Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM)

Machine Learning (ML) is considered as one of the contemporary approaches in predicting, identifying, and making decisions without having human involvement. ML is quickly evolving in the medical industry ranging from diagnosis to visualization of diseases and the study of disease transmission. These algorithms were developed to identify the problems in medical image processing. Numerous studies previously attempted to apply these algorithms on MRI (Magnetic Resonance Image) data to predict AD (Alzheimer's disease) in advance. The present study aims to explore the usage of support vector machine (SVM) in the prediction of dementia and validate its performance through statistical analysis. Data is obtained from the Open Access Series of Imaging Studies (OASIS-2) longitudinal collection of 150 subjects of 373 MRI data. Results provide evidence that better performance values for dementia prediction are achieved by low gamma (1.0E-4) and high regularized (C = 100) values. The proposed approach is shown to achieve accuracy and precision of 68.75% and 64.18%. Keywords: Machine learning, OASIS, Support vector machines, Kernel, Gamma, Regularization (C

Performance analysis of different machine learning algorithms in breast cancer predictions

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Second wave of COVID-19 in Italy: Preliminary estimation of reproduction number and cumulative case projections

The second wave of a novel coronavirus in Italy has caused 247,369 new cases and 1782 deaths only in October 2020. This significantly alarming infectious disease controlling board to impose again mitigation measures for controlling the epidemic growth. In this paper, we estimate the latest COVID-19 reproduction number (R_0) and project the epidemic size for the future 45 days. The R_0 value has calculated as 2.83 (95% CI: 1.5-4.2) and the cumulative incidences 100,015 (95% CI; 73,201-100,352), and daily incidences might be reached up to 15,012 (95% CI: 8234-16,197) respectively

Model discovery, and replay fitness validation using inductive mining techniques in medical training of CVC surgery

Medical training is a foundation on which better health care quality has been built. Freshly graduated doctors have required a good knowledge of practical competencies, which demands the importance of medical training activities. As of this, we propose a methodology to discover a process model for identifying the sequence of medical training activities that had implemented in the installation of a Central Venous Catheter (CVC) with the ultrasound technique. A dataset with twenty medical video recordings were composed with events in the CVC installation. To develop the process model, the adoption of process mining techniques of infrequent Inductive Miner (iIM) with a noise threshold value of 0.3 had done. A combination of parallel and sequential events of the process model was developed. Besides, process conformance was validated with replay fitness value about 61.1%, and it provided evidence that four activities were not correctly fit in the process model. The present study can assist upcoming doctors involved in CVCs surgery by providing continuous training and feedback on better patient care

COVID-19 outbreak reproduction number estimations and forecasting in Marche, Italy

Background: COVID-19 disease is becoming a global pandemic and more than 200 countries were affected because of this disease. Italy is one of the countries is largely suffered with this virus outbreak, and about 180,000 cases (as of 20 April 2020) were registered which explains the large transmissibility and reproduction case numbers. Objective: In this study, we considered the Marche region of Italy to compute different daily transmission rates (Rt) including five provinces in it. We also present forecasting of daily and cumulative incidences associated after the next thirty days. The Marche region is the 8th in terms of number of people infected in Italy and the first in terms of diffusion of the infection among the 4 regions of the center of Italy. Methods: Epidemic statistics were extracted from the national Italian Health Ministry website. We considered outbreak information where the first case registered in Marche with onset symptoms (26 February 2020) to the present date (20 April 2020). Adoption of incidence and projections with R statistics was done. Results: The median values of Rt for the five provinces of Pesaro and Urbano, Ancona, Fermo, Ascoli Piceno, and Macerata, was 2.492 (1.1-4.5), 2.162 (1.0-4.0), 1.512 (0.75-2.75), 1.141 (1.0-1.6), and 1.792 (1.0-3.5) with 95% of CI achieved. The projections at end of 30th day of the cumulative incidences 323 (95% CI), and daily incidences 45 (95% CI) could be possible. Conclusions: This study highlights the knowledge of essential insights into the Marche region in particular to virus transmission dynamics, geographical characteristics of positive incidences, and the necessity of implementing mitigation procedures to fight against the COVID-19 outbreak

