Medical data mining in sentiment analysis based on optimized swarm search feature selection

LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.

Managing Complexity. From Documentation to Knowledge Integration and Informed Decision Findings from the Clinical Information Systems Perspective for 2018

Objective : To summarize recent research and to propose a selection of best papers published in 2018 in the field of Clinical Information Systems (CIS).

Method : Each year a systematic process is carried out to retrieve articles for the CIS section of the IMIA Yearbook of Medical Informatics and to select a set of pest papers for the section. The same query as in the last five years was used. The retrieved articles were categorized in a multi-pass review carried out by the two section editors. The final selection of candidate papers was then peer-reviewed by Yearbook editors and external reviewers. Based on the review results the best papers were then chosen at the selection meeting with the IMIA Yearbook editorial board. Text mining, and term co-occurrence mapping techniques were again used to get an overview of the content of the retrieved articles.

Results : The query was carried out in mid-January 2019, yielding a consolidated, deduplicated result set of 2,264 articles which had been published in 957 different journals. This year, we nominated twelve papers as candidates and three of them were finally selected as best papers in the CIS section. Again, the content analysis of the articles revealed the broad spectrum of topics which is covered by CIS research.

Conclusions : We could observe ongoing trends from our 2017 analysis. The patient increasingly moves in the focus of the research activities and trans-institutional aggregation of data is still an important field of work. The move to use patient and other clinical data directly for the patient and to support data driven process management, the move away from clinical documentation to patient-focused knowledge generation and support of informed decision, is gaining momentum by the application of new or already known but, due to technological advances, now applicable methodological approaches.