Data Science and Its Impact on Medical Technology
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Data Science and Its Impact on Medical Technology

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Data science provides effective medical treatment with reduced risks, delivers medical services promptly, and enables the healthcare industry to be agile and adapt to various situations. Data science increases the healthcare industry’s productivity, synchronization, and efficiency. 

The task of optimizing healthcare workflows falls on the data scientist. A data scientist works with doctors, researchers, and other professions in the healthcare industry to perform the following tasks:

  • Work alongside important business leaders to comprehend their needs and determine the kind of analytical data needed.
  • Gather incoming data and structure and synchronize datasets.
  • Contribute to the Public Health Datasets.
  • Perform database audits.
  • Provide data analytics for different applications.
  • Collaborate with ML and other developer teams to design solutions.

The healthcare industry can benefit from data science due to the important role it has played in the shaping and evolution of medical technology. Here’s how data science affects medical technology:

 

  • Medical Image Analysis Tests

 

Data science assists in the recognition of scanned visuals to determine the defects in the mind and body. It helps medical professionals create an effective treatment strategy. The medical image analysis tests include sonography, X-ray, CT scan, X-ray, and more. 

With the help of data science, medical professionals can note the differences between resolution and state of the image and examine the orthogonality. For instance, Deep Learning can diagnose skin diseases with 97% accuracy. 

Deep Learning combines data science, machine learning, and deep neural networks to diagnose skin diseases. Lastly, medical image analysis uses three algorithms— anomaly detection, image processing, and descriptive image recognition.

  • Anomaly detection identifies medical conditions such as fractured or displaced bones.
  • Image processing analyzes images, enhances them, and removes noise from them.
  • Descriptive image recognition visualizes and extracts the information from an image, interprets it, and uses it to create a bigger picture, such as combining images of the brain.

 

  • Predictive Analytics

 

Predictive analytics enables medical professionals to collect data about a patient in an efficient manner. The data can include the patient’s body temperature, blood pressure, sugar level, and more. They analyze the data for patterns and correlations. The objective is to identify the symptoms and stages of the disease, its level of progression, among other things.

Predictive analytics incorporates data science to predict the patient’s condition. It allows doctors to create an effective treatment plan for the patient. Here’s how predictive analytics benefits the healthcare industry:

  • It assists medical professionals to manage the treatment of chronic diseases.
  • It observes and examines the demand for medicinal logistics.
  • It forecasts the medical crises of patients that may arise in the future.
  • It predicts the condition of the patient and recommends preventive measures.
  • It provides medical professionals with faster documentation of hospital data.

 

  • Facilitates Drug Research

 

The wide range of new medical diseases coming about has placed a strain on medical researchers tasked with finding new vaccines and medicines to combat them. Researchers can’t find and perfect the formula for a vaccine or medicine without understanding the causative agent’s characteristics. 

To understand it, they may need to perform several tests. Once the formula is found, the researchers will again have to perform more tests to ensure its effectiveness in treating the disease. Easily, this can take 10 to 12 years, but thanks to data science, researchers can use the data from past test cases and process it within months or weeks to create a formula. 

Data analysis allows them to evaluate the formula’s efficiency and make it available to people in less than a year. Making the entire process increasingly efficient and successful is data science and machine learning. Data science course in bangalore, combined with machine learning, has revolutionized the healthcare industry’s research and development sectors. 

 

  • Genomics 

 

Genomics studies the sequencing and inspection of genomes consisting of your genes and DNA. Gene research facilitates the development of high-level treatments. Genomics allows medical professionals to discover DNA’s irregularities and characteristics. 

Genomics also help medical professionals find a connection between disease, its symptoms, and the person’s health condition. Moreover, genomics involves analyzing how a certain type of DNA responds to a particular drug.

Before the introduction of data science and big data tools, studying genomics was a time-consuming and redundant task. Researchers had to go through millions of pairs of DNA cells. Data science and big data tools can examine human genes in half the time now. 

It allows them to locate a specific genetic issue and the drug best suited to address that specific gene. Researchers use the following tools in the research genomics:

  • MapReduce processes large quantities of genetic data and allows medical professionals to process genetic sequencing quickly.
  • SQL retrieves genetic data from several different databases and assists in data computation.
  • Galaxy performs research on genomes.
  • Bioconductor analyzes genetic data. 

 

  • Virtual Assistance Applications

 

Data science has played a significant role in the development of virtual assistance. Data scientists have created designed platforms to provide patients with a personalized experience. The medical applications using data science analyze the patient’s symptoms to identify the disease. Patients need to enter their symptoms and let the medical application do the rest. 

Apart from identifying the disease, medical applications will also suggest medication, precautions, and the treatment options the patient can opt for according to their medical diagnosis. The applications also examine the patient’s data to create a checklist of the various treatment processes they should follow. It also reminds patients to take their medication each day. 

Virtual assistance has become one of the most recommended options for people suffering from depression, anxiety, and Alzheimer’s disease. The application also notifies patients to work out and eat right.

To conclude, data science is a remarkable human innovation which has transformed the medical technology, optimizing to serve both medical professionals and patients better. Over time, technological advancements, digitalization, the need for reducing treatment costs and duration, and the need to manage a large population will continue to improve and advance medical technology with data science promoting its growth. 

Author Bio

Santosh Rameshbabu is a data scientist with experience working in various industries, including the healthcare industry. His interests include learning about big data, machine learning, and data mining.

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