Special Issue "Natural Language Processing in Healthcare and Medical Informatics"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editor

Dr. KC Santosh
E-Mail Website
Guest Editor
Department of Computer Science, The University of South Dakota, Vermillion, SD 57069, USA
Interests: pattern recognition; computer vision; machine learning; medical imaging; document analysis

Special Issue Information

Dear Colleagues,

Natural language processing (NLP) can help to revolutionize information management and retrieval in healthcare settings. Conventionally, text mining, information retrieval (IR), terminology, and NLP are used for schema mapping. Within this framework, in this Special Issue, we focus on relevant theoretical frameworks and empirical research findings in NLP for healthcare and medical informatics. Unlike the regular engine, we focus on having complex issues in the healthcare domain, where using knowledge classifications, taxonomies with hierarchical ties between knowledge items, and ontologies at a high cost in human labor and involvement must be addressed. 

Since NLP tools reorganize artificial intelligence (AI) techniques to focus on linguistic–conceptual relationships, rather than primarily textual analysis, knowledge integration is crucial, where it can help to translate synonymous terms using data and text mining to complete or correct existing knowledge structures. Out of many techniques and/or tools, we focus on deep learning and high-end machine learning tools for (though not limited to) the following tasks:

  • Clinical NLP with deep learning;
  • Clinical NLP for health outcomes research;
  • NLP to analyze/classify health literacy;
  • Clinical text mining/classification (weakly supervised case);
  • NLP for Clinical Notes (different pathologies);
  • NLP on electronic health records (EHRs);
  • NLP in healthcare with speech recognition;
  • NLP-driven computer assisted coding;
  • NLP for clinical trial matching;
  • NLP in clinical decision support;
  • NLP in healthcare with imaging techniques;
  • NLP and data visualization in healthcare.

Dr. KC Santosh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Article
Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary
Information 2020, 11(5), 281; https://doi.org/10.3390/info11050281 - 23 May 2020
Cited by 4 | Viewed by 1832
Abstract
Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information [...] Read more.
Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information that could be used to test the treatment quality for improving medical-related decisions. One of the significant techniques in literature for discharge summaries classification is feature extraction techniques from the domain of natural language processing on text data. We propose a novel sentiment analysis method for discharge summaries classification that relies on vector space models, statistical methods, association rule, and extreme learning machine autoencoder (ELM-AE). Our novel hybrid model is based on statistical methods that build the lexicon in a domain related to health and medical records. Meanwhile, our method examines treatment quality based on an idea inspired by sentiment analysis. Experiments prove that our proposed method obtains a higher F1 value of 0.89 with good TPR (True Positive Rate) and FPR (False Positive Rate) values compared with various well-known state-of-the-art methods with different size of training and testing datasets. The results also prove that our method provides a flexible and effective technique to examine treatment quality based on positive, negative, and neutral terms for sentence-level in each discharge summary. Full article
(This article belongs to the Special Issue Natural Language Processing in Healthcare and Medical Informatics)
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Article
Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital
Information 2020, 11(2), 106; https://doi.org/10.3390/info11020106 - 16 Feb 2020
Cited by 15 | Viewed by 2116
Abstract
In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we [...] Read more.
In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge. Full article
(This article belongs to the Special Issue Natural Language Processing in Healthcare and Medical Informatics)
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