Acquisition of temporal patterns from electronic health records: an application to multimorbid patients
Abstract Background The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illne...
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02287-0 |
| _version_ | 1827634024135262208 |
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| author | Alicia Ageno Neus Català Marcel Pons |
| author_facet | Alicia Ageno Neus Català Marcel Pons |
| author_sort | Alicia Ageno |
| collection | DOAJ |
| description | Abstract Background The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish. Methods We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances. Results As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients. Conclusion Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool. |
| first_indexed | 2024-03-09T15:07:40Z |
| format | Article |
| id | doaj.art-32e1446305fb45a1bf5bd2d0d45308aa |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-09T15:07:40Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-32e1446305fb45a1bf5bd2d0d45308aa2023-11-26T13:32:15ZengBMCBMC Medical Informatics and Decision Making1472-69472023-09-0123111510.1186/s12911-023-02287-0Acquisition of temporal patterns from electronic health records: an application to multimorbid patientsAlicia Ageno0Neus Català1Marcel Pons2TALP Research Center, Computer Science Department, Universitat Politècnica de Catalunya (UPC)TALP Research Center, Computer Science Department, Universitat Politècnica de Catalunya (UPC)Facultat d’Informàtica de Barcelona, Universitat Politècnica de Catalunya (UPC)Abstract Background The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish. Methods We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances. Results As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients. Conclusion Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.https://doi.org/10.1186/s12911-023-02287-0Electronic health recordsTemporal data miningTemporal association rulesClinical decision support systemsRisk factors detection |
| spellingShingle | Alicia Ageno Neus Català Marcel Pons Acquisition of temporal patterns from electronic health records: an application to multimorbid patients BMC Medical Informatics and Decision Making Electronic health records Temporal data mining Temporal association rules Clinical decision support systems Risk factors detection |
| title | Acquisition of temporal patterns from electronic health records: an application to multimorbid patients |
| title_full | Acquisition of temporal patterns from electronic health records: an application to multimorbid patients |
| title_fullStr | Acquisition of temporal patterns from electronic health records: an application to multimorbid patients |
| title_full_unstemmed | Acquisition of temporal patterns from electronic health records: an application to multimorbid patients |
| title_short | Acquisition of temporal patterns from electronic health records: an application to multimorbid patients |
| title_sort | acquisition of temporal patterns from electronic health records an application to multimorbid patients |
| topic | Electronic health records Temporal data mining Temporal association rules Clinical decision support systems Risk factors detection |
| url | https://doi.org/10.1186/s12911-023-02287-0 |
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