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Found 16 papers, 3 papers with code
PerkwE_COQA: Enhanced Persian Conversational Question Answering by combining contextual keyword extraction with Large Language Models
Conversational query-answering is an emerging approach for user engagement.
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling
These results illustrate the proficiency of our proposed model in performing biomedical Named Entity Recognition.
Applying unsupervised keyphrase methods on concepts extracted from discharge sheets
Considering that most of the clinical concepts are in the form of multi-word expressions and their accurate identification requires the user to specify n-gram range, we have proposed a shortcut method to preserve the structure of the expression based on TF-IDF.
Assessing mortality prediction through different representation models based on concepts extracted from clinical notes
The quality of a learning embedding is influenced by how clinical notes are used as input to representation models.
Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice
With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic.
Hybrid deep learning methods for phenotype prediction from clinical notes
We also showed that BiGRU layer with FastText embedding had better performance than BiLSTM layer to identify patient phenotypes.
A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders
This paper proposes a new approach to network development named GraphTS to overcome the limited accuracy of existing methods through EMG time series using the visibility graph method.
A Study into patient similarity through representation learning from medical records
To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs.
MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist.
Text Summarization in the Biomedical Domain
The primary purpose of this chapter is to review the most significant research efforts made in the current decade toward new methods of biomedical text summarization.
A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm
A hybrid architecture called GGeo is proposed to apply the MOSES mining rules considering fuzzy topological relations from spatial data.
Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning
As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases.
Different approaches for identifying important concepts in probabilistic biomedical text summarization
Moreover, the results suggest that using the meaningfulness measure and considering the correlations of concepts in the feature selection step lead to a significant increase in the performance of summarization.
Fuzzy Least Squares Twin Support Vector Machines
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification.
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on.
Ambiguity-Driven Fuzzy C-Means Clustering: How to Detect Uncertain Clustered Records
However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases.
