Special Issue "Health Informatics: The Foundations of Public Health"
Deadline for manuscript submissions: 31 October 2022 | Viewed by 10213
Interests: data mining; medical/health informatics; artificial intelligence and applications; applied statistics
Interests: machine learning; medical/healthcare informatics; time series forecasting; supply chain management; quality management
Special Issues, Collections and Topics in MDPI journals
A Special Issue on Public Health in Health Informatics is being organized in Healthcare. For detailed information about the journal, please refer to https://www.mdpi.com/journal/healthcare. Public health provides an extremely wide variety of problems that can be tackled using computational and machine learning techniques. Health informatics is a spectrum of multidisciplinary fields that includes study of the design, development, and application of computational techniques to improve healthcare. Disciplines involved combine medical fields with computing fields such as software engineering, data science, information technology, and behavior informatics. Health informatics research focuses on applications of artificial intelligence in healthcare for academic institutions. As COVID-19 continues to put serious pressure on healthcare systems worldwide, with more than 188 million confirmed cases and more than 4 million death cases to date and huge datasets collected, it will provide lots of research topics for healthcare informatics. This Special Issue is open to relevant subject areas of healthcare informatics. The keywords listed below provide an outline of some of the possible areas of interest.
Prof. Dr. Michael T. S. Lee
Prof. Dr. Chi-Jie Lu
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 submissions that pass pre-check are 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. Healthcare 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 1800 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.
- health informatics
- public health
- behavior informatics
- medical informatics
- healthcare management
- artificial intelligence
- machine learning
- data analytics
- cognitive informatics
- data science
- information technology
- geographic information systems
- database management
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Develop a Natural Language Processing Pipeline to Automate Extraction of Periodontal Disease Information from Electronic Dental Clinical Notes
Authors: RISHI RAO; JASIM ALBANDAR; MARISOL TELLEZ; JOACHIM KROIS; HUANMEI WU *
Affiliation: Department of Health Services Administration and Policy, College of Public Health, Temple University Department Of Periodontology and Oral Implantology, Temple University Kornberg School of Dentistry Department of Oral Health Sciences, Temple University Kornberg School of Dentistry Department Of Oral Diagnostics, Digital Health and Health Services Research Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany
Abstract: Introduction: Periodontal disease (PD) is one of the most prevalent dental diseases, suffered by 80% of US adults. PD can be prevented if its etiologic and risk factors are identified and controlled early. Prediction models may help clinicians identify high-risk PD patients before the disease initiation and progression. Electronic dental record (EDR) data provide researchers a unique opportunity to develop prediction models that can provide personalized disease risk and treatment recommendations. However, 90% of rich clinical information is documented in the free-text format of EDR. The objective of this study was to develop natural language processing (NLP) applications to extract PD diagnoses, medical histories (e.g., cardiovascular diseases, diabetes), and social history (e.g., smoking) in a structured format for comprehensive follow-up periodontal research. Methods: We have developed a five-stage NLP pipeline. First, we retrieve both structured and no-structured data from the EDR using SQL queries. . we developed manual annotation guidelines using a bottom-up and top-down approach. Results: The SQL queries results of We have examined 347 clinical notes to identify the writing patterns in our EDR system. We also used existing literature to develop manual annotation guidelines. Two domain experts manually reviewed 4,000 clinical notes using the eHOST annotation tool to create a gold standard dataset. We then split the gold standard dataset into 40% training, 20% testing, and 40% validation datasets (external dataset). The training set was used to create NLP applications, and the performance of these applications was evaluated using the testing and validation sets. We achieved excellent results (>90% accuracy) in extracting patients’ detailed PD diagnoses, CVD, smoking, and diabetes information from the EDR. Out of a total of 27,138 unique patients, we found 2,358 (13%) patients into healthy, 3,474 (16%) into gingivitis, and 12,353 (67%) into periodontitis categories. We also found that 3,688 (13.6%) out of 27,138 patients had at least one reported CVD in the EDR. Moreover, 4,973 (18%) patients' HbA1C level was more than 7% indicating poor diabetes control. Last, we found that 1,406 patients were light smokers, and 589 patients were heavy smokers. We conclude that NLP applications designed to extract patients’ detailed PD diagnoses, CVD, diabetes, and smoking status worked excellently with high (>90%) accuracy. EDR data provided rich clinical information about patients’ periodontal health, and this data quality is high and has a high potential to be utilized for periodontal research. Most rich dental clinical information is documented in the free-text format; therefore, this information may not be readily available to the researchers. Hence, developing novel informatics methods such as NLP is critical for using EDR data optimally and efficiently for research.