Big Data Analytics代写 Healthcare代写

Big Data Analytics代写

Big Data Analytics In Healthcare, It’s Potential And Challenges

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Big Data Analytics代写 This paper describes the promises, potentials, and challenges of big data and analytics in healthcare.

Abstract

Objective/Purpose: This paper describes the promises, potentials, and challenges of big data and analytics in healthcare. As there are more and more data generated in healthcare, there are vast possibilities provided with big data analytics.
Method: I conduct a literature review using thematic analysis focusing on the categorization of primary use cases of Big Data analytics in healthcare and main blocking factors on given use cases.
Results: The paper describes that by learning applications and its architectural framework, as well as the implementation of big data analytics in healthcare give considerable opportunities to improve the quality of the patient care, by reducing error and the cost spent for the healthcare.
Conclusion: The perspectives and opportunities of big data analytics in healthcare are tremendous; however, there are remaining challenges to be taken and resolved.

Keywords: Big data; Analytics; patient care; applications; methodology; healthcare; personalized medicine

Table of Contents  Big Data Analytics代写

Big Data Analytics in Healthcare, It’s Potential and Challenges ———————————–4

Chapter 1 ————————————4

1.1 Introduction ————————————4

1.2 Problem Statement ————————————5

1.3 Purpose of the Study ————————————5

1.4 Objectives of Research ————————————5

1.5 Research questions ————————————5

Chapter 2 ————————————6

2.1 Methodology ————————————6

Chapter 3 ————————————6

3.1 Literature Review ————————————6

3.1.1 Big Data Analytics ————————————6

3.1.2 Features of Big Data ————————————7

3.1.3 Big Data Analytics in Healthcare ————————————8

Chapter 4 ————————————15

4.1 Findings ————————————15

Chapter 5 ————————————20

5.1 Conclusion ————————————20

References ————————————22

Big Data Analytics in Healthcare, It’s Potential and Challenges

Chapter 1  Big Data Analytics代写

1.1 Introduction

The term big data is new in the 21st century, but there are a variety of definitions associated with the time. Most attempts view it as data that is complex, large, fast, and changing necessitating new mechanisms for storage, analysis,  and visualizations. As such, health care is one of the sectors that the four Vs of data, that is, variety, velocity, and volume can be applied because of the data produced. The reason being, healthcare data is widely distributed in systems, including insurers, researchers, government agencies, and other interest groups. The data from these entities are stored in databases in varieties of ways and manners that are difficult for categorization of data.

Notwithstanding the inherent complexities, there is potential and promises in siloed healthcare data.  Big Data Analytics代写

The data can be used to apply big data techniques to derive critical knowledge that proves essential in the development of the healthcare sector. According research by McKinsey Global Institute, is the U.S. were to harness the potential of big data, it would generate a value of over $300 billion every year (Manyika et al., 2011). One of the key benefits that the U.S. has missed is the reduction of healthcare expenditure. Traditional healthcare is based on the psychological changes that use single modality of that data that can be limiting and flawed. Big Data Analytics代写**范文

Although the approach is fundamental to the understanding of diseases, it does not recognize interconnectedness and variations that play a role in healthcare (Celi, Mark, Stone, and Montgomery, 2013). Additionally, the medical field has just started to integrate digital capabilities in data processing, storage, and communication in the last decade. The new era has made it possible for extensive data to be processed and stored. However, despite the implementation and use of digital equipment in healthcare, the data collected have not been utilized to yield real benefits.

1.2 Problem Statement  Big Data Analytics代写

There are numerous evidence-based researches on the application of big data analytics in healthcare processes. Each of these studies offers fundamental insight into the potentials and challenges of big data analytics in their distinct context of the application. Their findings are specific in use within the condition of the research conducted. Also, while each of these researches identifies the potential of big data application in healthcare, they fail to identify them in the context of specific healthcare cases. Therefore, they all miss to point out the promises of big data analytics in healthcare. Hence, a need for research focusing on overall promises, potential and challenges of big data analytics in healthcare.

