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Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics


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NoSQL databases that are categorized into four main types:

      1 1. Those based on the keys (Key-value).

      2 2. In-memory (Family) in which column-store (file-family).

      3 3. Document-store (Data-memory).

      4 4. Graph-store (Data in memory).

      It’s good for simple data that is only read rarely, but has the potential to expand to contain more data because of its expandability. It keeps vast amounts of data in one column family; in other words, the column family database stores huge numbers of individual columns all at once. The semi-structured data contains vast amounts of information pertaining to document formats, with regard to the documents in it, or data (opinions, theories, opinions, or interpretations). The last thing on the graph is the inversion of an N-to-M relationship, which is a Q-to-M relationship which is recorded as an N-to-entry database.

      1.5.4 Framework for Reconstructing Epidemiological Dynamics (FRED)

      It is an open-source modeling that can apply to a wide variety of disease patterns. Every resident, office holder, owner, and entity location (entities which hold locations or locations which hold entities) is listed in the regional economic data system FRED. Each agent is distinguished by both by their personal sociodemographic features (such as whether they are working, have a sex, and reside in a particular residence) and their daily activities (their occupational, for example). The experimental populations that are used in the FRED simulation to work out the potential spread of disease are called artificial populations.

      1.5.5 Advanced Risk and Disease Management

      Measuring and identifying factors such as genes, proteins, cell membrane and organ systems, the immunology of specific diseases, and epidemiology can also expand their capacity for care by reducing operating more economically while improving the quality of data management costs across the healthcare field.

      1.5.6 Digital Epidemiology

      There is a form of epidemiology known as Digital Epidemiology that incorporates digital methods from data collection to analyze data. It boosts epidemiological methods, such as case reports, control group studies, and ecologic studies. It makes use of case studies, ecological studies, and crosstype studies that include cases in its investigation and a mix of controlled trials and cohort studies, such as separate cohorts and ecological case studies. It also makes use of data sourced from other sectors such as data sets that were originally developed for health purposes or information sets.

      1.5.7 Internet of Things (IoT)

      There was previously only one model for patients interacting with doctors i.e. in person and via telephone or tele and text messaging. There was no way that doctors and hospitals could monitor patient health continuously, and thus be able to give prescriptions appropriately.

      It is almost certain that the healthcare industry will be changed by how it connects with devices and the physical bodies of people by means of Internet of Things. It has applications in the healthcare industry, as well as being beneficial to patients, family members, physicians and hospitals.

       1.5.7.1 IoT for Health Insurance Companies

      Healthcare devices are rapidly becoming more connected, and thus many approaches are necessary to deal with the various scenarios that may arise from that. A health-monitoring device can be used to assist in insurance under writing and operational tasks, for example, is it possible for insurance companies to leverage that data providing this information will help them detect and evaluate potential clients’ claims of fraud as well as identify those who could benefit from this method of treatment.

      Insurance Information Technologies (IIT) have another significant benefit for customers. Not only are they utilized for introducing standard under writing, pricing, but they are also utilized for risk assessment. Better visibility means customers can view the information used in every decision, fostering data driven decisions. This allows companies to conduct in-based thinking in all aspects of their organization, which increases customers’ comprehension of the thought behind every decision.

      Many insurance companies are researching incentives that would reward customers for utilizing and contributing to the health data generated by IoT (Internet of Things) devices. There are various potential approaches for better treatment compliance and more substantially compliant customers who use IoT devices. They could offer these services in exchange for their measured activity, which is something they have control over. This will also assist insurance companies, as they work to reduce their liability claims. As with the devices that collect data from the Internet of Things, this type may also handle claims for insurance companies, as it is feasible that they can prove payment claims for the insurance firms’ involvement.

       1.5.7.2 IoT for Physicians

       1.5.7.3 IoT for Hospitals

      Many patients in the healthcare facilities will benefit from IoT, but there are also many things that will be more intricately tracked, such as wellness and medical issues, which can be effectively maintained with IoT implementation. It is One Instrument/Remotely-transitioning devices, like wheelchairs, nebulizers, and blood sensors that are useful for locating these kinds of equipment on the one time scale, as they can be monitored and tracked real time, time with sensors such as the Sensor Modules. Doctors, patients, sensors, and access to state-of-the-art equipment technology such as X-ray imaging can get quick, accurate results, regardless of their distance from one another.

      Patients should be aware of the potential infection spread because it is significant. Useful in providing a hygienic (safe) patient monitoring devices or associated with a hygienic (prophylactic) hygiene reduces infection. Asset items such as smartphones and environmental control are frequently used for things like checking temperatures and humidity levels, for instance, for example

       1.5.7.4 IoT for Patients