A baby with a rare neurological disorder, airlifted to hospital after collapsing in a supermarket, is not out of the woods yet, said his father. Seven-month-old Rupert Smith, from Broughton, Flintshire, stopped breathing in a Tesco store in Broughton Park, on Monday. His mother Siobhan, 35, immediately called for help and administered CPR before emergency services, including paramedics, police and an air ambulance arrived. Rupert, who has a disorder called alternating hemiplegia of childhood (AHC), was flown to Alder Hey Children's Hospital in Liverpool for treatment. Dad Dave Smith said Rupert had continued to have quite significant seizures [in hospital] so they have been giving him medication and he has undergone various different tests.

We expect our doctors to be demi-gods – flawless, tireless, always right. But they are only human. Increasingly, they are stretched thin, working long hours, under immense pressure, and often with limited resources. Of course, better conditions would help, including more staff and improved systems. But even in the best-funded clinics with the most committed professionals, standards can still fall short; doctors, like the rest of us, are working with stone age minds.

Artificial Intelligence has revolutionised critical care for common conditions. Yet, rare conditions in the intensive care unit (ICU), including recognised rare diseases and low-prevalence conditions in the ICU, remain underserved due to data scarcity and intra-condition heterogeneity. To bridge such gaps, we developed KnowRare, a domain adaptation-based deep learning framework for predicting clinical outcomes for rare conditions in the ICU. KnowRare mitigates data scarcity by initially learning condition-agnostic representations from diverse electronic health records through self-supervised pre-training. It addresses intra-condition heterogeneity by selectively adapting knowledge from clinically similar conditions with a developed condition knowledge graph. Evaluated on two ICU datasets across five clinical prediction tasks (90-day mortality, 30-day readmission, ICU mortality, remaining length of stay, and phenotyping), KnowRare consistently outperformed existing state-of-the-art models. Additionally, KnowRare demonstrated superior predictive performance compared to established ICU scoring systems, including APACHE IV and IV-a. Case studies further demonstrated KnowRare's flexibility in adapting its parameters to accommodate dataset-specific and task-specific characteristics, its generalisation to common conditions under limited data scenarios, and its rationality in selecting source conditions. These findings highlight KnowRare's potential as a robust and practical solution for supporting clinical decision-making and improving care for rare conditions in the ICU.

Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at all available, historical data often form a heterogeneous time series due to high probability of other changes over long periods of time (e.g., road works, introduced traffic calming initiatives, etc.). This is especially prominent in cities and suburban areas. We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions. We show increased performance for travel time prediction over different baselines when utilizing the vector-space encoding for representing the temporal setting.

When we were in school and were given a problem to solve, we usually stopped working on the problem as soon as we found the answer and we recorded that answer on our paper. This might be a fair approach for elementary school assignments, but that approach is not good in higher education or in life. Unfortunately, many people continue this learned behavior into adulthood, at the university and/or on their jobs. Consequently, these people miss new opportunities for learning, discovery, recognition, and advancement. In data science, we are trained to keep searching (at least, I hope that this is true for all of us) even after we find that first model from our data that appears to answer our business question accurately.

The healthcare industry is increasingly focusing on niche patient populations. Around half of FDA approvals in the past two years were for rare or orphan drugs that serve fewer than 200,000 patients in total in the US and 1 in 2,000 patients in Europe. By 2024, orphan drug sales are expected to capture one-fifth of worldwide prescription sales. However, finding these hard-to-reach patients is difficult and keeping them engaged over time even more so. Could machine learning platforms that deliver personalized experiences for patients and caregivers be part of the answer?

We have all observed how great pioneers of paediatric neurology effortlessly drew on their vast reserves of knowledge and experience to make diagnoses based primarily on observation. In today's environment, individual clinicians see fewer cases, while medical science uncovers increasing numbers of often rare conditions. Artificial intelligence (AI), machine learning, and big data promise to emulate, permanently represent, and make widely available the fruits of clinical experience. The concept of big data is not new: petabyte (1015 bytes)‐sized resources have informed particle physics, climate science, and genomics for decades.1 One may ask how big data can benefit a field increasingly dominated by rare diseases. It is axiomatic that genomic data is uninterpretable without phenotypic information, and readers will be familiar with SimulConsult (https://simulconsult.com), a machine learning‐based platform to assist the diagnosis of rare conditions in child neurology, sometimes with unexpected results.2

It is estimated that one in three rare and genetic diseases show up in these features, which could aid an earlier diagnosis. Researchers from Curtin University in Australia have developed a tool, as part of the Cliniface project, which scans the face, creating a 3D image. It then measures the distance between facial features and compares them with the average measurement for their ethnicity, sex and age according to their system. By way of example, they use Foetal Alcohol Spectrum Disorders (FASD), an umbrella term used to describe the range of effects caused by alcohol exposure in the uterus. Researchers have developed a too, called Cliniface, which scans the person's face and then creates a 3D image of it.

Children with rare conditions could be diagnosed quicker thanks to 3D facial analysis software. Richard Palmer at Curtin University in Western Australia and his colleagues have developed a tool that can spot subtle, but important, differences in facial geometry. Around one in three rare and genetic diseases show up in facial features.

Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to'teach' the algorithms what to look for. Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting. Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. "In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays."