If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Heart failure hospitalization is a severe burden on healthcare. How to predict and therefore prevent readmission has been a significant challenge in outcomes research. To address this, we propose a deep learning approach to predict readmission from clinical notes. Unlike conventional methods that use structured data for prediction, we leverage the unstructured clinical notes to train deep learning models based on convolutional neural networks (CNN). We then use the trained models to classify and predict potentially high-risk admissions/patients. For evaluation, we trained CNNs using the discharge summary notes in the MIMIC III database. We also trained regular machine learning models based on random forest using the same datasets. The result shows that deep learning models outperform the regular models in prediction tasks. CNN method achieves a F1 score of 0.756 in general readmission prediction and 0.733 in 30-day readmission prediction, while random forest only achieves a F1 score of 0.674 and 0.656 respectively. We also propose a chi-square test based method to interpret key features associated with deep learning predicted readmissions. It reveals clinical insights about readmission embedded in the clinical notes. Collectively, our method can make the human evaluation process more efficient and potentially facilitate the reduction of readmission rates.
Neurons in a human brain have been somewhat of a mystery for scientists. Unlike the traditional electrical circuits, the inner workings of the biological circuitry in the brain have always been less than predictable, apart from the complex biology they exhibit. Scientists at the University of Bath now seem to have decoded the bizarre behavior of our brain cells and replicated it on tiny silicon chips. Researchers from the Universities of Bristol, Zurich & Auckland collaborated on this effort. Designing artificial neurons has been a challenge for medical researchers for decades.
You are free to share this article under the Attribution 4.0 International license. An experimental device uses machine learning tools--and a bathroom scale--to monitor heart failure. Researchers envision this scenario: The user steps onto a the scale and touches metal pads. The device records an electrocardiogram from their fingers and--more importantly--circulation pulsing that makes the body subtly bob up and down on the scale. Machine learning tools compute that heart failure symptoms have worsened.
Owkin, which is developing federated learning and AI technologies to advance medical research, has announced a collaboration with technology company NVIDIA and King's College London (KCL) to deliver federated learning in the healthcare and life sciences sector. It will initially connect four of London's teaching hospitals before expanding throughout the UK, and will offer AI services with the aim of accelerating research and improving clinical practice in a wide range of therapeutic areas, including cancer, heart failure and neurodegenerative disease. Owkin's co-founder and chief scientific officer, Gilles Wainrib, said: "This partnership brings together the best players in life science & healthcare, machine learning and data centre infrastructure. NVIDIA's platforms create the ideal and flexible footprint for hospitals to invest in machine learning. King's College London has assembled the engineering, medical and data science talent, the high-quality patient data, and the governance framework in the AI4VBH Centre, that will show the world the future of healthcare analytics and the power of machine learning. Together we will be enabling the formation of a decentralised dataset that will generate enormous value for research and clinical practice. "Owkin hopes to demonstrate that a Federating Learning architecture is safer for patients, and statistically equivalent to the traditional pooled model for analysis.
When Avi Yagil, PhD, Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012, he thought he had caught a cold from his travels. When a "collection of pills" did not improve his symptoms, his wife encouraged him to see a doctor. Further tests revealed something far more life-threatening to Yagil than the common cold. "A chest X-Ray showed my lungs were flooded with fluid, and a subsequent echocardiogram found I had damage to my heart." Yagil was diagnosed with heart failure.
An artificial intelligence (AI) tool had an 88% success rate in predicting life expectancy in patients with heart failure, researchers at UC San Diego Health reported in the European Journal of Heart Failure. Using a machine-learning algorithm, researchers developed a mortality risk score in patients with heart failure. The risk score had an area under the curve of 0.88 and was predictive across the full spectrum of risk, study authors wrote. The results support the use of the AI to evaluate patients with heart failure and in other settings where predicting risk has been challenging, reported the authors. "This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year," said Eric Adler, M.D., cardiologist and director of cardiac transplant and mechanical circulatory support at the Cardiovascular Institute at UC San Diego Health.
Researchers have developed an artificial intelligence (AI) tool to predict life expectancy in heart failure patients. The machine learning algorithm based on de-identified electronic health, records data of 5,822 hospitalised or ambulatory patients with heart failure at UC San Diego Health in the US. "We wanted to develop a tool that predicted life expectancy in heart failure patients, there are apps where algorithms are finding out all kinds of things, like products you want to purchase," said Avi Yagil, Professor at University of California. "We needed a similar tool to make medical decisions. Predicting mortality is important in patients with heart failure. Current strategies for predicting risk, however, are only modestly successful and can be subjective," Yagil added.
Ejection fraction is an important method of mortality prediction among cardiac patients and a low ejection fraction number suggests problems with the heart's pumping function, and may be associated with heart failure. An estimated 6.2 million Americans suffer from heart failure, according to federal statistics. The American Heart Association predicts that more than eight million will have the condition by 2030. When tested on 100 patients, the Eko DUO device combined with an AI model was able to detect ejection fraction 35% with an area under the curve (AUC) of 0.90, which is comparable to previously published research in Nature Medicine. These findings could help identify patients with a low ejection fraction during routine physical examinations, facilitating rapid clinical recognition of those requiring further testing. This marks the first time that a point of care device with a single lead ECG combined with an AI algorithm identified low ejection fraction in patients.
When Avi Yagil, Ph.D., Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012, he thought he had caught a cold from his travels. When a "collection of pills" did not improve his symptoms, his wife encouraged him to see a doctor. Further tests revealed something far more life-threatening to Yagil than the common cold. "A chest X-Ray showed my lungs were flooded with fluid, and a subsequent echocardiogram found I had damage to my heart." Yagil was diagnosed with heart failure.
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes. TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical reformulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14x faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 80 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.