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Found 19 papers, 6 papers with code
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health.
Time Expressions in Mental Health Records for Symptom Onset Extraction
Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these.
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results.
Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics
By taking advantage of Apache Spark, Nvidia DGX1, and DGX2 computing platforms, we demonstrate unprecedented compute speed-ups for deep learning inference on pixel labeling workloads; processing 21, 028~Terrabytes of imagery data and delivering an output maps at area rate of 5. 245sq. km/sec, amounting to 453, 168 sq. km/day - reducing a 28 day workload to 21~hours.
The side effect profile of Clozapine in real world data of three large mental hospitals
Further, the data was combined from three trusts, and chi-square tests were applied to estimate the average effect of ADRs in each monthly interval.
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records.
Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records
The purpose of this analysis was to understand the complexity of medication mentions and their associated temporal information in the free text of EHRs, with a specific focus on the mental health domain.
Comparative Analysis of Text Classification Approaches in Electronic Health Records
Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research.
Bombus Species Image Classification
Entomologists, ecologists and others struggle to rapidly and accurately identify the species of bumble bees they encounter in their field work and research.
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis.
Benchmarking Quantized Neural Networks on FPGAs with FINN
FINN and Brevitas, two frameworks from Xilinx labs, are used to assess the impact of quantization on neural networks using 2 to 8 bit precisions and weights with several parallelization configurations.
Network robustness as a mathematical property: training, evaluation and attack
We also perform experiments to compare the applicability and efficacy of different training methods for ensuring the network obeys these different definitions.
Differentiable Logics for Neural Network Training and Verification
What difference does a specific choice of DL make in the context of continuous verification?
Logic of Differentiable Logics: Towards a Uniform Semantics of DL
A DL consists of a syntax in which specifications are stated and an interpretation function that translates expressions in the syntax into loss functions.
Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach
Mental health electronic health records are a good data source to study this overlap.
Development of a Knowledge Graph Embeddings Model for Pain
This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task.
Sample Size in Natural Language Processing within Healthcare Research
The simulations conducted within this study provide guidelines that can be used as recommendations for selecting appropriate sample sizes and class proportions, and for predicting expected performance, when building classifiers for textual healthcare data.
Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach
To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.