1.3 Purpose of the Study

The use of systematic analysis is to establish a common finding on promises, potentials, and challenges of big data analytics in the whole healthcare system. It will summarise all the results from evidence-based researchers to a single document of reference. The research will also contribute to the knowledge of the implementation and application of big data analytics in diseases detection, patient care, and treatment.

1.4 Objectives of Research  Big Data Analytics代写

The paper seeks to find out promises, potential, and challenges of the big data analytics in the healthcare system.

1.5 Research questions

i. How has big data analytics been implemented and applied in disease treatment and prevention, patient care, as well as in research and development?

ii. What are the challenges that face big data analytics in healthcare?

iii. What are the promises and potentials of the big data analyticsapplication and implementation of healthcare?

Chapter 2  Big Data Analytics代写

2.1 Methodology

The paper conducted a literature review using thematic analysis focusing on the categorization of primary use cases of Big Data analytics in healthcare and main blocking factors on given use cases. Each case was analysed regarding the application of big data analytics, the promises it presents, potentials and likely challenges faced. The cases categories included big data analytics and medical image analysis, cancer, sickle cell anemia, telemedicine, and HIV/AIDs. The findings are based on articles results in each case. Each category of case findings was presented as either promise, potential or challenge. The findings informed the formulation of a conclusion about the application of big data analytics in healthcare.

Chapter 3  Big Data Analytics代写

3.1 Literature Review

3.1.1 Big Data Analytics

Figure 1 shows big data analytics as to the science of collection, analysis, and presentation of extensive data set to reveal the trends, patterns, and relationship in the data (Pouyanfar, Yang, Chen, Shyu, &   Iyengar,   2018). The overall goal of big data analytics in healthcare as shown in figure 2 is to improve and lower costs of treatment and care. Conventional databases have become inefficient in the management of data due to the massive data generated by advancement in technologies. Big Data Analytics代写**范文

According to Kubick, databases lack the capacity and capability to capture and process data in real-time (Kubick, 2012). The limitation in space with few databases having dozen of petabytes of data and that is exponentially increasing makes it challenging to process the data. Baseman et al. (2017) observed that storage, search, and analysis of data in databases is difficult using conventional techniques. Therefore, to do extensive data analytics, advanced methods like real-time analysis and visualisation are required.

Big Data Analytics代写
Big Data Analytics代写

 

 

 

 

 

 

 

 

 

Figure 1: Data collection and analysis. Adapted from Reddy and Sun (2013).

Big Data Analytics代写
Big Data Analytics代写

 

 

 

 

 

 

 

Figure 2: Primary objectives of big data analytics in healthcare. Adapted from Khalid and AbdelWahab (2016).

3.1.2 Features of Big Data  Big Data Analytics代写

Big data analytics is the amount of data that is large and diverse. There are advanced architecture, tools and analytics create significant value that makes it essential modern data analysis. A paper is written by Raghupathi andRaghupathi (2014) reveal the importance of data integrity for its impact on decision making. As such, data analytics has four commonly identified features including volume, variety, velocity and veracity as depicted in figure 3 below. Big Data Analytics代写**范文

According to McAfee and Brynjolfsson (2012), digital technologies have contributed extensive data and is continuously increasing. They also noted that the data come from many sources and hence of different type and usage. Digital technologies and various source of data combined increase the speed at which data is created and therefore fast techniques are needed to get meaningful insight from it (Oussous, Benjelloun, Ait Lahcen, & Belfkih, 2017). These features are an essential consideration in big data analytics for decision making.

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Figure 3: The Four Vs. of big data analytics. Adapted from Zanabria and Mlokozi (2018)

3.1.3 Big Data Analytics in Healthcare  Big Data Analytics代写
3.1.3.1 Medical Image Analysis

Aiello, Cavaliere, D’Albore, and Salvatore (2019) did a review on challenges of medical images. Medical imaging is essential for anthropometry and simulation which depend on big data analytics. They found out that diagnostic imaging data is not well-managed to utilize its full potential. Below figure 4 is the bioimaging workflow that is needed for beneficial big data analytics. Notably, there are numerous uncontrolled imaging data from autonomous and monolithic sources that reduce the potential of big data analytics. Big Data Analytics代写**范文

According to Lambin, et al. (2017) images lack high-level presentation for radionics and anthropometrics fields. It then becomes difficult to apply predictive and pattern, recognition models. Therefore, there is lack of enough credible bioimages data that can result in misleading decisions. The authors identified the challenge to be a lack of centralized data sharing center. They suggested a need to have controlled decision protocols that target to contribute and improve big data science.

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Figure 4: Adapted from Aiello, Cavaliere, D’Albore, and Salvatore, (2019)

3.1.3.2 Cancer and Big Data Analytics  Big Data Analytics代写
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An article by Chang reporting for Weill Cornell Medicine Research Center shows how Elemento’s lab uses big data analytics for cancer care and cure. According to Chang (2018), there are over 100 billion cells in a tumor with each of these cell having a distinct mutation process. The mutation ability of these cells makes the disease always to change, evolve and adapt. It is fundamental for researchers to know the genetical make-up of cancerous cells. Big Data Analytics代写**范文

When more frequent analysis of these tumors is done, the closer it is to the understanding of tumors. The continuous measurements of tumors result in data that is critical to Olivier Elemento research. The researcher uses big data generated over time identify cancer genome, understand their changes, and use the information to discover treatments.

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Moreover, the lab has developed a diagnostic model for thyroid cancer (Chang, 2018). They used available data to identify patterns in cancer cells. Machine learning algorithms were used to get trends. Elemento’s lab is also developing a database that will host cancer genome mutations from their research. The database offers the future potential of applying big data analytics in cancer treatment. Also, Capobianco (2017) saw big data analytics as promising research and development its application in precision oncology. The data on regions of interests and genomics has made understanding of cancer like never before. They have increased possibility of early cancer detection, and personalised treatment and hence makes it treatable.

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Despite the benefits, promises, and potentials of using big data analytics, there are inherent challenges. There are large volumes of genome data involved in the research (Bates, Saria, Ohno-Machado, Shah, and Escobar, 2014). It becomes a challenge to analyse 3 billion genomes per person in a study that needs thousand of samples. The massive data takes many days, weeks, or months to complete a single analysis. If the researcher decides to reduce the data to lower analysis time, information loss is high and may result in misinterpretation. The whole process can be frustrating to the researchers.

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Additionally, the article by Paniagua (2019) through Cancer World explains that although artificial intelligence and big data analytics are promising to the in tackling cancer, there is a caution to their use and application. The developments require rigorous studies and publication as well as clinical validations before they are assumed to be useful and implemented patient care. Also, statistical limitations have slowed the development of precision cancer prognosis, and more genomics and clinical data analysis are needed as explained by Ow and Kuznetsov, (2016). Big Data Analytics代写**范文

The challenges are as a result of substantial varieties of data that are difficult for stratification. Topol (2019) concluded that though the fields of AI and big data analytics are promising, there is relatively low data and proof of their usefulness. As such, there is a possibility of faulty algorithms and yet exist promises of reducing errors and inefficiencies in the future.

3.1.3.3 Big Data Analytics and HIV  Big Data Analytics代写

Moreover, big data analytics has proved valuable in HIV treatment. A research by Olatosi et al. (2018) medical care has remained a challenge due to low linkage and retention data. According to the research, the utilisation of big data science in the modelling of HIV care is critical in establishing patterns that facilitate decisions antiretroviral medications. Big Data Analytics代写**范文

As such, the authors describe the creation of data center for all South Carolina persons living with HIV (SC PLWH) and the implementation of big data analytics in the data for better and new insights in offering HIV care. Figure 5 below shows the revenue and fiscal affairs integrated data system. Figure 6 shows the linkage of various variables that constitute cascaded HIV care. The two figures, 5 and 6, are used in the HIV care utilisation predictive model shown in figure 7.

