As a data science enthusiast everyone would have thought, can data scientists work in healthcare? We know various applications of data-driven technologies in various sectors such Banking, Finance, ecommerce, marketing, media & publishing etc. But who would have assumed that data science will bring big advancements in the healthcare sector.
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. Thus, in this article, we will discuss various applications of Data Science in the healthcare industry.
How Data Science is used in Healthcare.
Data Science has brought about a massive and welcome change to the healthcare industry. Due to the large amounts of medical data generated from the healthcare sector like activities of the brain, stress level, heart rate, sugar level, laboratory reports, information about the purchase of medicines and many more there lies an immense opportunity to analyze and study these using recent technologies. The huge volume of data can be stored together and analyzed effectively using machine learning algorithms. Analyzing the trends and understanding the patterns in the data can help in better decision-making resulting in a better quality of patient care.
How Data Science has changed the Healthcare industry.
Data science in healthcare has become an essential part and aspect that has transformed the industry. Patients now have access to some of the best diagnostic tools, new treatments and procedures resulting in less pain and quicker healing. It has boosted the speed of treatment and diagnosis and the workflow of the healthcare system has been enhanced. Some benefits of using data science in healthcare sector include:
- Digitization of Health Records
- Providing proper treatment on time
- Reducing waiting time for treating people
- Reducing failure risk while treating patient
- Improved patient care
There are several applications of Data Science in the Healthcare industry. We are going to discuss a few of them.
Medical Imaging
One of the most effective uses of data science in healthcare is medical imaging. It is one of the most interesting areas of study in image recognition technology. Data science techniques help in recognition of medical test images X-ray, sonography, MRI, CT scan, and many more to figure out defects to help health practitioners make an effective treatment strategy. Analyzing these medical test images helps gain valuable insights for health practitioners to provide better treatments to the patients.
With fast improving computational power and the availability of large amount of data, deep learning has become the go to Data science technique that is used since it can learn unstructured data. Among all deep learning methods, convolutional neural networks (CNNs) are of special interest. Naturally, there are many recent works trying to apply CNN on medical image analysis. Such a case of using deep learning in healthcare is a recent study published by Google AI in which using deep neural networks and Machine Learning, a deep learning model is created which can diagnose 26 skin related diseases with 97% accuracy. Some of the common data science algorithms used for medical image analysis include image processing algorithms, anomaly detection algorithms and descriptive image recognition algorithms.
Drug discovery
In 2020, medical researchers all over the world faced the emergent need for a treatment for Coronavirus disease (COVID-19). It was so challenging for medical research institutes to find vaccines for the novel virus in a short time.
Thus, to develop vaccines/medicines medical researchers require to perform millions of tests to find the formula. In earlier days, to analyze the data of the millions of tests, it required 10–12 years. But now, with the help of various Data Science techniques, it has become a much easier task. In the fields of drug discovery and development, data science techniques have been used for the development of novel drug candidates.
Data science have significantly advanced drug discovery. Pharmaceutical companies have greatly benefited from the utilization of various techniques in drug discovery. Various machine learning models are being used for predicting chemical, biological, and physical characteristics of compounds in drug discovery. For example, machine learning algorithms have been used to find a new use of drugs, predict drug-protein interactions, discover drug efficacy, ensure safety biomarkers, and optimize the bioactivity of molecules. ML algorithms that have been widely used in drug discovery, which include Random Forest, Naive Bayes and Support Vector as well as other methods.
| Electronic Health Records |
In earlier days, when health practitioners needed different forms of information such as the medical history of the patient, laboratory reports and other personal or private medical data. The usual practice for a clinic, hospital or patient was maintaining either written notes or in the form of printed reports. However, now, the scenario has changed. In 2003, the Institute of Medicine, a division in the National Academies of Sciences and Engineering coined the term “Electronic Health Records” for representing an electronic portal that saves the records of the patients.
Electronic Health Records (EHRs) consist of digital summaries of a patient’s medical records. EHRs are a vital part of healthcare industry. EHRs can contain a patient’s medical history, diagnoses, medications, treatment plans, allergies, radiology images, and laboratory results. EHRs are built to share information with other health care providers and organizations such as laboratories, specialists, medical imaging facilities, pharmacies and emergency facilities. With EHRs, information is available whenever and wherever it is needed which benefits in improved patient care, improved Care Coordination, Diagnostics and Patient Outcomes.
Predictive Analytics
Much of Healthcare sector is about anticipating and reducing risk based on current and historical patient data. Incomplete, unstructured or less data may lead to wrong treatment for the patient or can worsen the patient condition. So, it is important that patient data must be collected efficiently. Health practitioners have always had to make decisions or predict likelihood of events before they occur. With the advancement of predictive analytics in healthcare, it help doctors to make predictions for the patient conditions and make proper strategies to treat the patients.
Predictive analytics models are built on top of Data Science. Some benefits of predictive analysis for the healthcare system are detecting early signs of patient deterioration, identifying at-risk patients in their homes preventing avoidable downtime of medical equipment, faster treatment of the patients, faster documentation and predicting preventive measures and their condition. One of the major use predictive analytics is to predicts the future medical crises of a patient. On this topic I have done a project of predicting ten-year risk of Coronary Heart disease of different age groups using Machine Learning algorithms which I will publish in my next article. So stay tune and follow to see my next article.
Conclusion
Data Science technology is being adopted in healthcare sector all over the world. We discussed some of the applications of Data Science in healthcare. Technology is already advancing the healthcare sector by digitalization, reducing treatment cost and time, handling large population, improved patient care and many more. To conclude, the applications of Data Science in healthcare have the potential to enhance the entire healthcare system.
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