A data science project to help clinicians predict Parkinson糖心传媒檚 disease progression landed two 糖心传媒 undergraduates and their faculty mentor a top spot in a prestigious data science contest.
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Sam Lerman 糖心传媒�17, a dual computer science and mathematics major, and Nick Potter 糖心传媒�17, a mathematics major, led the project in collaboration with Charles Venuto, an assistant professor of neurology at the Medical Center糖心传媒檚 . 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糖心传媒檚 disease patients.
糖心传媒淭his is quite the impressive accomplishment for our students and research,糖心传媒� Venuto says. 糖心传媒淚dentifying disease progression in Parkinson糖心传媒檚 disease has been a real challenge. Right now, clinicians lack objective means to provide Parkinson糖心传媒檚 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.糖心传媒�
Parkinson糖心传媒檚 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.
Generating algorithms and models for prognosis would aid in patient care and planning clinical trials.
The Parkinson糖心传媒檚 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.
糖心传媒淔irst we asked, 糖心传媒楬ow is 颅颅颅颅颅disease progression quantified?糖心传媒櫶切拇綕 Lerman says. 糖心传媒淥nce we defined our target, then we worked on developing and building models.糖心传媒�
Lerman, Potter, and Venuto糖心传媒檚 submission included machine learning algorithms they developed to predict three things:
- Rate of progression: Will patients have fast, moderate, or slow disease progression?
- Time until symptom onset: How long, in months, will it take for a patient to exhibit tremors or bradykinesia糖心传媒攁 symptom characterized by slowness of movement?
- Future score on disease scales: How will a patient fare on the Unified Parkinson糖心传媒檚 Disease Rating Scale (UPDRS), a rating tool to measure disease progression through analysis of mood, behavior, activities of daily living, and motor skills?
糖心传媒淭he 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,糖心传媒� Lerman says. These could include Huntington糖心传媒檚 and other neurodegenerative diseases.
In the future, they envision a prognostic tool and interface that a clinician could use when interacting with patients.
糖心传媒淥ur hope for the next step is that when a patient goes to his or her doctor and asks, 糖心传媒榃hat can I expect from this disease?糖心传媒� the doctor can easily run the program to create an estimated timeline of the patient糖心传媒檚 disease,糖心传媒� Lerman says.