Big Data Analytics代写
Big Data Analytics代写

Figure 5: Adopted from Olatosi et al. (2018)

Big Data Analytics代写
Big Data Analytics代写

Figure 6: Gelberg-Anderson Model variables and data source. Adopted from Olatosi et al. (2018)

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Figure 7: Predictive model of HIV care utilisation. Adopted from Olatosi et al. (2018)

While the study recognised fundamental in profiling and compiling PLWH in South Carolina for creation of HIV care predictive model, there were inherent difficulties in it. Olatosi et al. (2018) encountered challenges in getting agreements, collection, and merging data from many sources. The considerations of data privacy and confidentiality was a subject of efforts and time which were proved to be limit factors. Further, the use of machine learning in predictive modeling, yield information that is difficult to interpret due to the complexity of raw data. Nonetheless, big data analytics have proven to be the most promising development in the developing treatment, care, and cure of HIV.

Young (2015) contributed to the impact of big data analytics in HIV treatment and prevention.  Big Data Analytics代写

According to Young, digital technologies including social media and smartphones, are a promising development in addressing HIV epidemic through bioinformatics, digital epidemiology, and disease modeling. The approach uses social media text mining which filters HIV related keywords and phrases which are used to model prevalence of HIV. Some of the high-risk social interactions include sexual and drug use behaviors. Young found that there exists a correlation between online HIV-related posts and CDC reports on HIV cases. Big Data Analytics代写**范文

Thus, social media has been used as a cost-effective method of monitoring and surveillance of HIV risk. The data is useful in HIV prevention and intervention in that it can be used in the provision of need-based home-HIV testing kits. In another study, Young and Jaganath (2013) found that those who use social platforms to post about HIV prevention and testing are more likely to use HIV self-testing kit. The data from social media analysis is of critical importance to the health departments for planning purposes and real-time response need-based HIV prevention and treatment.

However, the use of social media big data analytics came with criticism.

According to Lazer, Kennedy, King, and Vespignani (2014) highlighted that the data and method are questionable on their reliability and validity. These challenges need to be addressed before the application of big data analytics and decision making. The problem is further aggravated because government agencies, research institutes, and other health departments have limited capacity to handle big data generated from social media and digital technologies (Murdoch & Detsky 2013). Big Data Analytics代写**范文

It is also observed that data collected from an online post need to be updated frequently for the application of big data analytics. The real-time HIV data from databases is not usually accessible instantly, which limits the power of social media HIV monitoring of cases and prediction. Instead, it is used to show relationships between posts and HIV prevalence.

3.1.3.4 Big Data Analytics and Sickle Cell Anemia  Big Data Analytics代写

According to a systematic study conducted by Badawy et al. (2018), there is need for improved self-management results of patients with sickle cell anemia. The research relied on literature evaluations that focused on the application of technologies in the management of sickle cell disease. Despite their findings that the use of eHealth in the proper control of illness, all the research used showed concern about the availability data large enough for the studies to be feasible and acceptable. Therefore, continuous research was needed to evaluate the efficacy of self-management of a patient with sickle cell.

Moreover, the use of big data analytics and artificial intelligence are affected by biases.

A study by Lacy et al. (2017) established the existence of bias in hemoglobin A1c levels in sickle cell patients in the African Americas. They observed that “Among African Americans from 2 large, well-established cohorts, participants with SCT had lower levels of HbA1c at any given concentration of fasting or 2-hour glucose compared with participants without SCT.” They concluded that “HbA1c can underestimate past glycemia in African American patients with the sickle cell disease.” It was found that when variables in a large dataset are sampled repeatedly, there is a possibility of selection bias in reporting, which can show differences in typical values that otherwise does not exist.

