The state of health (SOH) of a Li-ion battery is a critical parameter that determines the remaining capacity and the remaining lifetime of the battery. In this paper, we propose SambaMixer a novel structured state space model (SSM) for predicting the state of health of Li-ion batteries. The proposed SSM is based on the MambaMixer architecture, which is designed to handle multi-variate time signals. We evaluate our model on the NASA battery discharge dataset and show that our model outperforms the state-of-the-art on this dataset. We further introduce a novel anchor-based resampling method which ensures time signals are of the expected length while also serving as augmentation technique. Finally, we condition prediction on the sample time and the cycle time difference using positional encodings to improve the performance of our model and to learn recuperation effects. Our results proof that our model is able to predict the SOH of Li-ion batteries with high accuracy and robustness.

The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening. Machine learning models can automate clinical consultations and auscultation, offering vital support in this area. However, the data involved, spanning demographics, medical history, symptoms, and respiratory audio, are heterogeneous and complex. Existing approaches are insufficient and lack generalizability, as they typically rely on limited training data, basic fusion techniques, and task-specific models. In this paper, we propose RespLLM, a novel multimodal large language model (LLM) framework that unifies text and audio representations for respiratory health prediction. RespLLM leverages the extensive prior knowledge of pretrained LLMs and enables effective audio-text fusion through cross-modal attentions. Instruction tuning is employed to integrate diverse data from multiple sources, ensuring generalizability and versatility of the model. Experiments on five real-world datasets demonstrate that RespLLM outperforms leading baselines by an average of 4.6% on trained tasks, 7.9% on unseen datasets, and facilitates zero-shot predictions for new tasks. Our work lays the foundation for multimodal models that can perceive, listen to, and understand heterogeneous data, paving the way for scalable respiratory health diagnosis.

Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being. There is a large, compelling body of evidence in population health studies showing that a wide range of SDOH is strongly correlated with health outcomes. Yet, a majority of the risk prediction models based on electronic health records (EHR) do not incorporate a comprehensive set of SDOH features as they are often noisy or simply unavailable. Our work links a publicly available EHR database, MIMIC-IV, to well-documented SDOH features. We investigate the impact of such features on common EHR prediction tasks across different patient populations. We find that community-level SDOH features do not improve model performance for a general patient population, but can improve data-limited model fairness for specific subpopulations. We also demonstrate that SDOH features are vital for conducting thorough audits of algorithmic biases beyond protective attributes. We hope the new integrated EHR-SDOH database will enable studies on the relationship between community health and individual outcomes and provide new benchmarks to study algorithmic biases beyond race, gender, and age.

In the latest of our Predictions series we take a look at what the future of healthcare will look like. Could artificial intelligence and smart technology improve every stage of our lives? We look at how the future of healthcare will affect us from birth including wearable tech and the internet of things to capturing baseline health data we can use to monitor our health as we grow older. As part of the government's plan to build a Britain fit for the future, £300 million will go towards developing the innovations and new technologies of tomorrow. From womb to tomb Health scanning and data will become ever present in our lives – even from the very start of life.

Accurately predicting the future health of batteries is necessaryElectrochemical batteries, such as lithium-ion and leadacid to ensure reliable operation, minimise maintenance cells, experience degradation over time and during costs, and calculate the value of energy storage investments.usage, leading to decreased energy storage capacity and The complex nature of degradation renders datadrivenincreased internal resistance. Being able to predict the approaches a promising alternative to mechanistic rate of degradation and the remaining useful life (RUL) modelling. This study predicts the changes in batteryof a battery is important for performance and economic capacity over time using a Bayesian nonparametric reasons. For example, in an electric vehicle, the driveable approach based on Gaussian process regression. These range is directly related to the battery capacity. For energy changes can be integrated against an arbitrary input sequence storage asset valuation, depreciation, warranty, insurance to predict capacity fade in a variety of usage scenarios, and preventative maintenance purposes, predicting forming a generalised health model. The approach RUL at design stage and during operation is crucial, and naturally incorporates varying current, voltage and temperaturethe investment case is strongly dependent on the degradation inputs, crucial for enabling real world application.

In the latest of our Predictions series we take a look at what the future of healthcare will look like. Could artificial intelligence and smart technology improve every stage of our lives? We look at how the future of healthcare will affect us from birth including wearable tech and the internet of things to capturing baseline health data we can use to monitor our health as we grow older. From womb to tomb Health scanning and data will become ever present in our lives – even from the very start of life. Before birth, scanning will take place in the womb which will create a basic profile of a person's health and create treatment plans from the very start.

The NHS could be applying machine learning-style processing to its patient, doctor and hospital data in an effort to improve efficiency within five years if plans by Google/DeepMind to push into the healthcare sector are approved. According to New Scientist, which has obtained a Memorandum of Understanding drawn up between DeepMind and the Royal Free NHS Trust in London, the two organisations are attempting to form a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximising the potential to work on genuinely innovative and transformative projects." Among the areas the project aims to touch on are making improvements in clinical outcomes and patient safety, and reducing costs throughout the organisation. The memo also sets out a long list of "areas of mutual interest" where the two organisations could work together over the next five years, including bed and demand management software, financial control products, private messaging and task management for junior doctors, and even real-time health prediction. In fact, health prediction has formed the basis of the first project between the two partners, with Google/DeepMind creating an app called Streams that aims to study healthcare data to try to identify patients at risk of deterioration, readmission or even death.