Artificial intelligence in health care: enabling informed care

We read with interest the Lancet Editorial on artificial intelligence (AI) in health care (Dec 23, 2017, p 2739).1 Deep learning as a form of AI risks being overhyped. Deep neural networks contain multiple layers of nodes connected by adjustable weights. Learning occurs by adjusting these weights until the desired input-to-output function is achieved.2 With many millions of weights, huge amounts of data are required for learning, a process facilitated by recent increases in computational power. However, the learning algorithm, known as the error back-propagation algorithm, was invented in the 1980s and has been used to train neural networks ever since. Two decades ago, our neural network system scored sleep and diagnosed sleep disorders.3 Our machine learning algorithm,4, 5 which now provides early warning of deterioration in many hospitals, was commercialised a decade ago.6 A key change occurred in the early 2000s. Since then, error back-propagation learns features directly from the input data, rather than relying on expert-selected features (eg, microaneurysms for a neural network assessing diabetic retinopathy). The first layers become implicit feature detectors. The success of deep learning has been shown mainly in problems with inputs of image (or image-like) data, as shown in medical image analysis,7, 8 speech recognition, and board game playing. Deep learning also lacks explanatory power; deep neural networks cannot explain how a diagnosis is reached and the features enabling discrimination are not easily identifiable. Clinicians should be aware of the capabilities as well as current limitations of AI. Properly integrated AI will improve patient outcomes and health-care efficiency. Augmented intelligence at the point of care is likely to precede AI without human involvement. LT and PW are supported by the Biomedical Research Centre, Oxford. Both authors have received funding from the National Institute for Health Research. The authors have developed an electronic observations application for which Drayson Health has purchased a sole licence. Drayson Health has a research agreement with the University of Oxford and has paid LT personal fees for consultancy as a member of its Strategic Advisory Board. Drayson Health might pay PW consultancy fees in the future

DySurv: Dynamic Deep Learning Model for Survival Prediction in the ICU

Survival analysis helps approximate underlying distributions of time-to-events which in the case of critical care like in the ICU can be a powerful tool for dynamic mortality risk prediction. Extending beyond the classical Cox model, deep learning techniques have been leveraged over the last years relaxing the many constraints of their counterparts from statistical methods. In this work, we propose a novel conditional variational autoencoder-based method called DySurv which uses a combination of static and time-series measurements from patient electronic health records in estimating risk of death dynamically in the ICU. DySurv has been tested on standard benchmarks where it outperforms most existing methods including other deep learning methods and we evaluate it on a real-world patient database from MIMIC-IV. The predictive capacity of DySurv is consistent and the survival estimates remain disentangled across different datasets supporting the idea that dynamic deep learning models based on conditional variational inference in multi-task cases can be robust models for survival analysis

Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity, potential biases, and the need for interpretability. To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial. Explainability is usually referred to as an AI system's ability to provide a robust interpretation of its decision-making logic or the decisions themselves to human stakeholders. In clinical risk prediction, other aspects of explainability like fairness, bias, trust, and transparency also represent important concepts beyond just interpretability. In this review, we address the relationship between these concepts as they are often used together or interchangeably. This review also discusses recent progress in developing explainable models for clinical risk prediction, highlighting the importance of quantitative and clinical evaluation and validation across multiple common modalities in clinical practice. It emphasizes the need for external validation and the combination of diverse interpretability methods to enhance trust and fairness. Adopting rigorous testing, such as using synthetic datasets with known generative factors, can further improve the reliability of explainability methods. Open access and code-sharing resources are essential for transparency and reproducibility, enabling the growth and trustworthiness of explainable research. While challenges exist, an end-to-end approach to explainability in clinical risk prediction, incorporating stakeholders from clinicians to developers, is essential for success

La cova d'en Passol i altres cavitats litorals situades entre cala sa Nau i cala Mitjana

