127,797 research outputs found
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Personalized predictive medicine necessitates the modeling of patient illness
and care processes, which inherently have long-term temporal dependencies.
Healthcare observations, recorded in electronic medical records, are episodic
and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural
network that reads medical records, stores previous illness history, infers
current illness states and predicts future medical outcomes. At the data level,
DeepCare represents care episodes as vectors in space, models patient health
state trajectories through explicit memory of historical records. Built on Long
Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle
irregular timed events by moderating the forgetting and consolidation of memory
cells. DeepCare also incorporates medical interventions that change the course
of illness and shape future medical risk. Moving up to the health state level,
historical and present health states are then aggregated through multiscale
temporal pooling, before passing through a neural network that estimates future
outcomes. We demonstrate the efficacy of DeepCare for disease progression
modeling, intervention recommendation, and future risk prediction. On two
important cohorts with heavy social and economic burden -- diabetes and mental
health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare
trajectories from medical records: A deep learning approach
Comparative Study of Artificial Neural Network based Classification for Liver Patient
The extensive accessibility of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to steadily select the most appropriate strategy to cope with clinical prediction problems. Data mining offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. Therefore, in this study, Liver patient data is considered and evaluated by univariate analysis and a feature selection method for predicator attributes determination. Further comparative study of artificial neural network based predictive models such as BP, RBF, SOM, SVM are provided. Keywords: Medical Informatics, Classification, Liver Data, Artificial Neural Networ
A Review on Deep Learning in Medical Image Reconstruction
Medical imaging is crucial in modern clinics to guide the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most
fundamental and important components of medical imaging, whose major objective
is to acquire high-quality medical images for clinical usage at the minimal
cost and risk to the patients. Mathematical models in medical image
reconstruction or, more generally, image restoration in computer vision, have
been playing a prominent role. Earlier mathematical models are mostly designed
by human knowledge or hypothesis on the image to be reconstructed, and we shall
call these models handcrafted models. Later, handcrafted plus data-driven
modeling started to emerge which still mostly relies on human designs, while
part of the model is learned from the observed data. More recently, as more
data and computation resources are made available, deep learning based models
(or deep models) pushed the data-driven modeling to the extreme where the
models are mostly based on learning with minimal human designs. Both
handcrafted and data-driven modeling have their own advantages and
disadvantages. One of the major research trends in medical imaging is to
combine handcrafted modeling with deep modeling so that we can enjoy benefits
from both approaches. The major part of this article is to provide a conceptual
review of some recent works on deep modeling from the unrolling dynamics
viewpoint. This viewpoint stimulates new designs of neural network
architectures with inspirations from optimization algorithms and numerical
differential equations. Given the popularity of deep modeling, there are still
vast remaining challenges in the field, as well as opportunities which we shall
discuss at the end of this article.Comment: 31 pages, 6 figures. Survey pape
Predicting systolic blood pressure using machine learning
In this paper, a new study based on machine learning technique, specifically artificial neural network, is investigated to predict the systolic blood pressure by correlated variables (BMI, age, exercise, alcohol, smoke level etc.). The raw data are split into two parts, 80% for training the machine and the remaining 20% for testing the performance. Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the prediction system. Based on a database with 498 people, the probabilities of the absolute difference between the measured and predicted value of systolic blood pressure under 10mm Hg are 51.9% for men and 52.5% for women using the back-propagation neural network With the same input variables and network status, the corresponding results based on the radial basis function network are 51.8% and 49.9% for men and women respectively. This novel method of predicting systolic blood pressure contributes to giving early warnings to young and middle-aged people who may not take regular blood pressure measurements. Also, as it is known an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff. Our experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. © 2014 IEEE.published_or_final_versio
Efficient Continuous Manifold Learning for Time Series Modeling
Modeling non-Euclidean data is drawing attention along with the unprecedented
successes of deep neural networks in diverse fields. In particular, symmetric
positive definite (SPD) matrix is being actively studied in computer vision,
signal processing, and medical image analysis, thanks to its ability to learn
appropriate statistical representations. However, due to its strong
constraints, it remains challenging for optimization problems or inefficient
computation costs, especially, within a deep learning framework. In this paper,
we propose to exploit a diffeomorphism mapping between Riemannian manifolds and
a Cholesky space, by which it becomes feasible not only to efficiently solve
optimization problems but also to reduce computation costs greatly. Further, in
order for dynamics modeling in time series data, we devise a continuous
manifold learning method by integrating a manifold ordinary differential
equation and a gated recurrent neural network in a systematic manner. It is
noteworthy that because of the nice parameterization of matrices in a Cholesky
space, it is straightforward to train our proposed network with Riemannian
geometric metrics equipped. We demonstrate through experiments that the
proposed model can be efficiently and reliably trained as well as outperform
existing manifold methods and state-of-the-art methods in two classification
tasks: action recognition and sleep staging classification
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