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Data Science in Healthcare

Introduction

All the industries in the world today are run by data science. Being a vast field it has various applications relating to different aspects of life including healthcare, machine learning, road travel, sports and many others. Data science is growing its roots in almost each sector and it is one of those technologies that is carrying out innovative ideas and revolutionizing in each aspect (John & Pierscionek, 2017).

Data science in healthcare has improves a lot and this has brought positive outcomes in medical field including life saving outcomes and machine learning equipment for scientists. In healthcare, data science assists to cure deadly diseases, prevent epidemics and bring new ways in treatment of diseases with innovation in technologies. The management of hospital records and the performance of different machinery at the medical centers are all included in this category.

The data scientists are now making vast modifications in technology to bring their best in the medical field. Patients are getting advices online and dealing with best doctors in the world. Many people seek help through data science by booking appointments through Zocdoc, that is the best tool and a convenient way to interpret data. Data science is helpful in monitoring health of patient and diagnosing the disease at early stage using the data records (Zuhaerah, Tohir, Nguyen, Shankar, & Rahim, 2019).

Applications of Data Science in Healthcare

Data science in healthcare is improving day by day and there are various applications including medical imaging, electronic health records, drug discovery and research, telemedicine, augment cancer treatments, risk and disease management, improved strategic plans and many others (Rani & Govrdhan, 2010).

Big Data in Healthcare

Medical Image Analysis

The first and foremost use of data science in healthcare field is medical imaging. Different types of medical imaging techniques have now been carried out such as X-ray, MRI, ultrasounds and CT scans of body parts of patients. According to a survey, about 600 million processes for medical imaging are performed in US each year. The medical imaging is expensive and time taking procedure with efficient results to diagnose the disease at early stages. Many of the radiologists are earning handsome through this and the medical centers are keeping records of their patients (Dinggang, Wu, & Suk, 2017).

In ancient times, doctors tried to examine the patients manually by inspecting these images and a lot of irregularities were also found within them. With the help of data technologies in the modern era it is now possible to interpret the microscopic deformities in patients through the help of scanned images taken through advanced machinery and equipment. 

Advances in Diagnostic Medical Image Analysis

There are various types of medical imaging in data science including tomography, mammography, PET (positron emission tomography) and so on. There are three common algorithms that are required for analyzing the medical images:

  • Anomaly Detection Algorithm: This algorithm helps to recognize the bone fractures and dislocation of bones in patients (Justin, Wang, Rao, & Lim, 2017).
  • Image Processing Algorithm: Image Processing is used for interpreting and analyzing the images taken and examining and diagnosing the diseases.
  • Descriptive Image Recognition Algorithm: This algorithm extracts the data from the images and visualize it to analyze. It also helps to merge them to create a bigger picture.

Electronic Health Records (EHRs)

The EHR is the most extensive application in medical field. The hospitals and medical centers need to keep records of the patients including their medical history, information related to their demographics, library tests results and many more. Each digital record is provided with a code and file number that can be modified and renewed with the passage of time. These records are kept secured and confidential from other public and private sectors as these consist of personal information related to the patient (Sebastian, Savulescu, & Sahakian, 2016).

Easy Access to EHRs for Patients

EHRs are quite helpful in tracking the prescriptions to the patients to locate if they are focusing on the orders of doctor or not. These also help to trigger warning when a patient is required to get a new lab test according to his health. According to the research of HITECH 94% of medical centers have adopted the electronic health records in US. The EHRs can be modified by the doctor at any time and there is no need of paper work to be done as all the prescription is kept and recorded digitally with the help of data science.

Drug Discovery and Research

The drug discovery is a vast and complicated discipline in data science. This is a time consuming application that require massive financial resources and expenditures to carry out research and create drugs for treatment of different diseases in people. It has become a competition and challenge for medical institutes to create medicines and vaccines for treatment of diseases in limited time. A number of various test cases are needed to carry out by the researchers to understand the characteristics of medicines and the causative agents added in them. Numerous tests are also required to interpret and analyze the formulas used in the creation of medicines (Enrico, et al., 2020).

Medical Field Discovery and Research

All the tests to assess the performance of medicines for treatment of patients required approximately 10 to 12 years in ancient times but now as everything has been advanced through data science this has become easier to discover drugs. Machine learning algorithms have improved a lot and carried out innovative ideas for optimizing and expanding the success rate of drug discovery techniques. These algorithms also assist in simulating the action of drugs within the human body that otherwise requires long time to carry out experimentations in laboratory.

Different types of historical data about the drug discovery for a specific disease is recognized and various development processes are performed to bring innovation in drug development procedures. The genetic research is correlated with drug-protein database and various results are observed through this process. The researchers can modify the historical data through genetic mutations to discover drugs (Hongming, Engkvist, Wang, Olivecrona, & Blaschke, 2018).  

Augmenting Cancer Treatments

Data science is helpful in diagnosing the tumors in cancer patients and augmenting treatments for such a deadly disease with a lot of innovations in the previous treatment methods. The cancerous cells in the body of patient can be detected through medical imaging such as X-rays, ultrasounds and MRI of patients. The medical researchers  are required to large amount of data including the historical findings of old times and the data related to modern technologies to carry out results in identifying the treatment methods of cancer (Bowen, Ding, Chen, & Shi, 2020).

Augmenting Cancer Treatments

The researchers can recognize the tumor samples of patients in biobank which are directly connected to the health records of patients. Utilizing this information, the scientists can bring certain mutations in the treatment and find new ideas that will lead to better outcomes in patient recovery. Data science related information may astonish the scientists through unexpected benefits such as different types of antidepressant medicines could be consumed by patients to cure various types of lung cancer. 

The treatments for cancer patients is carried out by collecting their demographic and health records from medical centers and hospitals making sure that the data would be kept confidential. Other methods may include sequencing of cancerous tissues and cell samples from patients and carrying out treatment processes on them at trial bases to bring innovation in cancer treatments. Most importantly the biopsy reports of patients are great source of help for the data scientists to augment the treatment procedures for cancer patients on the basis of new technology.

Predictive Analytics in Healthcare

According to the data science insights, it is most significant to gather information about important factors of healthcare of patients to carry out processes. The information about patient’s health will define the condition of certain disease diagnosed to him and researchers would go for collecting data about patients from their health records such as blood pressure, sugar level and temperature of patient. This data will provide the scientist with current situation of health of patient and through this data they will recognize the stages and symptoms of diseases in patient (Louis, et al., 2020).

Predictive Analytics in Healthcare

Interpreting the symptoms of disease, the predictive analytics model would be helpful to create the predictions about health conditions of patients. This would also assist in analyzing strategies for treatment of patient on the basis of medical conditions of person. There are a lot of benefits for predictive analytics in healthcare such as:

  • Predictive analysis would assist in managing chronic diseases in patient.
  • It would help to monitor and evaluate the pharmaceutical logistics.
  • For helping maximum amount of patients it helps in getting appointment with efficient doctors and other online resources (Carson, et al., 2020).
  • The preventive measures can be discussed with patients by predicting their current conditions on the basis of health records.
  • This also provides information about early treatments for patients according to symptoms diagnosed and minimizing the risk of further damage to the health of patients.

Data Science and Genomics

The genomics and genetics are required to enhance advanced level treatment for various types of diseases. The impact of DNA is understood and different biological connections can be identified between the genetics of patients, their diseases that have been diagnosed and the treatments or drug response prescribed by the doctor. The research on genes of the patient would provide with an insight of the health conditions of patient and the drug response is analyzed on the basis of the type of DNA that the patient is having (Suparna, Fishman, McGowan, & Juengst, 2014).