3.1.3.5 Big Data Analytics and Telemedicine  Big Data Analytics代写

People have always imagined a cure for all diseases, extending life span, and improved general health of the world’s population. These are hopes that are more or less realistic in the era of advancement of big data analytics coupled with other health technologies that give rise fo telemedicine (Bairagi, 2017). It has made healthcare better by harnessing extensive patients data available. He established that the development of telemedicine had been made a reality by application of the big data analytics in healthcare. Big Data Analytics代写**范文

Telemedicine has impacted of increasing patient care, diagnosis, and treatment as well as reducing the cost of accessing healthcare. Healthcare providers use data analytics for diagnosis that is drawn from vast information that goes beyond personal experience and local resources. The process promotes accuracy in diagnosis and use of evidence-based practice efficiency. The future growth of telemedicine is highly dependent on the future of data analytics. That is, advancements in data analytics is a potential development in telemedicine.

Chapter 4  Big Data Analytics代写

4.1 Findings

This systematic review used a total of 14 articles. The study was divided into distinct categories of application of big data analytics in healthcare. The table below shows the types of cases used in a systematic review and the number of articles used as well as their conclusions. The article’s findings are tabulated further according to their results.

Cases of big data analytics Promises Potentials Challenges
Cancer Genome analysis for cancer cure

Discovery of treatments for cancer diseases

A more significant number of data on genomic for efficient application of big data analytics and predictive models

Monitoring of cells to detect cancer cells

Large volumes of genome data per patient sample that makes the process tedious and time-consuming.

There is no significant data to get conclusive analytics for cancer treatment.

Difficulty in interpreting machine learning outcomes due to their large sizes

Sickle cell anemia Big data analytics will continue to develop self-management Model variations in patients to predict the amount of hemoglobin A1c No availability data that is large enough for the studies to be feasible and acceptable

Presence of selective reporting biases.

HIV
Medical image analysis Development of anthropometry and simulation.

More no incision therapies in the future

Understanding of diseases.

Centralised image data sharing center.

There is a need to have controlled decision protocols

There are numerous uncontrolled imaging data from autonomous and monolithic source limit the credibility of biomedical images.
Telemedicine The new era of telemedicine where patients do not have to visit a health facility.

Free e-Medical sites that diagnosis and give recommended treatments

Extensive patient data that offers efficacy after the application of big data analytics

Promotion of accuracy in diagnosis.

Lack of ERH interoperability which can cause problems for medical practitioners

There are legal concerns like government regulations such as FDA which block licensing of telemedicine platforms.

Undoubtedly, the use of big data analytics will unlock the potential of healthcare.  Big Data Analytics代写

All article agree that that amount of data generated in healthcare is exponentially growing. Therefore, it will be about the ability to extract meaningful information from pools of data. Due to the growth and utilization of big data, the healthcare sector is booming, the number of patients increased, and innovative treatments have increased synonymously. Big Data Analytics代写**范文

The interests that health practitioners have taken on the implementation of big data analytics in various cases show that the sector has high potential to grow. Research by Market Watch (2018) predicts that the healthcare market will grow by up to $34.27 billion by 2022 at a CAGR of 22.07 per cent. These data will be stored in electronic health records, government agencies data, private research institutions, and other databases.

