{"id":228912,"date":"2017-03-24T12:55:10","date_gmt":"2017-03-24T16:55:10","guid":{"rendered":"http:\/\/www.rochester.edu\/newscenter\/?p=228912"},"modified":"2025-11-19T07:59:47","modified_gmt":"2025-11-19T12:59:47","slug":"undergraduate-students-place-in-top-three-in-parkinsons-disease-data-science-contest-228912","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/undergraduate-students-place-in-top-three-in-parkinsons-disease-data-science-contest-228912\/","title":{"rendered":"Student work earns national praise in data science competition"},"content":{"rendered":"

A data science project to help clinicians predict Parkinson\u2019s disease progression landed two Ä¢¹½´«Ã½ undergraduates and their faculty mentor a top spot in a prestigious data science contest.<\/p>\n

\"illustration<\/a>
\n

Unlocking big data<\/h2>\n

A Newscenter series on how Rochester is using data science to change how we research, how we learn, and how we understand our world.<\/h3>\n<\/figcaption><\/figure>\n

Sam Lerman \u201917, a dual computer science and mathematics major, and Nick Potter \u201917, a mathematics major, led the project in collaboration with Charles Venuto, an assistant professor of neurology at the Medical Center\u2019s Center for Human Experimental Therapeutics<\/a><\/strong>. The team entered their computer model in a data challenge organized by the Michael J. Fox Foundation and GE Healthcare. Competing against other data and computational scientists, the students learned their submission was one of the top three finalists in determining baseline factors predictive of clinical disease progression in Parkinson\u2019s disease patients.<\/p>\n

\u201cThis is quite the impressive accomplishment for our students and research,\u201d Venuto says. \u201cIdentifying disease progression in Parkinson\u2019s disease has been a real challenge. Right now, clinicians lack objective means to provide Parkinson\u2019s disease patients advice on what to expect the symptomatic course of their disease will be in the next six months, in the next year, and so on.\u201d<\/p>\n

Parkinson\u2019s disease is a nervous system disorder that affects movement, with symptoms worsening over time. Age of onset, rate of disease progression, and type and severity of symptoms vary for the five million people worldwide living with the disease.<\/p>\n

Generating algorithms and models for prognosis would aid in patient care and planning clinical trials.<\/p>\n

The Parkinson\u2019s Progression Markers Initiative (PPMI) provided contest participants with a dataset of baseline information from a group of patients. The challenge involved analyzing this dataset to gain new insight into diagnosis and disease progression.<\/p>\n

\"graphic
(University image \/ Sam Lerman, Nick Potter, Charles Venuto)<\/em><\/figcaption><\/figure>\n

\u201cFirst we asked, \u2018How is \u00ad\u00ad\u00ad\u00ad\u00addisease progression quantified?\u2019\u201d Lerman says. \u201cOnce we defined our target, then we worked on developing and building models.\u201d<\/p>\n

Lerman, Potter, and Venuto\u2019s submission included machine learning algorithms they developed to predict three things:<\/p>\n

    \n
  • Rate of progression: Will patients have fast, moderate, or slow disease progression?<\/li>\n
  • Time until symptom onset: How long, in months, will it take for a patient to exhibit tremors or bradykinesia\u2014a symptom characterized by slowness of movement?<\/li>\n
  • Future score on disease scales: How will a patient fare on the Unified Parkinson\u2019s Disease Rating Scale (UPDRS), a rating tool to measure disease progression through analysis of mood, behavior, activities of daily living, and motor skills?<\/li>\n<\/ul>\n

    \u201cThe machine learning side of it was programmed abstractly so that this can be applied to any diseases that have these similar clinical outcome scale measurements,\u201d Lerman says. These could include Huntington\u2019s and other neurodegenerative diseases.<\/p>\n

    In the future, they envision a prognostic tool and interface that a clinician could use when interacting with patients.<\/p>\n

    \u201cOur hope for the next step is that when a patient goes to his or her doctor and asks, \u2018What can I expect from this disease?\u2019 the doctor can easily run the program to create an estimated timeline of the patient\u2019s disease,\u201d Lerman says.<\/p>\n","protected":false},"excerpt":{"rendered":"

    A computer model to help clinicians predict Parkinson\u2019s disease progression has landed two Rochester undergraduates and their faculty mentor a top honor from the Michael J. Fox Foundation.<\/p>\n","protected":false},"author":912,"featured_media":229302,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[42472,116],"tags":[486,11716,18802,23492,18632,18882,16072],"class_list":["post-228912","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-health-medicine","category-sci-tech","tag-awards","tag-data-science","tag-department-of-computer-science","tag-department-of-mathematics","tag-hajim-school-of-engineering-and-applied-sciences","tag-parkinsons-disease","tag-school-of-arts-and-sciences"],"acf":[],"yoast_head":"\nStudent work earns national praise in data science competition<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.rochester.edu\/newscenter\/undergraduate-students-place-in-top-three-in-parkinsons-disease-data-science-contest-228912\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Student work earns national praise in data science competition\" \/>\n<meta property=\"og:description\" content=\"A computer model to help clinicians predict Parkinson\u2019s disease progression has landed two Rochester undergraduates and their faculty mentor a top honor from the Michael J. 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