AI and machine learning facilitate pioneering research on Parkinson's


At the 2020 Machine Learning for Healthcare Conference, IBM and Michael J. Fox Foundation will reveal a disease progression model that accurately pinpoints how far a patient’s PD has advanced.

Image: iStock

A long-sought understanding of Parkinson’s Disease (PD) will be revealed at Friday’s 2020 Machine Learning for Healthcare Conference. In early 2019, IBM Research and The Michael J. Fox Foundation (MJFF) announced plans to collaborate and use artificial intelligence (AI) and machine learning (ML) to decode the elusive and complex mysteries surrounding PD symptoms and progression. 

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

IBM and the MJFF have built an innovative disease progression model that helps clinicians more accurately pinpoint the exact status of a PD patient’s progression. Despite PD’s first identification more than a century ago in 1817, how it affects patients during the course of the disease has been an undertaking that previously evaded both doctors and researchers.

Yet many questions about the chronic disease remain unanswered, but a better understanding through clinical trials can improve patient-care management and more efficient development of mitigating drugs.

Machine learning has helped attempts to grasp the complexities surrounding PD. The team designed innovative algorithms that use factors that can mask the outward appearance of someone’s PD, including medications that can palliate symptoms such as tremors, improve motor control, and modify other common symptoms. 

PD is a neurological disorder that affects a person’s movements and often includes tremors–dopamine levels drop because of brain nerve-cell damage. It usually starts with tremors in one hand, but other symptoms that develop from the potentially lifelong disease–which remains incurable–are loss of balance, stiffness, and slow movement. 

Since PD’s underlying biology is still unknown, it has been onerous for doctors to determine how advanced the disease is by just judging a patient’s outward appearance. It’s difficult to detect the connection from disease states to biological mechanisms. If a patient is on medication (as is often the case), the physician is further challenged, as medications can mask some symptoms. 

SEE: Managing AI and ML in the enterprise 2020 (free PDF) (TechRepublic)

PD patients do not react to medications, develop symptoms or related issues in the exact same way, making progression not straightforward, and difficult to define, and, the development of understanding and classifying stages very difficult. The collaborative study takes into consideration the effects of different medications, which may manifest differently in each individual at different stages–this had not been explored previously.

IBM will further use a vast amount of PD patient data, aggregated by the MJFF, in the hopes of discovering new results that can accurately define each stage of PD as it develops; if this stage is developed clinicians will be assisted in designing more accurate and customized treatment plans. Achieving the goal will also provide drug developers with more accurate levels when recruiting for clinical trials of new treatments and potential cures. 

Further, the team hopes that the research might be inspirational or useful in the examinations and research into other chronic conditions, such as diabetes, Alzheimer’s disease, and ALS. The next stage for IBM Research and the MJFF will be to focus on the recent discoveries, from the application of the new models, combined with the extensive data the MJFF has provided. 

PD is one of the top 10 causes of death in those 65 and older, and it’s estimated that 6 million people worldwide, and one million people in the US have PD–these figures are expected to double by 2040, making research and even more understanding critical and urgent. 

Also see 



Source link

Leave a Reply

Your email address will not be published. Required fields are marked *

error

Enjoy this blog? Please spread the word :)

Follow by Email
LinkedIn
Share
Instagram