Generally, the findings from the articles can be summarized into five central promise and potentials of big data in healthcare as discussed below.
a. Promote health tracking  Big Data Analytics代写

Ranging from cancer treatment and care, bioimaging analysis, sickle cell anaemia and HIV, big data analytics is used together with internet of thing to revolutionize the way these healthcare processes are tracked. The article on cancer care revealed that big data is used in profiling tumours and cell to show their genomic make-up that causes mutation. Recent break-through in tumour cell patterns, as well as the use of machine-assisted non-incision treatments, are indication that big data is the answer to the cure of cancer epidemic. Also, the application of big data analytics in HIV prevention and monitoring using social media has reduced the spread of the virus. Health tracking is applied in variety of uses and give the patient control of their health.

b. Reduce healthcare cost  Big Data Analytics代写

The use of predictive analysis saves time in disease monitoring and treatment. The use of bioimage analysis has contributed to the control of diseases at an early stage. When cancer is discovered early, it saves on the cost of treatment and increases the chances of cure. The use of predictive analysis can also be used to cut the cost associated with the hospital readmissions. According to Kent (2019), more than 47 per cent of healthcare centres is using predictive analysis to save cost.

c. Reduce cases of high-risk diseases

Most of the articles reviewed call for digitization of healthcare processes to make available data that can be used in understanding patterns in patient care. For instance, by the utilization of big data analytics, physicians can quickly access diagnosis and hence save times and deterioration of patient health. Big data has facilitated customization of healthcare especially to the patient suffering from high-risk diseases like cancers, hypertension, cardiac arrest among others.

d. Prevent human errors  Big Data Analytics代写

There are cases that physicians can get diagnosis wrong and hence miss the required prescription and treatment. Big data analytics is more accurate than humans and is being leveraged to analyze patient data. It has been applied in cancer treatments and promising to yield more and better results in the future.

e. Advancement in the healthcare sector

The use of big data is promising to the progress in patient care and treatment. It is due to growth in data size that machine learning and artificial intelligence are also growing. The use of AI in medicine has made it possible to surf through numerous data within seconds to get solutions to various diseases.

On the other hand, the review has revealed inherent challenges in the use of big data analytics in healthcare. Some of the difficulties identified include:
Big Data Analytics代写
Big Data Analytics代写
a. Data capturing  Big Data Analytics代写

Most data in healthcare lack control on data governance. It has therefore been proved that capturing clean, complete, accurate, and formatted data in a multi-system difficult (Beaton, 2017). Most healthcare facilities use poor ERH that is convoluted with data and incomplete.

b. Data cleaning

Most that acquired from ERH are dirty. Therefore, they require cleaning, which might take a lot of time and resources. Some data cleaning also use sophisticated tools such as machine learning that though fast, they are expensive to implement.

c. Data storage  Big Data Analytics代写

The review also identified challenges of limited space for storing big data before and during analytics. For instance, when studying the human genome in cancer research, the storage capacity can be limiting. Bates, Saria, Ohno-Machado, Shah, and Escobar (2014) observed that there are large volumes of genome data involved in a single sample which translate to billion of data when many human samples are required.

d. Lack of interoperability

Additionally, data sharing is an essential part of big data analytics in healthcare. However, it is prone to a lack of data interoperability. The challenge is posed by the design and implementation of ERH in healthcare facilities. The failure of the system to share data between organizations and departments limit the potential of big data analytics that physicians depend on for critical decision.

Chapter 5  Big Data Analytics代写

5.1 Conclusion

The variety of health information systems used today have generated an unprecedented amount of patient data. This information if well-utilized can reveal important information that helps in formulation of health policies, create diagnosis and prescriptions and eliminate costs as well as redundancies. The process of extracting data from health data is the most critical in revealing the potential and promises of big data analytics. Big Data Analytics代写**范文

Despite significant challenges identified with the big data analytics, its current and future promises and potential out-weigh them. Healthcare sector should focus on using the data analytics to aid decision making and performance of healthcare. The rising volume of data forces data scientists to be innovative in solutions. Medical data analytics requires centralized control of medical data to enhance uniformity in storage, processing, formatting, and interoperability. Patient care processes should be carefully designed and stored in forms that are usable and acceptable in big data data analytics.

References  Big Data Analytics代写

A-L

Aiello, M., Cavaliere, C., D’Albore, A., & Salvatore, M. (2019). The challenges of diagnostic imaging in the era of big data. Journal of clinical medicine, 8(3), 316.

