37,326 research outputs found
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
Affective Man-Machine Interface: Unveiling human emotions through biosignals
As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals
Race in the Life Sciences: An Empirical Assessment, 1950-2000
The mainstream narrative regarding the evolution of race as an idea in the scientific community is that biological understandings of race dominated throughout the nineteenth and twentieth centuries up until World War II, after which a social constructionist approach is thought to have taken hold. Many believe that the horrific outcomes of the most notorious applications of biological raceāeugenics and the Holocaustāmoved scientists away from thinking that race reflects inherent differences and toward an understanding that race is a largely social, cultural, and political phenomenon. This understanding of the evolution of race as a scientific idea informed the way that many areas of law conceptualize human equality, including civil rights, human rights, and constitutional law.
This Article provides one of the first large-scale empirical assessments of publications in peer-reviewed biomedical and life science journals to examine whether biological theories of race actually lost credibility in the life sciences after World War II. We find that biological theories of race transformed yet persisted in the dominant academic discourse up through modern timesāa finding that contradicts the central narrative that the life sciences became ācolor-blindā or āpost-racialā several decades ago. The continued salience of biological race in the life sciences suggests that more attention needs to be paid to the questionable assumptions driving this research on biological race and its potential spillover effects, i.e., how persisting claims of biological race in the scientific literature might reconstitute its significance in law and society in a manner that may be harmful to racial minorities
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
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