[cat] Presentam la topografia i la descripció de quatre cavitats del litoral de Felanitx. Aquestes formacions endocàrstiques es localitzen a la plataforma tabular postorogènica del Llevant. Tres són de la tipologia anomenada coves de la zona de mescla costanera i almenys les més grans estan relacionades genèticament entre sí. L'altra és una cavitat d'abrasió marina de tipus túnel, però amb la peculiaritat que una de les boques es va taponar per una antiga d’una, cosa que fa difícil destriar el seu origen. La cavitat més destacable del treball és la cova d'en Passol, amb 2176 m de recorregut. Presenta tres zones subaquàtiques que totalitzen un recorregut de 1579 m, separades per una gran sala terrestre, que és en realitat un col·lapse. Una d'aquestes zones, el sifó des Somnis adquireix un elevat interès per la gran abundancia i bellesa dels espeleotemes i pel volum de les sales. La fondària màxima sota l’aigua és de 25 m. Es poden observar diversos paleonivells freàtics pleistocènics enregistrats als espeleotemes de les galeries subaquàtiques. Aquests espeleotemes s'han trobat des de la cota -8 m fins a la cota -23.[eng] We present the description and surveys of four coastal caves within the municipality of Felanitx. These karstic caves are located in a tabular platform Laid down after the Llevant mountain system orogeny. Three of them have origins associated with the coastal ground water mixing zone. The fourth is apparently a marine abrasion cave in the form of a tunnel with the particularity of having one of its entrances block by fossil dune, making it difficult to determine its precise origins. The most notable cave in this paper is cave cova d'en Passol, with a total run of 2176 m. It has three subaquatic sections with a total run of 1579, these being separated by a large chamber, whose present day form is largely due to periodical roof-collapse. One of these subaquatic sections is great interest owing to its large abundance and beauty of its speleothems as well as the size of its chambers. The maximum (reach ed) dive-depth is 25 m. In the submerged galleries it is possible to observe speleothems indicating phreatic paleo-levels from the Pleistocene. These speleothems are at depths between minus 8 and 23 m

Evaluating the effect of formulation on the uptake of a ZIKA subunit vaccine candidate by antigen-presenting cells

A major issue with vaccination for Zika, Dengue and other flaviviruses is the potential for antibody-dependent enhancement (ADE) of disease, caused by the generation or boosting of infection-enhancing antibodies. To address this concern, a subunit vaccine is being developed against the Zika virus using a modified version of the envelope protein as the antigen which has been modified with glycan residues to mask the fusion loop region of the protein (Figure 1), which is a cross-reactive and immunodominant site strongly implicated in the generation of antibodies capable of ADE. With this subunit vaccine approach, there is a need to formulate with an appropriate adjuvant to enhance the immunogenicity of the modified envelope protein. In this study we have evaluated a range of adjuvants using flow cytometry and fluorescence microscopy and have determined the relative uptake by human Antigen-presenting cells (APCs). Various combinations of clinically acceptable adjuvant materials: Alhydrogel®, 3D-(6-acyl) PHADTM (a synthetic analogue of MPL) and QS21, were tested using liposomal formulations. In addition, the modified Zika envelope protein was compared to that of wild type Zika antigen, similarly formulated. Please click Additional Files below to see the full abstract

La cova des Drac de cala Santanyí

[cat] En aquest article presentam la descripció, la topografia, l’estudi geomorfològic i l’estudi toponímic d'una cavitat costanera coneguda des d'antic, en la qual s'han trobat ara importants continuacions subaquàtiques. La cova s'obri a les calcarenites del Miocè superior postorogènic. El recorregut total de la cavitat és de 1005 m, del quals 766 m són subaquàtics. La màxima fondària sota l’aigua és de 24 m. Cal destacar la troballa d'espeleotemes freàtics relacionats amb els paleonivells plistocènics negatius de la mar Mediterrània. La situació batimètrica d'aquests espeleotemes abraça des dels -13'5 m fins a la cota -19 m.[eng] In this paper we present the description, survey, geomorphology and toponymy of a coastal cave that, in spite of being known about since ancient times, has some important subaquatic continuations. The cave lies in post-orogenic limestones from the Upper Miocene and has a total run of 1005 m, 766 m of which are subaquatic, with a maximum reached dive-depth of 24 m. It is worth mentioning the finding of phreatic speleothems related with Pleistocene and Mediterranean paleo-levels. These speleothems are found at bathymetric depths of between 13.5 m and 19 m