Discriminant document embeddings with an extreme learning machine for classifying clinical narratives

Patient-rated scales improve the classification accuracy for patients with depression and anxiety disorder: a linear discriminant analysis

BACKGROUND

The current study aimed to investigate the performances of clinical scales rated by clinicians and patients as well as cognitive function tests in distinguishing patients with affective and anxiety disorders from healthy controls (HCs).

METHODS

We recruited a total of 122 subjects, comprising 24 patients with bipolar disorder (BD), 34 patients with major depressive disorder (MDD), 29 patients with anxiety disorder (AD), and 35 matched HCs. Three clinician-rated scales and five patient-rated scales were used to quantify clinical symptoms, while four cognitive tests were employed to measure cognitive functions in all subjects. Fisher's discriminant analysis (FDA) was employed to distinguish patients from HCs, as well as to discriminate patient sub-groups from each other. In the FDA model, the prior probability of each group was set as 0.5 in the two-group classification and 0.25 in the four-group classification.

RESULTS

The results showed that patient-rated scales achieved higher classification accuracies than clinician-rated scales in identifying MDD and AD from HCs. In contrast, cognitive tests exhibited the lowest accuracy.

CONCLUSIONS

These findings suggest that patient-rated scales might improve the classification accuracy for patients with MDD and AD.

A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models

Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP-related tasks. In the end, this survey briefly discusses the commonly used ML- and DL-based classifiers, evaluation metrics, and the applications of these word embeddings in different NLP tasks.

Automatic recommendation of medical departments to outpatients based on text analyses and medical knowledge graph

In many countries, outpatients generally visit a major hospital without a referral from health professionals due to the shortage of family physicians. Not knowing at which medical specialty department to register, outpatients have to wait in long queues to consult receptionists. We propose to alleviate this situation via a computer system offering an automatic recommendation of departments (ARD) to outpatients, which identifies the appropriate medical department for outpatients according to their chief complaints. Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients.

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

Dynamic Data Retrieval Using Incremental Clustering and Indexing

The evolution of the Internet and real-time applications has contributed to the growth of massive unstructured data which imposes the increased complexity of efficient retrieval of dynamic data. Extant research uses clustering methods and indexes to speed up the retrieval. However, the quality of clustering methods depends on data representation models where existing models suffer from dimensionality explosion and sparsity problems. As documents evolve, index reconstruction from scratch is expensive. In this work, compact vectors of documents generated by the Doc2Vec model are used to cluster the documents and the indexes are incrementally updated with less complexity using the diff method. The probabilistic ranking scheme BM25+ is used to improve the quality of retrieval for user queries. The experimental analysis demonstrates that the proposed system significantly improves the clustering performance and reduces retrieval time to obtain top-k results.