Outline of the problem
The objective of the competition was to identify new biomarkers that contribute to the progression of Parkinson’s disease. Experts believe that alterations in protein and peptide levels signal further development of the disorder. By forecasting future MDS-UPDRS scores for patients, we could devise more effective treatments for each unique case. Since there is no known cure for Parkinson’s disease, we must understand all phases of its development in order to ease unpleasant symptoms.
Proposed solution
After carefully analyzing the problem, the team came up with the innovative idea of developing an ML model that could predict MDS-UPDRS scores for each individual patient, personalized to their level of protein and peptide.
The solution required a deep dive into the data to uncover hidden patterns and relationships. These patterns were then used to train machine learning algorithms that could predict the progression of Parkinson’s disease in months interval.
To improve our understanding of Parkinson’s disease and develop new treatments, we used a variety of technologies, including:
Doctors currently monitor the progression of Parkinson’s disease (PD) by taking a medical history, performing a physical examination, ordering tests, using rating scales, and tracking the person’s progress over time. However, there is no way to predict with certainty how quickly PD will progress in each individual.
Now, DAC.digital researchers among many others are developing a system to help predict the development of PD using machine learning (ML) algorithms and models. This system could be used to identify people who are at risk of developing PD and to provide them with early treatment. Early treatment could help to slow the progression of the disease and improve the quality of life for people with PD.
The development of this system is a promising step towards improving the diagnosis and treatment of PD. By better understanding the factors that contribute to PD progression, researchers can develop more effective treatments and improve the lives of people with this disease.
A team of researchers was tasked with analyzing tabular data of over 10,000 subjects, including patients’ peptides/proteins levels (taken from Cerebrospinal Fluid sample) and past Unified Parkinson’s Disease Rating Scale (UPDRS) score with additional clinical state on medication. Data was collected during cyclic visits and often included incomplete information.
Experts’ work was successful in identifying that the relations between given peptides and protein data is not sufficient to accurately forecast the PD progression.. That was due to incompleteness of provided data and incorrect incorporation of control group to the data set. Stronger signal was found in the visits frequency, although it was not explored by DAC experts.
The team was self-organized and efficient. When new tasks came in, the person who was interested in the topic was responsible for that part. This allowed the team to be flexible and adaptable to changing priorities.
While the team was eager to deepen their current skills in data analysis, especially in processing tabular data. They had to master new methods and techniques, and they often had to troubleshoot problems. It was a tough nut to crack, but the team persevered and eventually gained a deep understanding of tabular data processing.
The team’s hard work paid off. DAC engineers were able to successfully process the tabular data and gain valuable insights. This knowledge will be essential for future projects. Team is confident that they can handle any challenge that comes their way.
Our team developed a working machine learning (ML) model that can predict the progression of Parkinson’s disease (PD). We started with raw data and imputed missing records required for accurate predictions… Then, we trained ML algorithms on found relations to make predictions about the PD progression in set time intervals. Our computational pipeline was able to make correct predictions, and we are confident that it can be used to improve the diagnosis and treatment of PD.
This project was a valuable learning experience that allowed us to apply our knowledge of data analysis and machine learning to a real-world problem. We are proud to have made a positive impact on the lives of people with Parkinson’s disease.
