Monthly News Roundup: March 2022

Need some good reading material? Here are some of the top stories we loved diving into over the last month!

In recent years, machine learning has become increasingly important due to the increasing volume and variety of data, easy access and affordability of computational power, and the availability of high-speed Internet. Understanding that unbalanced data can hinder the development of a machine-learning model. This article discusses how researchers at MIT developed a new technique that boosts models’ ability to reduce bias. Injecting fairness into machine-learning models, MIT

In today’s world, Google is committed to connecting and helping millions of people get healthier through products and services that connect and make health information meaningful. The article sheds some light on the health projects Google is continuing to work on across its enterprise to achieve its health initiatives. 5 health projects Google is working on, Beckers Hospital Review

Incorporating Machine Learning (ML) and operating it through Machine Learning Operations (MLOps) can lead to many advantages. This blog post sheds some light from a technological perspective of building an ML model on how ML model generation takes a backstage while making it reliable and operationalizing it (MLOps) takes the center stage. Machine Learning for Healthcare: Development Lifecycle & MLOps, ML-Architects Basel

Today, clinical decision support systems have become crucial for organizations seeking to improve care delivery. This feature offers some insights on how the role of machine learning, right data, and integration methods, has the potential to advance and transform the clinical decision support system and help providers deliver optimal care. How Machine Learning is Transforming Clinical Decision Support Tools, Health IT Analytics

Machine learning enhances data-driven decision-making, identifies key trends, and increases research efficiency. When it comes to Healthcare, machine learning techniques can be used in various ways. This article sheds some light on some real world use cases of machine learning. Machine learning in healthcare: 12 real-world use cases to know, Nix

Today, payers, employers, and providers are analyzing millions of data points, and claims data. This article offers insights on how to put these troves of health data to use and leverage machine learning to maximize outcomes. Leveraging Machine Learning to Maximize Outcomes — and Savings, Fierce Healthcare

The opportunities Machine Learning offers for Healthcare organizations are enormous and have many effective uses, but it also has the potential to do a lot more. This article presents the findings of a study published by American Journal of Health-System Pharmacy that leveraged machine learning and advanced analytics technology to track stolen drugs from hospitals. Study: Machine learning successfully tracks drugs stolen from hospitals faster and with minimal error, Medcity News

Machine learning is developing at a rapid pace and its algorithms are already being put to use for diagnosis and assisting clinicians charting out treatment plans. This article offers excerpts from the talk delivered by David Klebonis, COO of Palm Beach Accountable Care Organization (PBACO), during his session, “Driving Appropriate Hospice Utilization with Explainable AI,” at the HIMSS22 conference in Orlando. AI, machine learning can drive better hospice utilization, Healthcare Finance.

Machine learning is assisting health care professionals make better decisions to improve patient safety, care quality, and efficiency. In this article, offers the talk as well the experts from talk on the topic Designing Accountable, Equitable Health Care Algorithms, where the Assistant Public Health Officer at the County of Santa Clara Public Health Department outlines a machine-learning use case for matching Covid-19 contact tracers with patients based on language requirements. Designing Accountable, Equitable Health Care Algorithms, NEJM Catalyst.

Google has been working on many healthcare projects and is ramping up its investments in health-focused initiatives. This article offers some insights on how Google wants to speed up healthcare with machine learning and smartphones as it has intensified its focus on health tech and expanded its healthcare market reach. Google Health wants to speed up healthcare with machine learning and smartphones, Android Police.

Hope you enjoy reading our Sparity News roundup — March 2022.

We’ll see you next month with more.

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