8,027 research outputs found
Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles
Machine learning shows remarkable success for recognizing patterns in data.
Here we apply the machine learning (ML) for the diagnosis of early stage
diabetes, which is known as a challenging task in medicine. Blood glucose
levels are tightly regulated by two counter-regulatory hormones, insulin and
glucagon, and the failure of the glucose homeostasis leads to the common
metabolic disease, diabetes mellitus. It is a chronic disease that has a long
latent period the complicates detection of the disease at an early stage. The
vast majority of diabetics result from that diminished effectiveness of insulin
action. The insulin resistance must modify the temporal profile of blood
glucose. Thus we propose to use ML to detect the subtle change in the temporal
pattern of glucose concentration. Time series data of blood glucose with
sufficient resolution is currently unavailable, so we confirm the proposal
using synthetic data of glucose profiles produced by a biophysical model that
considers the glucose regulation and hormone action. Multi-layered perceptrons,
convolutional neural networks, and recurrent neural networks all identified the
degree of insulin resistance with high accuracy above .Comment: 4 pages, 2 figur
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation
People with Type 1 diabetes (T1D) require regular exogenous infusion of
insulin to maintain their blood glucose concentration in a therapeutically
adequate target range. Although the artificial pancreas and continuous glucose
monitoring have been proven to be effective in achieving closed-loop control,
significant challenges still remain due to the high complexity of glucose
dynamics and limitations in the technology. In this work, we propose a novel
deep reinforcement learning model for single-hormone (insulin) and dual-hormone
(insulin and glucagon) delivery. In particular, the delivery strategies are
developed by double Q-learning with dilated recurrent neural networks. For
designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator
was employed. First, we performed long-term generalized training to obtain a
population model. Then, this model was personalized with a small data-set of
subject-specific data. In silico results show that the single and dual-hormone
delivery strategies achieve good glucose control when compared to a standard
basal-bolus therapy with low-glucose insulin suspension. Specifically, in the
adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved
from 77.6% to 80.9% with single-hormone control, and to with
dual-hormone control. In the adolescent cohort (n=10), percentage time in
target range improved from 55.5% to 65.9% with single-hormone control, and to
78.8% with dual-hormone control. In all scenarios, a significant decrease in
hypoglycemia was observed. These results show that the use of deep
reinforcement learning is a viable approach for closed-loop glucose control in
T1D
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
An Empirical Model for Thyroid Disease Classification using Evolutionary Multivariate Bayseian Prediction Method
Thyroid diseases are widespread worldwide. In India too, there is a significant problems caused due to thyroid diseases. Various research studies estimates that about 42 million people in India suffer from thyroid diseases [4]. There are a number of possible thyroid diseases and disorders, including thyroiditis and thyroid cancer. This paper focuses on the classification of two of the most common thyroid disorders are hyperthyroidism and hypothyroidism among the public. The National Institutes of Health (NIH) states that about 1% of Americans suffer from Hyperthyroidism and about 5% suffer from Hypothyroidism. From the global perspective also the classification of thyroid plays a significant role. The conditions for the diagnosis of the disease are closely linked, they have several important differences that affect diagnosis and treatment. The data for this research work is collected from the UCI repository which undergoes preprocessing. The preprocessed data is multivariate in nature. Curse of Dimensionality is followed so that the available 21 attributes is optimized to 10 attributes using Hybrid Differential Evolution Kernel Based Navie Based algorithm. The subset of data is now supplied to Kernel Based NaEF;ve Bayes classifier algorithm in order to check for the fitness
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