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Nice latent variable models have log-rank

arXiv.org Machine Learning

Matrices of low rank are pervasive in big data, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why the low rank structure appears in the first place. We explain the abundance of low rank matrices in big data by proving that certain latent variable models associated to piecewise analytic functions are of log-rank. A large matrix from such a latent variable model can be approximated, up to a small error, by a low rank matrix.


Intertwining Artificial Intelligence With Blockchain

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Except for those folks living under rocks (sounds uncomfortable), everyone knows about or at least has heard of bitcoin. However, not everyone understands the technology of bitcoin, which extends well beyond Internet-based currency. For the rock people, bitcoin is an Internet-based currency that allows for transparency with respect to each transfer of the currency through the use of a distributed database. Each transaction is locked in a block, and blocks are connected to form a "blockchain." Blockchain is an open source technology that facilitates creating each block, locking each block, and connecting the resulting string of blocks.


The opportunities and challenges of AI in health care VentureBeat AI

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When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on health care. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with AI we could achieve exponential breakthroughs. Deep learning first caught the media's attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford's School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists. The ultimate dream in health care is to eradicate disease entirely.


Millions of veteran health care records are being used to train this startup's artificial intelligence

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Last spring the startup Flow Health began a five-year contract with the Department of Veteran Affairs to examine all historic and ongoing medical records. The startup will use information obtained from those records to train artificial intelligence to, among other things, fight illness and predict disease for the more than eight million people cared for by the Department of Veteran Affairs. Advice and predictions from Flow Health will be presented to health care professionals through Vista, the DoD's open source system for electronic medical records. Doctors can then choose to apply or ignore the advice drawn from the VA's vast storage of medical records. "When a veteran comes in and presents certain clinical symptoms, we can better understand and make predictions about'What is the likely diagnosis?


Stanford researchers: Artificial intelligence is ripe for healthcare

#artificialintelligence

When it comes to artificial intelligence, forget the scary movies about rebellious robots or the dire warnings of a dystopian world of disconnected humanity imagined by some popular writers. AI promises, rather, to change our lives in profound ways we are just beginning to experience, according to a ground-breaking survey produced by Stanford University. Stanford is taking the long view of AI, with a project called One Hundred Study on Artificial Intelligence (AI100). The study, written by a panel of AI experts from multiple fields including healthcare, will continue as an ongoing activity, with periodic reports examining how AI will touch different aspects of daily life. The first of those reports, "Artificial Intelligence and Life in 2030," looks into the effects that AI advancements will have on a typical North American city a little more than a decade from now.


Demographical Priors for Health Conditions Diagnosis Using Medicare Data

arXiv.org Machine Learning

This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the age has the potential of aiding the diagnostic process in hospitals. Working with Electronic Health Records (EHR), we show that medical conditions group into clusters that share distinctive population age densities. We use Electronic Health Records from Brazil for a period of 15 months from March of 2013 to July of 2014. The number of patients in the data is 1.7 million patients and the number of records is 47 million records. The findings has the potential of helping in a setting where an automated system undergoes the task of predicting the condition of a patient given their symptoms and demographical information.


What's the Big Deal with Big Data? - ASH Clinical News

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Clinical trials, the largest of which may enroll a few thousand patients with hematology or oncology diagnoses, represent the gold standard of clinical research. But what if clinical decisions could be made, or research questions answered, using data from tens of thousands or even a million patients? Initiatives are springing up across the country to examine the power and promise of big data – massive amounts of information that can be analyzed to provide an overview of trends or patterns – to revolutionize health care and transform how patients are diagnosed, treated, and even involved in their own care. For instance, in 2012, the National Institutes of Health (NIH) established the Big Data to Knowledge (BD2K) initiative, an effort to promote research and development of tools and approaches that would accelerate the use of big data in biomedical research.1 This spring, IBM launched IBM Watson Health and the Watson Health Cloud platform, a new unit of the IBM Watson cognitive computing system that will analyze and extract large volumes of health data from structured and unstructured medical systems.2


A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference

arXiv.org Machine Learning

Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning an HASMM from the EHR data is achieved via a novel forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reverse-time direction while conditioning on the future states. Real-time inferences are drawn via a forward- filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the di- agnostic and prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center.


Stanford researchers: Artificial intelligence is ripe for healthcare

#artificialintelligence

When it comes to artificial intelligence, forget the scary movies about rebellious robots or the dire warnings of a dystopian world of disconnected humanity imagined by some popular writers. AI promises, rather, to change our lives in profound ways we are just beginning to experience, according to a ground-breaking survey produced by Stanford University. Stanford is taking the long view of AI, with a project called One Hundred Study on Artificial Intelligence (AI100). The study, written by a panel of AI experts from multiple fields including healthcare, will continue as an ongoing activity, with periodic reports examining how AI will touch different aspects of daily life. The first of those reports, "Artificial Intelligence and Life in 2030," looks into the effects that AI advancements will have on a typical North American city a little more than a decade from now.


Feature-Augmented Neural Networks for Patient Note De-identification

arXiv.org Machine Learning

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-the-art results. Unlike other systems, it does not rely on human-engineered features, which allows it to be quickly deployed, but does not leverage knowledge from human experts or from electronic health records (EHRs). In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system. Our results show that the addition of features, especially the EHR-derived features, further improves the state-of-the-art in patient note de-identification, including for some of the most sensitive PHI types such as patient names. Since in a real-life setting patient notes typically come with EHRs, we recommend developers of de-identification systems to leverage the information EHRs contain.