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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Clinical Big Data And Deep Learning: Applications, Challenges, And Future Outlooks, Ying Yu, Liangliang Liu, Yaohang Li, Jianxin Wang
Clinical Big Data And Deep Learning: Applications, Challenges, And Future Outlooks, Ying Yu, Liangliang Liu, Yaohang Li, Jianxin Wang
Computer Science Faculty Publications
The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details …
Drug Repositioning Based On Bounded Nuclear Norm Regularization, Mengyun Yang, Huimin Lao, Yaohang Li, Jianxin Wang
Drug Repositioning Based On Bounded Nuclear Norm Regularization, Mengyun Yang, Huimin Lao, Yaohang Li, Jianxin Wang
Computer Science Faculty Publications
Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, …
Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles
Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles
Computer Science Faculty Publications
We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …
Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya
Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya
Computer Science Faculty Publications
Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …