Sequential Pattern Mining of Electronic Health Record for Early Diagnosis of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease primarily affecting the upper and lower motor neurons. The average survival time for ALS patients is 19 months from the time of diagnosis and 30 months from symptom onset. The diagnosis of ALS is primarily based on clinical evaluation along with a series of tests to rule out other mimicking diseases. The clinical diagnosis remains challenging with an average diagnostic delay of 11 to 12 months or more after the onset of symptoms. Thus, early diagnosis of ALS is critical to prolong survival and improve quality of life. Our early work indicates that early detection of ALS based on electronic health records (EHR) using sequential pattern mining algorithm is possible to reach sensitivity and specificity accuracy to serve as an assistive diagnosis tool.

In this study, we further develop the early ALS detection algorithm in the suspected ALS patient population requiring diagnosis from a neurologist . Our objective is to improve our algorithm accuracy to >80% sensitivity with at least 90% specificity and to reduce the complexity of the algorithm to make the algorithm explainable. The algorithm is validated on an independent EHR dataset. The design of the prospective clinical validation study of the algorithm is described.

machine learning

ALS diagnosis

sequential pattern mining

big data

bioinformatics

clinical validation 

Mid-Level

Knowledge

Women in Statistics and Data Science 2022