Machine Learning for Healthcare and Life Sciences (HCLS)

Machine learning is transforming product development and research in many business verticals in the marketplace today. And the biotech and medical industries are no exception. As the sector has adopted ML, the technology  has proved its worth and is being readily adopted by industry business leaders. As a result, researchers can now process the vast amounts of data their companies produce to improve the understanding of disease and to offer better treatment options. Read on to discover what is possible with machine learning in the biotech and medical industries.

What Is Machine Learning?

Machine learning (ML) is a branch of computer science that gives computers the ability to learn from the data they receive. Machine learning focuses on algorithms and statistical models that allow a computer to make predictions or decisions by building a model from input data.

Machine learning has existed for decades. However, it has become a significant component of the data science field much more recently. Over the past several years, machine learning has been used to solve problems in many areas, including language translation, voice recognition, image analysis, video analytics, and text processing.

Learn more in our article: What is Machine Learning?

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What Does Machine Learning Have to Do With the Biotech and Medical Fields?

Machine learning has become a critical component for many applications in the biotech and medical fields. One of the main problems machine learning can address in these fields is data overload. Biomedical researchers are constantly conducting clinical studies, drug candidate screening, biomedical equipment testing, and other research that produces a massive amount of data that is difficult to process manually. Even standard patient monitoring can lead to an overwhelming data load.

To address this issue, machine learning can be applied in various areas of biomedical research. ML applications in biotech and medical include developing diagnostic tests, prognostics models, and therapeutic decision support systems. It can also be used in health care to provide evidence-based clinical decision support and in personalized medicine to predict patient risk and disease progression.

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Machine Learning in Clinical Trials

Artificial intelligence (AI) and machine learning are now used in clinical trials to speed up the process and improve decision-making. In a clinical trial, researchers typically measure biochemical markers through laboratory tests at various points to assess a patient’s response to a drug. However, it is impractical to conduct laboratory tests at multiple points in the trial. Researchers pool data and use machine learning to replace sparse laboratory measurements with a mathematical model.

For example, researchers are accomplishing promising work with the help of machine learning in clinical trials for Alzheimer’s disease. Researchers use data from magnetic resonance imaging (MRI), and positron emission tomography (PET) scans of patients to predict whether a patient has Alzheimer’s disease with extremely high accuracy. In addition, machine learning can help predict the risk of progression from mild cognitive impairment to full-blown Alzheimer’s disease.

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Machine Learning and Cancer Biomarker Discovery

Cancer biomarker discovery aims to improve the early diagnosis, treatment, and prevention of several types of cancer. Cancer biomarkers are physiological states or conditions that may indicate the presence of cancer, such as proteins released from a tumor or changes in a person’s DNA. Biomarkers could also be used to monitor the effectiveness of a particular therapy.

In recent years, machine learning has been successfully employed to discover new biomarkers for breast cancer. Researchers begin by analyzing a large aggregate of tumor samples to identify proteins that may be important for differentiating between cancer and noncancerous cells. A machine learning algorithm may then be used to search for patterns in the expressed proteins that are common across multiple cancer samples. The aim is to discover potential biomarkers that could be used to achieve early diagnosis.

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More Machine Learning Applications in the Biotech and Medical Industry

There are many other machine learning applications in the biotech and medical fields, including:

• Medical imaging. Deep learning is being applied to MRI technology to enable improved interpretation of magnetic resonance images. Neural networks are used because they can uncover structures in the images that may be difficult to identify with manual analysis.
  
  
• Molecular biology. Researchers are using supervised machine learning to predict the outcomes of drug-target interactions. This method of identifying novel drug targets and biomarkers of treatment success or failure through supervised learning can also be applied more broadly to study other diseases.
  
  
• Object detection. Neural networks can be used to identify a wide variety of patterns in images, including the detection of certain objects. For example, machine learning has been used to automatically diagnose abnormal cells in Pap tests and analyze mammograms for cancer.
  
  
• Image screening. Machine learning can be used to analyze thousands of images from a medical device as it scans the human body. The aim is to flag events that fall outside normal parameters or require immediate attention. For example, machine learning was used to detect adverse events in MRI scans by examining their 3D shape and texture.
  
  
• Predictive modeling. Machine learning can help predict treatment options for patients based on their genomic data. Machine learning is also used to classify clinical trial patient groups, identify which biomarkers to measure, and determine which clinical endpoints to study.
  
  
• Health wearables. Sensors in wearable devices, such as Fitbits, smartwatches, and dedicated health wearables, can be used to monitor a person’s health. The waveforms of the heart’s electrical activity used to create an electrocardiogram (ECG) reading can be predicted by feeding data into machine learning algorithms with an output for consumers or healthcare providers. With this information, individuals can detect and monitor any abnormalities.

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Machine Learning’s Impact on the Biotech and Medical Industry

As more people gain access to their genomic data, patients will be empowered with information that allows them to make better healthcare decisions. This may also contribute to the rise of value-based healthcare, where drug and medical device companies are reimbursed based on their performance. Machine learning can provide industry players with the tools they need to keep up in this changing market.

Overall, it’s important to respect the impact that machine learning is having on the biotech and medical fields. With even more applications waiting to be discovered, machine learning has the power to completely redefine many aspects of life scien ces that help to make our lives healthier and safer. For more information regarding how machine learning can transform the efforts of companies in the biotech and medical industry, contact the experts at Bistrapped. 

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Get started with Machine Learning

If you have questions or would like to explore how machine learning can help your biotech or medical industry business, reach out to us at Bitstrapped and book a free 30-minute discovery call. You can also learn more about how our machine learning developers work by reading this machine learning in neurological recovery therapy case study.