Related Experiment Video
Updated: May 15, 2025
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Discovering sequential patterns and interrelations among multiple diseases in electronic medical records using cSPADE
He Ma1,2, Qianxin Huang3, Hong Zhang4
1School of Information and Control Engineering, China University of Mining and Technology, No.1 Daxue Road, Xuzhou, 221000, Jiangsu, P.R. China.
Insights
This study reveals significant sequential disease patterns and time intervals between diagnoses, offering insights into comorbidity. Findings highlight gender-specific disease progressions, aiding in clinical decision support.
Area of Science:
- Computational epidemiology
- Health informatics
- Biostatistics
Background:
- Understanding disease onset sequences is crucial for comorbidity research and predicting patient outcomes.
- Temporal disease relationships inform disease progression and intervention strategies.
Purpose of the Study:
- To investigate interdependencies and chronological disease order using sequential pattern mining.
- To analyze time intervals between distinct disorder onsets.
- To examine gender-based differences in disease sequence patterns.
Main Methods:
- Utilized electronic medical record data from 269,973 patients (2012-2022).
- Employed the Sequential Pattern Discovery using Equivalence Classes (SPADE) algorithm.
- Analyzed 1,060,344 diagnostic entries with International Classification of Diseases, Tenth Revision (ICD-10) codes.
Main Results:
- Identified 212 significant sequential comorbidity patterns, primarily involving endocrine and circulatory systems.
- Disease onset intervals varied from under 2 months to 5-10 years, with many between 1-2 years.
- 176 patterns showed stronger support in males; cardiovascular/liver diseases were more common in males, orthopedic/endocrine in females.
Conclusions:
- The constrained SPADE (cSPADE) algorithm is effective for uncovering clinically relevant sequential comorbidity patterns.
- Identified patterns can advance disease prevention, etiological research, and clinical decision support systems.
Background:
The intricate relationships between diseases are characterized by the sequence and temporal intervals of their onset, which are critical for understanding the essence of comorbidity and predicting disease progression. This study seeks to investigate the interdependencies and chronological order of various diseases that occur in the same patient by employing sequential pattern mining algorithms. Specifically, the research endeavors to delineate the disparities in the time intervals between the onset of distinct disorders and to scrutinize the concordance and discordance in disease sequence patterns across gender groups.
Methods:
Patient identity information, visit dates, and diagnostic data were aggregated from the electronic medical record databases of three large general hospitals. The diagnostic information included the International Classification of Diseases, Tenth Revision (ICD-10) codes, along with their corresponding descriptions. A total of 1,060,344 diagnostic entries from 269,973 patients who visited during 2012-2022 were incorporated into the mining model, which was constructed using the Sequential Pattern Discovery using Equivalence Classes (SPADE) algorithm.
Results:
A total of 212 highly supported sequential pattern rules were ultimately identified, most of which were related to disorders of the endocrine and circulatory systems. In 66 patterns, the order of disease incidence or diagnosis was relatively well-defined. The time interval between the onset of two diseases ranged from 1 to 2 years in most patterns. For patterns with short-term relationships, the interval was less than 2 months, whereas in some cases, the interval extended to 5 to 10 years. Among the extracted patterns, 176 exhibited stronger support in the male dataset compared to the female dataset. Patterns related to cardiovascular and liver diseases were more prevalent in males, while those associated with orthopedic and endocrine disorders showed higher prevalence in females.
Conclusion:
Our findings demonstrate the effectiveness of the constrained SPADE (cSPADE) algorithm in comorbidity research and highlight several clinically significant sequential comorbidity patterns. These patterns are expected to contribute to disease prevention, etiological research, and the development of clinical decision support systems.
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
01:15Methods of Documentation VI: Case Management Model
For example, a patient with a chronic...
01:12Statistical Software for Data Analysis and Clinical Trials