Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Application of Ethics for Providing Telemedicine Services and Information Technology

INTRODUCTION

Advanced technology has increased the use of telemedicine and Information Technology (IT) in treating or rehabilitating diseases. An increased use of technology increases the importance of the ethical issues involved. The need for keeping patients' information confidential and secure, controlling a number of therapists' inefficiency as well as raising the quality of healthcare services necessitates adequate heed to ethical issues in telemedicine provision.

AIM

The goal of this review is gathering all articles that are published through 5 years until now (2012-2017) for detecting ethical issues for providing telemedicine services and Information technology. The reason of this time is improvement of telemedicine and technology through these years. This article is important for clinical practice and also to world, because of knowing ethical issues in telemedicine and technology are always important factors for physician and health providers.

MATERIAL AND METHODS

the required data in this research were derived from published electronic sources and credible academic articles published in such databases as PubMed, Scopus and Science Direct. The following key words were searched for in separation and combination: tele-health, telemedicine, ethical issues in telemedicine. A total of 503 articles were found. After excluding the duplicates (n= 93), the titles and abstracts of 410 articles were skimmed according to the inclusion criteria. Finally, 64 articles remained. They were reviewed in full text and 36 articles were excluded. At the end, 28 articles were chosen which met our eligibility criteria and were included in this study.

RESULTS

Ethics has been of a great significance in IT and telemedicine especially the Internet since there are more chances provided for accessing information. It is, however, accompanied by a threat to patients' personal information. Therefore, suggestions are made to investigate ethics in technology, to offer standards and guidelines to therapists. Due to the advancement in technology, access to information has become simpler than the past. This has prompted hackers to seize the opportunity.

DISCUSSION

This research shows that the ethical issues in telemedicine can be investigated from several aspects like technology, doctor-patient relationship, data confidentiality and security, informed consent, patient's and family's satisfaction with telemedicine services. Following ethical issues in telemedicine is a primary aspect of high quality services. In other words, if therapists abide by ethical rules, they can provide better services for patients. Attention to ethical issues in telemedicine guarantees a safer use of the services.

Genome-wide copy number variation pattern analysis and a classification signature for non-small cell lung cancer

The accurate classification of non-small cell lung carcinoma (NSCLC) into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is essential for both clinical practice and lung cancer research. Although the standard WHO diagnosis of NSCLC on biopsy material is rapid and economic, more than 13% of NSCLC tumors in the USA are not further classified. The purpose of this study was to analyze the genome-wide pattern differences in copy number variations (CNVs) and to develop a CNV signature as an adjunct test for the routine histopathologic classification of NSCLCs. We investigated the genome-wide CNV differences between these two tumor types using three independent patient datasets. Approximately half of the genes examined exhibited significant differences between LUAD and LUSC tumors and the corresponding non-malignant tissues. A new classifier was developed to identify signature genes out of 20 000 genes. Thirty-three genes were identified as a CNV signature of NSCLC. Using only their CNV values, the classification model separated the LUADs from the LUSCs with an accuracy of 0.88 and 0.84, respectively, in the training and validation datasets. The same signature also classified NSCLC tumors from their corresponding non-malignant samples with an accuracy of 0.96 and 0.98, respectively. We also compared the CNV patterns of NSCLC tumors with those of histologically similar tumors arising at other sites, such as the breast, head, and neck, and four additional tumors. Of greater importance, the significant differences between these tumors may offer the possibility of identifying the origin of tumors whose origin is unknown.