Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

Abstract

SummaryObjectives: Understanding the progression of comorbid neurodevelopmental disorders (NDD) during different critical time periods may contribute to our comprehension of the underlying pathophysiology of NDDs. The objective of our study was to identify frequent temporal sequences of developmental diagnoses in noisy patient data. Methods: We used a data set of 2810 patients, documenting NDD diagnoses given to them by an NDD expert at a child developmental center during multiple visits at different ages. Extensive preprocessing steps were developed in order to allow the data set to be processed by an efficient sequence mining algorithm (SPADE). Results: The discovered sequences were validated by cross validation for 10 iterations; all correlation coefficients for support, confidence and lift measures were above 0.75 and their proportions were similar. No significant differences between the distributions of sequences were found using KolmogorovSmirnov test. Conclusions:We have demonstrated the feasibility of using the SPADE algorithm for discovery of valid temporal sequences of comorbid disorders in children with NDDs. The identification of such sequences would be beneficial from clinical and research perspectives. Moreover, these sequences could serve as features for developing a full-fledged temporal predictive model.

Data‐driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering

“…All measures found that in each iteration, all patterns were similar at a signi cance level of P < 0.05. All Pearson and Spearman correlation coe cients are higher than 0.75 [19] , and the smallest correlation coe cient is 0.89, which means that the metric values in the training and validation sets are similar. The p-values of the Kolmogorov-Smirnov tests for the similarity of the distributions are all above 0.7, so no signi cant difference is found in the distributions.…”

“…[15][16][17] Recently, many studies have focussed on developing sequential pattern mining methods to discover real-world treatment behaviour patterns from clinical data, which has become a research hotspot. [18][19][20] Although current research mainly focuses on the mining of drug treatment models. [21][22][23] For example, Wright A P et al used sequential pattern mining to evaluate whether the method could effectively identify temporal relationships between diabetes drugs.…”

“…Most process mining algorithms can automatically build process patterns, which are very suitable for understanding and can be used for process optimization [15][16][17] . Recently, many studies have focused on developing sequential pattern mining methods to discover real-world treatment behavior patterns from clinical data, which has become a research hotspot [18][19][20] . However, current research focuses on the mining of drug treatment models [21][22][23] .Due to the multidimensional, multilayered, and chronological order of the cancer data in this study, it was challenging for us to extract treatment paths.…”

Data-Driven Treatment Pathways Mining for Early Breast Cancer Using cSPADE Algorithm and System Clustering

“…Most process mining algorithms can automatically build process patterns, which are very suitable for understanding and can be used for process optimization [15][16][17] . Recently, many studies have focused on developing sequential pattern mining methods to discover real-world treatment behavior patterns from clinical data, which has become a research hotspot [18][19][20] . However, current research focuses on the mining of drug treatment models [21][22][23] .…”