Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced
data make this prediction task extremely hard. Most existing work built a binary prediction for each label independently, ignoring the dependencies between labels. To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate generated by a base predictor. This paper is the first attempt at learning the label set distribution as a reranking module for ICD coding. In the experiments, our proposed framework is able to improve upon best-performing predictors for medical code prediction on the benchmark MIMIC datasets.
We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it
considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content. First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models. Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores.
MicroRNAs (miRNAs) are single-stranded, non-coding RNA
molecules, which can regulate the translation of target proteins and
thereby control biological functions. They qualify as diagnostic
markers and can help to detect diseases earlier. In case o
f a disease
the concentrations of specific miRNAs which are characteristic for
it do vary in quantity. For the detection of miRNA we are using a
surface plasmon resonance (SPR) biosensor, which allows to
measure interactions of molecules at an interface in real-time. For
this, thiolated LNA capture probes are immobilized on the gold
chip.
A mammogram is the best option for early detection of breast cancer,
Computer Aided Diagnostic systems(CADs) developed in order to
improve the diagnosis of mammograms. This paper presents a proposed
method to automatic images segmentation dependin
g on the Otsu's
method in order to detect microcalcifications and mass lesions in
mammogram images. The proposed technique is based on three steps:
(a) region of interest (ROI), (b) 2D wavelet transformation, and (c) OTSU
thresholding application on ROI. The method tested on standard mini-
MIAS database. It implemented within MATLAB software environment.
Experimental results and performance evaluate results show that the
proposed detection algorithm is a tool to help improve the diagnostic
performance, and has the possibility and the ability to detect the breast
lesions.
This study has conducted to investigate the effect of air pollution on diagnostic elements and morphologicl characters of leaves of Neruim oleander Cultivated in roadsides in Lattakia City . Samples were collected from tow sites differ in pollution
inteusity between october 2012 and september 2013 .
The first site ( Al Jmhoria street in Lattakia city ) was more polluted and has more traffic activity .
Whil the second site ( tishreen University park )
Was Less polluted and less traffic activity .
The results have showed that the length , the weight and the colour of the leaf are negtively incieased because of traffic intensity and this , inturn , affects the shape , the size and the number of diagnostic elements which play animportant rule in differentation the species from each other .
Low frequency shadows is one of hydrocarbons indicators. It can be
detected by means of a time-frequency decomposition which can provide higher
frequency resolution at lower frequencies and higher time resolution at higher
frequencies. This is des
irable for analyzing seismic data, because the
hydrocarbons in reservoir are diagnostic at lower frequencies. we have carried
out such analyses with post-stack data sets on Fahda field which is located in
Aleppo uplift, it contains oil. Adding a frequency axis to a 2D seismic section
makes the data 3D axis. The comparison of the single frequency sections from
such 3D volume can be utilized to detect low frequency shadows. A
preferentially illuminated single frequency section at lower frequencies from
Fahda field, shows high amplitude low frequency anomalies beneath oil zones.
These anomalies disappear at higher frequencies.
