increasing levels of support for other types such as AI.Work with a range of different types of TRE in different sectors and organisations to ensure wide applicability.Work with public and patients to explore what is needed for public trust, e.g., that any automation is acting as "an extra pair of eyes": supporting not supplanting TRE staff.Supported by funding from DARE UK (Data and Analytics Research Environments UK), we met these aims through production of documentation, open-source code repositories, and a 'Consensus' statement embodying principles organisations should uphold when deploying any sort of automated disclosure control.Looking forward, we are now ready for extensive user testing and refinement of the resources produced. Following a series of presentations to national and international audiences, a range of different organisations arein the process of trialling the SACRO toolkits. We are delighted that DARE UK has awarded funding to support a Community of Interest group (CoI). This will address ongoing support and the user-led creation of 'soft' resources (such as user guides, 'help desks', and mentoring schemes) to remove blocks to adoption: both for TREs, and crucially for researchers.There are two other areas where we are now ready to make significant advances: applying SACRO to allow principles-based OSDC for 'conceptual data spaces (e.g. via data pooling or federated analytics) and expanding the scope of risk assessment of AI/Machine Learning models to more complex models and types of data. This work is funded by UK research and Innovation, [Grant Number MC_PC_23006], as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK