Applications of Machine Learning in Healthcare

ML is an AI branch that teaches computers to access data to improve their programming without human intervention. Machine Learning has various benefits across industries. But the applications of machine learning in healthcare have been groundbreaking. It helps professionals through a patient’s medical journey, from diagnosis to cure. Machine learning has been a crucial part of the healthcare industry.

With the help of ML, the healthcare industry can quickly diagnose diseases and heal them. Here we discover a few real-life applications of machine learning in the healthcare industry and its benefits.

Real-life ML applications and their benefits to healthcare industry:

 

• Microsoft’s InnerEye Project:

  Application of Machine Learning in Healthcare:
  This technology helps discover cancerous tumors in a body. Once in place, InnerEye would use data and give meaning to it. It would read PET, MRIs, and CT Scans and determine the tumor’s size and volume and its prognosis.

  InnerEye helps with a quick diagnosis of the disease. It also helps determine if a particular treatment works for the patient or not.

  
  Its benefit in the healthcare industry:
  Initiatives like the InnerEye by Microsoft work on image diagnosing tools. It quickens the process of diagnosing diseases by analyzing images, helping healthcare professionals.

• BERG’s Interrogative Biology:

  Application of ML in Healthcare:
  BERG’s Interrogative Biology is an intelligence platform working towards mapping terminal diseases. Interrogative Biology allows professionals to develop innovative treatments according to a patient’s biology.

The platform and Bayesian AI identify and characterize an E2 isozyme in the Ubiquitin-Proteasome System through genetic composition traits. It also aims to assess the effects of BPM31510 on solid tumors and correlate adverse effects due to any biological indicators or features.

Its benefit in the healthcare industry:
In 2020, BERG files for a patent for the application for the E2 isozyme, the enzyme in the UPS (Ubiquitin-Proteasome System). According to Dr. Niven R. Narain, BERG’s Co-founders, President, and CEO, they are looking to utilize the discovery of such molecular level studies that have helped in oncology and non-oncology treatments.

BERG’s Interrogative Biology uses machine learning to manage large data sets. It can easily predict efficacy and help patients. It also studies age-related disorders and a way to tackle them through proper nutrition and physical exercise.

• IBM’s Watson and Pfizer:

  Application of ML in Healthcare:
  IBM’s Watson, an AI-driven technology, and Pfizer, the pharmaceutical company, collaborated in 2016. Pfizer aimed to use IBM’s Watson to research immuno-oncology. Pfizer believed it could leverage IBM’s Watson to develop drugs that could build immune systems to fight cancer.
  
  Its benefit in the healthcare industry:
  In 2018, according to fiercebiotech, Pfizer had partnered with Xtalpi, a Chinese AI biotech company, and is working towards designing drugs and testing them at a molecular level.

IBM Watson for Oncology is using the patient’s medical information to help professionals determine better cancer treatments. It uses ML algorithms trained by professionals. It measures patients’ unique conditions and provides specific therapies.

• MIT’s Clinical Machine Learning Group:

  Application of ML in Healthcare:
  MIT’s Clinical Machine Learning Group is an association of experts in ML and Healthcare. They use ML to understand its role in real-life medical emergencies and overcome the challenges.
  

The Group used a Multivariate Time-series modeling approach to assess and forecast clinical data in emergency cases and ICUs. Here rather than building a claim on the similarities of a patient’s condition, they use the correlation between and within several time-series.

Its benefit in the healthcare industry:
Recently, MIT’s Clinical Machine Learning Group joined the MIT Emergency Ventilator team and used ML algorithms and techniques to resolve the shortage of ventilators due to COVID 19, by splitting the ventilators between patients.

They used ML to understand the correlations and apply that to the multi ventilator design and uploaded the same on an open-source site.
They are currently working on determining ways to reduce unnecessary antibiotics in UTIs using ML algorithms.

A recent study also helped them build “health knowledge graphs” for computers. The graph allows computers to determine specific behavior and conditions in ill patients. It enables computers to discover and develop patterns to recognize diseases as well as diagnosis.

• PathAI

  Application of ML in Healthcare:
  PathAI utilizes AI-driven technology to diagnose diseases. They are developing an ML technique that would resolve challenges in pathology. Its Ai-driven pathology models were used in phase 2 and phase 3 clinical trials for hepatitis B virus.

It also used ML-based analysis of patient data from a Phase 2b trial of multiple therapies of advanced fibrosis (F3-F4) due to NASH.

Its benefit in the healthcare industry:
PathAI has recently worked towards ML techniques that have helped with Breast Cancer biopsies and liver disease prediction. It can help tackle various other health problems and pre-existing conditions well.
330 patients with chronic HBV infection participating in the clinical trials revealed fibrosis regression in year 1 which usually is detected in year 5 with the manual assessment. This discover

 

y helped in understanding the nature of the disease and how ML algorithms can figure out their progressions and future clinical events.

• prognosFACTOR

  Application of ML in Healthcare:
  Prognos Health is a leading clinically-focused healthcare analytics company. Its healthcare platform prognosFACTOR provides users billions of lab records to analyze. It complies with HIPAA regulations and creates a comprehensive data mapping of a patient’s journey.

ML Techniques help specify clinical knowledge of diseases provided thorough data on a patients’ medical journey which can be used for commercial applications.

Its benefit in the healthcare industry:
In 2020, Prognos Health claimed to have 325 Million de-identified patient records. ML makes it possible for prognosFACTOR to use fed data and diagnose diseases. It has made skimming through many data sets with patient information easily accessible.

It recently joined Datavant to provide companies with de-identified patient data to help combat COVID-19.

• Beta Bionics’ iLet Bionic Pancreas System:

  Application of ML in Healthcare:
  Beta Bionics has developed a bionic pancreas called iLet. It is an automated device that provides both insulin and glucagon. It helps manage the glycemia particularly found in type 1 diabetes.

It aims to provide safer and effective therapy as it is easy to use the device. FDA approved this technology in 2019, after years of research and development.

Its benefit in the healthcare industry:
It is a fully automated, bionic pancreas system and functions as insulin delivery technology. It improves diabetic care and manages blood sugar levels for people with type 1 diabetes.

Its main USP is that patients only need to enter their weight, and the machine calculated the dosage of insulin without any other information.

• MD Insider

  Application of ML in Healthcare:
  MD Insider is a machine learning-enabled platform that introduces patients to qualified medical specialists. Its ML technique understands the patient’s requirements and familiarizes them with an analysis of medical specialists.

Patients going through any healthcare procedure want the best possible treatments, here MD Insider uses ML to analyze medical professionals’ performance and present the analysis to the patient to help them make a well-informed decision.

Its benefit in the healthcare industry:
It is a trusted platform with 254 million patients, 1.9 million health, and 42.6 Thousand Medical Groups. MD Insider is an advanced reporting platform and is HIPAA compliant as well.

In 2020, Accolade acquired MD Insider and was launching “Accolade Total Care.” It provides a physician’s quality insights and transparency in cost. It increases the standard of healthcare by providing its members with quality healthcare solutions.

Here are a few more examples of developing applications of machine learning in healthcare:

• Machine learning has made it possible to conduct surgeries by training robots to do their bidding. Companies like Da Vinci Surgical Systems develop solutions for robotic surgeries. The technology is at a developing stage to make it more cost-effective and easily accessible.
• DeepMind technology and Google Health join hands in developing an app to deliver fast and accurate medical diagnoses and treatments. It is at the initial stage of research and development and is evolving to meet its objectives.

Machine learning and healthcare are changing people’s lives by improving the quality of healthcare. It has created platforms for medical professionals to reach patients easily. Machine learning in healthcare has created opportunities and has taken the initiative to its highest efficacy.

Conclusion:

To conclude, these machine learning applications are cutting edge technology in the healthcare industry. Here we understood how these real-life machine learning applications benefit the healthcare industry. These applications and technologies swiftly diagnose diseases or help in the research and development of medicines.

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