Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning Approach Using Clinical Note Annotations.

Background: Lower back pain is a common weakening condition that affects a large population. It is a leading cause of disability and lost productivity, and the associated medical costs and lost wages place a substantial burden on individuals and society. Recent advances in artificial intelligence and natural language processing have opened new opportunities for the identification and management of risk factors for lower back pain.

Causal graph extraction from news: a comparative study of time-series causality learning techniques.

Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles.

COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing.

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques.

Using molecular embeddings in QSAR modeling: does it make a difference?

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning molecular embeddings and their theoretical benefits, comparing molecular embeddings with each other and with traditional representations is not straightforward, which in turn hinders the process of choosing a suitable representation for Quantitative Structure-Activity Relationship (QSAR) modeling. A reason behind this issue is the difficulty of conducting a fair and thorough comparison of the different existing embedding approaches, which requires numerous experiments on various datasets and training scenarios.

MiBio: A dataset for OCR post-processing evaluation.

We introduce a dataset for OCR post-processing model evaluation. This dataset contains fully aligned OCR texts and the ground truth recognition texts of a English biodiversity book. To better used for benchmark evaluation, we extracted the following information in TSV files: 1) 2907 OCR-generated errors with position in the OCR texts and correction in the ground truth text, 2) ground truth word and sentence segmentation of the OCR texts.

Acoustic positioning using multiple microphone arrays.

Passive acoustic techniques are presented to solve the localization problem of a sound source in three-dimensional space using off-the-shelf hardware. Multiple microphone arrays are employed, which operate independently, in estimating the direction of arrival of sound, or, equivalently, a direction vector from the array's geometric center towards the source. Direction vectors and array centers are communicated to a central processor, where the source is localized by finding the intersection of the direction lines defined by the direction vectors and the associated array centers.

Knowledge management in pediatric pain: mapping on-line expert discussions to medical literature.

Clinical decision-making can be vastly improved with the availability of the right medical knowledge at the right time. This concept paper presents a knowledge management re-search program to (a) identify, capture and organize the tacit knowledge inherent within on-line problem-solving discussions between pediatric pain practitioners; (b) establish linkages between topic-specific pediatric pain discussions and corresponding published medical literature on children's pain available at PubMed--i.e.

Sonification of range information for 3-D space perception.

We present a device that allows three-dimensional (3-D) space perception by sonification of range information obtained via a point laser range sensor. The laser range sensor is worn by a blindfolded user, who scans space by pointing the laser beam in different directions. The resulting stream of range measurements is then converted to an auditory signal whose frequency or amplitude varies with the range.

Active stereo sound localization.

Estimating the direction of arrival of sound in three-dimensional space is typically performed by generalized time-delay processing on a set of signals from a fixed array of omnidirectional microphones. This requires specialized multichannel A/D hardware, and careful arrangement of the microphones into an array. This work is motivated by the desire to instead only use standard two-channel audio A/D hardware and portable equipment.

Filtering for medical news items using a machine learning approach.

We address the problem of filtering medical news articles for targeted audiences. The approach is based on terms and one of the difficulties is extracting a feature set appropriate for the domain. This paper addresses the medical news-filtering problem using a machine learning approach.