67 research outputs found
Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however limited for requirement of large training sample size
and high computational costs. In this paper we show that using small sample
size, denoising autoencoders constructed using convolutional layers can be used
for efficient denoising of medical images. Heterogeneous images can be combined
to boost sample size for increased denoising performance. Simplest of networks
can reconstruct images with corruption levels so high that noise and signal are
not differentiable to human eye.Comment: To appear: 6 pages, paper to be published at the Fourth Workshop on
Data Mining in Biomedical Informatics and Healthcare at ICDM, 201
Recovering Loss to Followup Information Using Denoising Autoencoders
Loss to followup is a significant issue in healthcare and has serious
consequences for a study's validity and cost. Methods available at present for
recovering loss to followup information are restricted by their expressive
capabilities and struggle to model highly non-linear relations and complex
interactions. In this paper we propose a model based on overcomplete denoising
autoencoders to recover loss to followup information. Designed to work with
high volume data, results on various simulated and real life datasets show our
model is appropriate under varying dataset and loss to followup conditions and
outperforms the state-of-the-art methods by a wide margin ( in some
scenarios) while preserving the dataset utility for final analysis.Comment: Copyright IEEE 2017, IEEE International Conference on Big Data (Big
Data
Competing risk survival analysis using SAS ® When, why and how
ABSTRACT Competing risk arise in time to event data when the event of interest cannot be observed because of a preceding event i.e. a competing event occurring before. An example can be of an event of interest being a specific cause of death where death from any other cause can be termed as a competing event, if focusing on relapse, death before relapse would constitute a competing event. It is well studied and pointed out that in presence of competing risks, the standard product limit methods yield biased results due to violation of their basic assumption. The effect of competing events on parameter estimation depends on their distribution and frequency. Fine and Gray's sub-distribution hazard model can be used in presence of competing events which is available in PROC PHREG with the release of version 9.4 of SAS ® software.
Comparison of One versus Two Fecal Immunochemical Tests in the Detection of Colorectal Neoplasia in a Population-Based Colorectal Cancer Screening Program
Objective. To determine the positive predictive value (PPV) of two versus one abnormal FIT in the detection of colorectal neoplasia in a Canadian population. Methods. Three communities enrolled in a colorectal cancer (CRC) screening pilot program from 01/2009 to 04/2013 using 2 FITs. Data collected included demographics, colonoscopy, pathology, and FIT results. Participants completed both FITs and had one positive FIT and colonoscopy. PPV of one versus two abnormal FITs was calculated using a weightedgeneralized score statistic. A two-sided 5% significance level was used. Results. 1576 of 17,031 average-risk participants, 50-75 years old, had a positive FIT. Colonoscopy revealed 58 (3.7%) cancers, 419 (31.6%) high-risk polyps, and 374 (23.7%) low-risk polyps as the most significant lesion. PPV of one versus two positive FITs for cancer, high-risk polyps, and any neoplasia were 1% versus 8%, 20% versus 40%, and 48% versus 67%, respectively ( value < 0.0001). When the first FIT was negative, the second positive FIT detected 7 CRCs and 98 high-risk polyps. Conclusions. PPV of two positive FITs is superior to one positive FIT for CRC and high-risk polyps. The added value of the second FIT was 12% of total CRCs and 23% of total high-risk polyps
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Missing data is one of the most common preprocessing problems. In this paper,
we experimentally research the use of generative and non-generative models for
feature reconstruction. Variational Autoencoder with Arbitrary Conditioning
(VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as
representatives of generative models, while the denoising autoencoder (DAE)
represented non-generative models. Performance of the models is compared to
traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by
Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein
modification of GAIN, which turns out to be the best imputation model when the
degree of missingness is less than or equal to 30%. Experiments were performed
on real-world and artificial datasets with continuous features where different
percentages of features, varying from 10% to 50%, were missing. Evaluation of
algorithms was done by measuring the accuracy of the classification model
previously trained on the uncorrupted dataset. The results show that GAIN and
especially WGAIN are the best imputers regardless of the conditions. In
general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.Comment: Preprint of the conference paper (ICCS 2020), part of the Lecture
Notes in Computer Scienc
Social and cultural determinants of antibiotics prescriptions: analysis from a public community health centre in North India
This paper explores the socio cultural and institutional determinants of irresponsible prescription and use of antibiotics which has implications for the rise and spread of antimicrobial resistance (AMR). This study describes the patterns of prescription of antibiotics in a public facility in India and identifies the underlying institutional, cultural and social determinants driving the irresponsible use of antibiotics. The analysis is based on an empirical investigation of patients’ prescriptions that reach the in-house pharmacy following an outpatient department (OPD) encounter with the clinician. The prescription analysis describes the factors associated with use of broad-spectrum antibiotics, and a high percentage of prescriptions for dental outpatient department prescribed as a precautionary measure. This paper further highlights the need for future research insights in combining socio-cultural approach with medical rationalities, to further explore questions our analysis highlights like higher antibiotic prescription, etc., Along with the recommendations for further research
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