Data Mining Practical Machine Learning Tools and Techniques

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.

Historically, most demographic data for states and substate areas were collected from the long version of the decennial census questionnaire. A “snapshot” of the characteristics of the population on the April 1 census date was available once every 10 years. The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005. Instead of a snapshot in which all of the data are gathered at one time, the ACS aggregates data collected over time, making the results more difficult to interpret. However, the ACS data are updated annually.

A practical guide to the American Community Survey (3-year estimates)

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

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered “unhackable”. Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients’ privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an “allowed list” for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

/pdf/a-patient-centric-health-information-exchange-framework-55emqzw4mv.pdf

A Patient-Centric Health Information Exchange Framework Using Blockchain Technology

"Blockchain" is a distributed ledger technology originally applied in the financial sector. This technology ensures the integrity of transactions without third-party validation. Its functions of decentralized transaction validation, data provenance, data sharing, and data integration are a good fit for the needs of health information exchange and clinical trials. We investigated the current workflow of Health Information Exchange and clinical trials; conducted design thinking processes with clinicians, trial managers, informaticians, and blockchain professionals; and implemented a private blockchain model to tackle known issues. We used coded Smart Contract regulations to simulate several scenarios in healthcare processes. This proof-of-concept work provides a feasible simulation for potential solutions to monitor clinical trials across different census regions persistently. Various levels of data access privileges have been designed to utilize a suite of customized Smart Contract settings. These settings emulate the workflow protocols for the monitoring entities, trial sponsors, clinical sponsors and participating subjects. Keywords: Blockchain, Smart Contract, Health Information Exchange, Clinical Trial, Persistent Monitoring.

Applying Blockchain Technology for Health Information Exchange and Persistent Monitoring for Clinical Trials.

Patient socioeconomic data is not usually included in medical records nor easily accessible to clinicians, yet socioeconomic disadvantage can be an important guide to disease management This study evaluated the neighborhood-level Area Deprivation Index (ADI), a measure of neighborhood socioeconomic disadvantage, as a factor in diabetes mellitus prevalence Electronic health records at an academic hospital system identified 4,770 Medicare beneficiaries Logistic regression of diabetes diagnosis (ICD9=250x) against ADI quintile, age, gender, and race/ethnicity found all these patient characteristics to be significantly associated Diabetes prevalence was lowest in the least disadvantaged quintile of neighborhoods after adjusting for age, gender, and race/ethnicity The positive non-linear association of diabetes prevalence with ADI demonstrates the power of this index to practically quantify socioeconomic disadvantage The ADI may be suitable for clinical decision support, and for informing the policy changes which are needed to reduce socioeconomic disparities in diabetes prevalence and other health outcomes

The Effect of Neighborhood Disadvantage on Diabetes Prevalence.

Purpose Confounding of racial, socioeconomic, and behavioral characteristics may lead to the misidentification of race as a risk factor for certain diseases. The authors evaluated the validity of attributing race as a risk factor in a widely used pathology textbook. Method In 2009-2010, the authors searched Robbins and Cotran Pathologic Basis of Disease, Professional Edition (8th ed) for assertions that African Americans have a different disease profile than do individuals of other races. They evaluated the references cited in the textbook, assessing the quality of the sources and whether those authors controlled for confounding variables, such as socioeconomic status and behavioral characteristics. For statements that remained unconfirmed, the authors searched the literature for evidence supporting or contradicting the claims made in the textbook. The authors classified each statement from the textbook as supported, unconfirmed, or contradicted. Results Of the 31 relevant statements, 11 were supported by the literature review, 17 remained unconfirmed, and three were contradicted. Conclusions About two-thirds of the assertions that different risk factors exist for African Americans found in a widely used pathology textbook could not be supported by the published literature. Failure to separate race from other socioeconomic and behavioral characteristics as risk factors for certain diseases may contribute to the misdiagnosis of patients and interfere with efforts to identify and remove causes of health disparities.

Lincoln Sheets

Papers

A Systematic Review of Healthcare Applications for Smartphones

A Patient-Centric Health Information Exchange Framework Using Blockchain Technology

Applying Blockchain Technology for Health Information Exchange and Persistent Monitoring for Clinical Trials.

The Effect of Neighborhood Disadvantage on Diabetes Prevalence.

Unsupported labeling of race as a risk factor for certain diseases in a widely used medical textbook.