Development of physical training smartphone application to maintain fitness levels in seafarers

Background: In recent years, the prevention of non-communicable diseases represents one of the main problems of preventive medicine. Significant risk factor for these diseases is sedentary lifestyle; in other words, lack of physical activity. It is happened, especially in seafarers, since they do not have much facilitates to do physical exercise on board. The present study is designed to develop a simple user-guide mobile application to conduct activities with available equipment on board a ship.  Materials and methods: We held two pilot tests for app evolution. In the first phase, we selected members (n = 13) and produced a questionnaire related to usability, feasibility, and accessibility of the app. Based on the responses from users, we developed the second version of the app and provided to (n = 15) random seafarers for testing and operating.  Results: On average, 93.3% of seafarers mentioned that app was easy to use, while in the first phase it was equal to 84.6%. At the same time, 89.9% of users were satisfied with feasibility, and we had accomplished 95% satisfaction rate in the second phase. Ultimately, we had achieved better responses in the second evolution phase when compared with the first phase.  Conclusions: This app is made for planning a quality physical activity program for seamen that allows a seafarer to choose the adequate activity in line with his physical characteristic, fitness level, and motivations.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

Objectives: High blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine. Design: A cross-sectional epidemiological study was employed. Setting: This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires. Participants: This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria. Outcome measures: We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics. Results: Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively. Conclusion: A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships

Text mining with sentiment analysis on seafarers' medical documents

Abstract Digital health systems contain large amounts of patient records, doctor notes, and prescriptions in text format. This information summarized over the electronic clinical information will lead to an improved quality of healthcare, the possibility of fewer medical errors, and low costs. Besides, seafarers are more vulnerable to have accidents, and prone to health hazards because of work culture, climatic changes, and personal habits. Therefore, text mining implementation in seafarers' medical documents can generate better knowledge of medical issues that often happened onboard. Medical records are collected from digital health systems of Centro Internazionale Radio Medico (C.I.R.M.) which is an Italian Telemedical Maritime Assistance System (TMAS). Three years (2018–2020) patient data have been used for analysis. Adoption of both lexicon and Naive Bayes' algorithms was done to perform sentimental analysis and experiments were conducted over R statistical tool. Visualization of symptomatic information was done through word clouds and 96% of the correlation between medical problems and diagnosis outcome has been achieved. We validate the sentiment analysis with more than 80% accuracy and precision

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

Factors affecting the quality and reliability of online health information

Background: Internet represents a relevant source of information, but reliability of data that can be obtained by the web is still an unsolved issue. Non-reliable online information may have a relevance, especially in taking decisions related to health problems. Uncertainties on the quality of online health data may have a negative impact on health-related choices of citizens. Objective: This work consisted in a cross-sectional literature review of published papers on online health information. The two main research objectives consisted in the analysis of trends in the use of health web sites and in the quality assessment and reliability levels of web medical sites. Methods: Literature research was made using four digital reference databases, namely PubMed, British Medical Journal, Biomed, and CINAHL. Entries used were “trustworthy of medical information online,” “survey to evaluate medical information online,” “medical information online,” and “habits of web-based health information users”. Analysis included only papers published in English. The Newcastle Ottawa Scale was used to conduct quality checks of selected works. Results: Literature analysis using the above entries resulted in 212 studies. Twenty-four articles in line with study objectives, and user characteristics were selected. People more prone to use the internet for obtaining health information were females, younger people, scholars, and employees. Reliability of different online health sites is an issue taken into account by the majority of people using the internet for obtaining health information and physician assistance could help people to surf more safe health web sites. Conclusions: Limited health information and/or web literacy can cause misunderstandings in evaluating medical data found in the web. An appropriate education plan and evaluation tools could enhance user skills and bring to a more cautious analysis of health information found in the web