This paper considers the problem of modeling long-term adverse events following prostatic surgery performed on patients with urination problems, using the French national health insurance database (SNIIRAM), which is a non-clinical claims database built around healthcare reimbursements of more than 65 million people. This makes the problem particularly challenging compared to what could be done using clinical hospital data, albeit a much smaller sample, while we exploit here the claims of almost all French citizens diagnosed with prostatic problems (with between 1.5 and 5 years of history). We introduce a new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) to capture such long-term adverse events, and we build a deep-learning architecture on top of it to deal with the complex, highly heterogeneous and sparse patterns observable in such a large claims database. This architecture combines several ingredients: embedding layers for drugs, medical procedures, and diagnosis codes; embeddings aggregation through a self-attention mechanism; recurrent layers to encode the health pathways of patients before their surgery and a final decoder layer which outputs the ZiMM's parameters.

We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.

Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning approaches burdens each development task as time and resource intensive. The vast majority of dense clinical information is stored in written reports, detailing pertinent patient information. The challenge with utilizing natural language data for standard model development is due to the complex nature of the modality. In this research, a model pipeline was developed to utilize an unsupervised approach to train an encoder-language model, a recurrent network, to generate document encodings; which then can be used as features passed into a decoder-classifier model that requires magnitudes less labeled data than previous approaches to differentiate between fine-grained disease classes accurately. The language model was trained on unlabeled radiology reports from the Massachusetts General Hospital Radiology Department (n=218,159) and terminated with a loss of 1.62. The classification models were trained on three labeled datasets of head CT studies of reported patients, presenting large vessel occlusion (n=1403), acute ischemic strokes (n=331), and intracranial hemorrhage (n=4350), to identify a variety of different findings directly from the radiology report data; resulting in AUCs of 0.98, 0.95, and 0.99, respectively, for the large vessel occlusion, acute ischemic stroke, and intracranial hemorrhage datasets. The output encodings are able to be used in conjunction with imaging data, to create models that can process a multitude of different modalities. The ability to automatically extract relevant features from textual data allows for faster model development and integration of textual modality, overall, allowing clinical reports to become a more viable input for more encompassing and accurate deep learning models.

Chronic disease progression is emerging as an important area of investment for healthcare providers. As the quantity and richness of available clinical data continue to increase along with advances in machine learning, there is great potential to advance our approaches to caring for patients. An ideal approach to this problem should generate good performance on at least three axes namely, a) perform across many clinical conditions without requiring deep clinical expertise or extensive data scientist effort, b) generalization across populations, and c) be explainable (model interpretability). We present SAVEHR, a self-attention based architecture on heterogeneous structured EHR data that achieves $>$ 0.51 AUC-PR and $>$ 0.87 AUC-ROC gains on predicting the onset of four clinical conditions (CHF, Kidney Failure, Diabetes and COPD) 15-months in advance, and transfers with high performance onto a new population. We demonstrate that SAVEHR model performs superior to ten baselines on all three axes stated formerly.

One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn't want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. Throwing a neural net at an RL problem will probably blow something up in front of your face -- and it will blow up in a different way each time you try. A lot of the biggest challenges in RL revolve around two questions: how we interact with the environment effectively (e.g. In this post, I want to explore a few recent directions in deep RL research that attempt to address these challenges, and do so with particularly elegant parallels to human cognition. This post will begin with a quick review of two canonical deep RL algorithms -- DQN and A3C -- to provide us some intuitions to refer back to, and then jump into a deep dive on a few recent papers and breakthroughs in the categories described above.

First, we will freeze the base layers so that the layers are not trainable.

Are you looking for fast deep learning modeling? If so, Keras is going to be your natural choice. But there are so many deep learning frameworks available today, and the list is growing very fast--why choose Keras? Keras has the capability and resources to run on top of popular deep learning libraries like TensorFlow, Theano, or CNTK. It also offers a relatively simple API that manages to also offer a lot of flexibility.

By leveraging the best of genetic algorithms and deep learning, Darwin provides the next step into the future of artificial intelligence.

Artificial intelligence can examine electrocardiogram (ECG) test results, a common medical test, to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia) or of dying within the next year, according to two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019--November 16-18 in Philadelphia. Researchers used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks--advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted. "This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care," said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania. Researchers speculated that a deep learning model could predict irregular heart rhythms, known as atrial fibrillation (AF), before it develops.

We are aware of the issue and are taking the necessary steps to address and resolve it,