Data Science and Genomics

This research could be time consuming as there are numerous pairs of DNA cells in the human body but it has made the task easier for the data scientists to diagnose the diseases in patients through studying the genome structure. There ae different kinds of tools that might be needed in research of genomics that are discussed below:

  • MapReduce: It is a programming model that consists of map procedures such as filtering and reduce methods to carry out operations. This helps to process the genetic data of patients that is collected in huge amounts by the data scientists. The MapReduce will assist to process the genetic sequence of DNA in lesser time.
  • SQL: As the name implies SQL (Structured Query Language) is a language that is specified to domains and utilized in programming for managing the data. It helps in getting back or recapturing the genomic data from various databases and applying logical methods on this data.
  • Galaxy: It is a source for biomedical data interpretation on the basis of web-based platforms. The galaxy is basically GUI based application that helps to perform tasks on the basis of biomedical research on genomes. 
  • Bioconductor: These are the conductors that provide tools for analysis of genomic data. The Bioconductor take use of R statistical programming language for carrying out the performance for analyzing the data of genomes of patients. These provide with an open source of research and releases for two times every year (Eugene & Lane, 2017).

Conclusion

From the above discussion stated in the article it is concluded that data science is a vast field with numerous of applications in different aspects of life especially those related to the healthcare. The data science required in healthcare can provide with various opportunities for the data scientists to do research and bring innovations according to modern era. There are a lot of applications of data science that ae related to healthcare such as telemedicine, road accident analysis, predictive analysis of healthcare, medical imaging of patients organs to diagnose the diseases, carrying out drug research for specific diseases and introducing vaccines for the treatment (Nina & Lamb, 2015).

Data Science in healthcare has a lot of benefits including the reduction in risk of failure of treatment for a specific disease. It can provide the advanced treatment at the ripe time by diagnosing the problem in patient through symptoms. It can also avoid emergency issues and non-availability of doctors as a lot of information can be enhanced through online websites including meetings with doctors overall the world. The time of patients that is wasted in waiting for their turn can also be minimized. The goals of healthcare are to provide comfort and easy way for the data scientists to perform their tasks in healthcare systems. 

References

Bowen, Y., Ding, L., Chen, Y., & Shi, J. (2020). Augmenting Tumor‐Starvation Therapy by Cancer Cell Autophagy Inhibition. Advanced Science.

Carson, L., Fung, D., Mushtaq, S., Leduchowski, O., Bouchard, R., Jin, H., . . . Zhang, C. (2020). Data science for healthcare predictive analytics. In Proceedings of the 24th Symposium on International Database Engineering & Applications, 1-10.

Dinggang, S., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering , 221-248.

Enrico, F., Brachat, S., Jenkins, J., Marc, P., Skewes-Cox, P., Altshuler, R., & Keller, C. (2020). Ten simple rules to power drug discovery with data science. PLoS Computational Biology .

Eugene, L., & Lane, H. (2017). Machine learning and systems genomics approaches for multi-omics data. Biomarker research , 2.

Hongming, C., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug discovery today, 1241-1250.

John, R., & Pierscionek, B. (2017). A critique of the regulation of data science in healthcare research in the European Union. BMC medical ethics, 27.

Justin, K., Wang, L., Rao, J., & Lim, T. (2017). Deep learning applications in medical image analysis. Ieee Access , 9375-9389.

Louis, E., Gasperino, G., Bischoff, N., Taraman, S., Chang, A., & Feaster, W. (2020). HealtheDataLab–a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions. BMC medical informatics and decision making , 1-12.

Nina, G., & Lamb, J. (2015). Using data science & big data analytics to make healthcare green. In 2015 12th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT), 1-6.

Rani, S. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE) , 250-255.

Sebastian, P., Savulescu, J., & Sahakian, B. (2016). Facilitating the ethical use of health data for the benefit of society: Electronic health records, consent and the duty of easy rescue. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 220-340.

Suparna, C., Fishman, J., McGowan, M., & Juengst, E. (2014). Big data, open science and the brain: lessons learned from genomics. Frontiers in human neuroscience, 239.

Zuhaerah, T., Tohir, M., Nguyen, P., Shankar, K., & Rahim, R. (2019). Mathematical Issues in Data Science and Applications for Health care. International Journal of Recent Technology and Engineering, 4153-4156.

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