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.

Badawy, S. M., Cronin, R. M., Hankins, J., Crosby, L., DeBaun, M., Thompson, A. A., & Shah, N. (2018). Patient-centred eHealth interventions for children, adolescents, and adults with sickle cell disease: a systematic review. Journal of medical Internet research, 20(7), e10940.

Bairagi, V. K. (2017). Big data analytics in telemedicine: a role of medical image compression. In Big Data Management (pp. 123-160). Springer, Cham.

Beaton, T. (2017). Mismatched Symptoms Call EHR Data Integrity into Question. Retrieved from https://healthitanalytics.com/news/mismatched-symptoms-call-ehr-data-integrity-into-question

Capobianco, E. (2017). Precision Oncology: The Promise of Big Data and the Legacy of Small Data. Frontiers in ICT, 4, 22.

Celi, L. A., Mark, R. G., Stone, D. J., & Montgomery, R. A. (2013). “Big data” in the intensive care unit. Closing the data loop. American journal of respiratory and critical care medicine, 187(11), 1157.

Lacy, M. E., Wellenius, G. A., Sumner, A. E., Correa, A., Carnethon, M. R., Liem, R. I., … & Luo, X. (2017). Association of sickle cell trait with hemoglobin A1c in African Americans. Jama, 317(5), 507-515.

K-M  Big Data Analytics代写

Kruse, C. S., Goswamy, R., Raval, Y., & Marawi, S. (2016). Challenges and Opportunities of Big Data in Health Care: Systematic Review. JMIR Medical Informatics, 4(4), e38.

Kent, J. (2019). 60% of Healthcare Execs Say They Use Predictive Analytics. Retrieved from https://healthitanalytics.com/news/60-of-healthcare-execs-say-they-use-predictive-analytics

Khalid, B., & Abdelwahab, N. (2016). Big Data and Predictive Analytics: Application in Public Health Field.

Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., … & van Wijk, Y. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology, 14(12), 749.

Lazer, D., Kennedy, R., King, G., Vespignani, A. (2014). Big data. The parable of Google Flu: traps in big data analysis. Science, 343(6176):1203-5. doi:10.1126/science.1248506.

Manyika, M., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.

Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. JAMA, 309(13):1351-2. doi:10.1001/jama.2013.393.

O-Y  Big Data Analytics代写

Ow, G. S., & Kuznetsov, V. A. (2016). Big genomics and clinical data analytics strategies for precision cancer prognosis. Scientific reports, 6, 36493.

Olatosi, B., Zhang, J., Weissman, S., Hu, J., Haider, R. M., Li, X. (2018). Using big data analytics to improve HIV medical care utilization in South Carolina: A study protocol. BMJ Open, 9(7), 1-11. doi:10.1136/bmjopen-2018-027688.

Paniagua, E. (2019). Big data and precision medicine in cancer: challenges to face. Cancer World. Retrieved from https://cancerworld.net/cancerworld-plus/big-data-and-precision-medicine-in-cancer-challenges-to-face/

Reddy, C. K., & Sun, J. (2013). Big data analytics for healthcare. In Tutorial presentation at the SIAM International Conference on Data Mining, Austin, TX.

Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25, 44–56.

Young, S. D., Jaganath, D. (2013). Online social networking for HIV education and prevention: a mixed-methods analysis. Sexually Transmitted Diseases, 40(2):162-7. doi:10.1097/OLQ.0b013e318278bd12.

Young S. D. (2015). A “big data” approach to HIV epidemiology and prevention. Preventive medicine, 70, 17–18. DOI:10.1016/j.ypmed.2014.11.002

 

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我们只雇用最优秀的母语为英语的代写写手,他们拥有公认的名校和大学研究生及以上学位。你永远不会从艾莎得到不满意的服务。这是我们对您的承诺!

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