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 teleme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/3/124 |
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| author | Nalini Chintalapudi Ulrico Angeloni Gopi Battineni Marzio di Canio Claudia Marotta Giovanni Rezza Getu Gamo Sagaro Andrea Silenzi Francesco Amenta |
| author_facet | Nalini Chintalapudi Ulrico Angeloni Gopi Battineni Marzio di Canio Claudia Marotta Giovanni Rezza Getu Gamo Sagaro Andrea Silenzi Francesco Amenta |
| author_sort | Nalini Chintalapudi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:07:00Z |
| format | Article |
| id | doaj.art-ac7da23d89d348c6b1739c17d8c2e32b |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T20:07:00Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-ac7da23d89d348c6b1739c17d8c2e32b2023-11-24T00:30:27ZengMDPI AGBioengineering2306-53542022-03-019312410.3390/bioengineering9030124LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text MiningNalini Chintalapudi0Ulrico Angeloni1Gopi Battineni2Marzio di Canio3Claudia Marotta4Giovanni Rezza5Getu Gamo Sagaro6Andrea Silenzi7Francesco Amenta8Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyGeneral Directorate of Health Prevention, Ministry of Health, 00144 Rome, ItalyClinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyClinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyGeneral Directorate of Health Prevention, Ministry of Health, 00144 Rome, ItalyGeneral Directorate of Health Prevention, Ministry of Health, 00144 Rome, ItalyClinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyGeneral Directorate of Health Prevention, Ministry of Health, 00144 Rome, ItalyClinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyGenerally, 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.https://www.mdpi.com/2306-5354/9/3/124seafarerstext mininglasso regressiondisease mappingcorrelations |
| spellingShingle | Nalini Chintalapudi Ulrico Angeloni Gopi Battineni Marzio di Canio Claudia Marotta Giovanni Rezza Getu Gamo Sagaro Andrea Silenzi Francesco Amenta LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining Bioengineering seafarers text mining lasso regression disease mapping correlations |
| title | LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining |
| title_full | LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining |
| title_fullStr | LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining |
| title_full_unstemmed | LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining |
| title_short | LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining |
| title_sort | lasso regression modeling on prediction of medical terms among seafarers health documents using tidy text mining |
| topic | seafarers text mining lasso regression disease mapping correlations |
| url | https://www.mdpi.com/2306-5354/9/3/124 |
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