The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

The Internet of Things for Health Care: A Comprehensive Survey

Health care professionals' use of mobile devices is transforming clinical practice. Numerous medical software applications can now help with tasks ranging from information and time management to clinical decision-making at the point of care.

Mobile devices and apps for health care professionals: uses and benefits.

(1) Whereas the objectives of the Community, as laid down in the Treaty, as amended by the Treaty on European Union, include creating an ever closer union among the peoples of Europe, fostering closer relations between the States belonging to the Community, ensuring economic and social progress by common action to eliminate the barriers which divide Europe, encouraging the constant improvement of the living conditions of its peoples, preserving and strengthening peace and liberty and promoting democracy on the basis of the fundamental rights recognized in the constitution and laws of the Member States and in the European Convention for the Protection of Human Rights and Fundamental Freedoms;

Some Preliminary Comments on the DIRECTIVE 95/46/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.

Process mining in healthcare

This paper introduces a novel, region-growing algorithm for the fast surface patch segmentation of three-dimensional point clouds of urban environments. The proposed algorithm is composed of two stages based on a coarse-to-fine concept. First, a region-growing step is performed on an octree-based voxelized representation of the input point cloud to extract major (coarse) segments. The output is then passed through a refinement process. As part of this, there are two competing factors related to voxel size selection. To balance the constraints, an adaptive octree is created in two stages. Empirical studies on real terrestrial and airborne laser scanning data for complex buildings and an urban setting show the proposed approach to be at least an order of magnitude faster when compared to a conventional region growing method and able to incorporate semantic-based feature criteria, while achieving precision, recall, and fitness scores of at least 75% and as much as 95%.

Octree-based region growing for point cloud segmentation

Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

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A Systematic Review of Healthcare Applications for Smartphones

Background: The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method: We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results: The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions:

/pdf/data-mining-technologies-for-diabetes-a-systematic-review-35goiuyvwc.pdf

Data-Mining Technologies for Diabetes: A Systematic Review

Background: Studies have reported that patient-related factors significantly impact the risk of Chemotherapy-Induced Nausea and Vomiting (CINV). The objective of this study was to analyze those risk factors of CINV through a systematic literature review. Methods: We searched MEDLINE to identify articles that addressed patient-related risk factors of CINV through clinical studies. Results: A total of 49 articles were selected for this study. A total of 28 patient-related risk-factors that significantly impact the risk of CINV were documented. Three factors are demographically-related, 17 factors are intrinsic in nature and innate to patient’s physiology or influenced by physiology, and eight factors are extrinsic in nature. At least five studies identified seven risk factors with notable summary odds ratio: history of nausea/vomiting (odds ratio: 3.13, 95% CI 2.40 – 4.07, p<0.05), female sex (odds ratio: 2.79, 95% CI 2.26 – 3.44, p<0.05), expectancy of CINV (odds ratio: 2.61, 95%CI 1.69 – 4.02, p<0.05), younger age (odds ratio: 2.59, 95% CI 2.18 – 3.07, p<0.05), anxiety (odds ratio: 2.57, 95% CI 1.94 – 3.40, p<0.05), history of morning sickness (odds ratio: 1.97, 95% CI 1.46 – 2.65, p<0.05), and low alcohol intake (odds ratio: 1.94, 95% CI 1.68 – 2.24, p<0.05). Conclusions: Oncologists can use these factors prior to the initiation of a chemotherapy regimen to identify patients at risk for CINV, in order to focus on more comprehensive antiemetic treatment options for those high-risk patients. This may enable better outcomes and avoid complications.

Patient-Related Risk Factors for Chemotherapy-Induced Nausea and Vomiting: A Systematic Review.

Background: PubMed is the largest biomedical bibliographic information source on the Internet. PubMed has been considered one of the most important and reliable sources of up-to-date health care evidence. Previous studies examined the effects of domain expertise/knowledge on search performance using PubMed. However, very little is known about PubMed users’ knowledge of information retrieval (IR) functions and their usage in query formulation. Objective: The purpose of this study was to shed light on how experienced/nonexperienced PubMed users perform their search queries by analyzing a full-day query log. Our hypotheses were that (1) experienced PubMed users who use system functions quickly retrieve relevant documents and (2) nonexperienced PubMed users who do not use them have longer search sessions than experienced users. Methods: To test these hypotheses, we analyzed PubMed query log data containing nearly 3 million queries. User sessions were divided into two categories: experienced and nonexperienced. We compared experienced and nonexperienced users per number of sessions, and experienced and nonexperienced user sessions per session length, with a focus on how fast they completed their sessions. Results: To test our hypotheses, we measured how successful information retrieval was (at retrieving relevant documents), represented as the decrease rates of experienced and nonexperienced users from a session length of 1 to 2, 3, 4, and 5. The decrease rate (from a session length of 1 to 2) of the experienced users was significantly larger than that of the nonexperienced groups. Conclusions: Experienced PubMed users retrieve relevant documents more quickly than nonexperienced PubMed users in terms of session length. [JMIR Med Inform 2015;3(3):e25]

Analysis of PubMed User Sessions Using a Full-Day PubMed Query Log: A Comparison of Experienced and Nonexperienced PubMed Users.

https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1049&context=scschcomart

Abu Saleh Mohammad Mosa

Papers

A Systematic Review of Healthcare Applications for Smartphones

Data-Mining Technologies for Diabetes: A Systematic Review

Patient-Related Risk Factors for Chemotherapy-Induced Nausea and Vomiting: A Systematic Review.

Analysis of PubMed User Sessions Using a Full-Day PubMed Query Log: A Comparison of Experienced and Nonexperienced PubMed Users.

Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS