1. Medication Use Pattern Mining for Childhood Pneumonia Using Six Year Inpatient Electronic Medical Records in a Shanghai Hospital, China Chunlei Tang, PhD1,2,3, Jiahong Yang, MPH4, Christopher J. Vitale, PharmD5,#a, Lu Ruan6,7, Angela C. Ai1, Yun Xiong, PhD6,7*, Guangjun Yu, MD8,9*, David W. Bates, MD1,2,3&, Jing Ma, MD, PhD10&
Key Points
Introduction to Children Hospital of Shanghai
Funding Supports
Question: What are the common in-hospital medication or treatment patterns for pediatric pneumonia in Chinese hospitals?
Findings: We applied three pattern mining algorithms on 680,138 electronic medical records from 30,512 pediatric inpatient cases with a diagnosis of pneumonia over a six year period. Each algorithm selected the top ten patterns used for six age groups to form a total of 180 distinct medication combinations. Results included five medication use patterns and two treatment patterns which deserve further investigation.
Meaning: Pattern mining identified expected and unexpected medication use patterns by which more well-defined research questions can be formulated for further study.
Supported by grant AA from the National High Technology Research and Development Program of China
The Children’s Hospital of Shanghai (CHS), one of the top comprehensive children’s hospitals in China, admits approximately 5,000 inpatients annually. Formerly known as Underprivileged Children’s Hospital, it was one of the earliest founded children’s hospitals in Asia.
Author Affiliations
Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA;
Harvard Medical School, Boston, MA, USA;
Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA;
Shanghai Shenkang Hospital Development Center, Shanghai, CHN;
Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA;
Shanghai Key Laboratory of Data Science, Shanghai, CHN;
School of Computer Science, Fudan University, Shanghai, CHN;
Children’s Hospital of Shanghai, Shanghai, CHN;
Shanghai Jiaotong University School of Medicine, Shanghai, CHN.
Department of Population Medicine, Harvard Medical School, Boston, MA, USA;
#a Current Address: Clinical Informatician for the Integrated Health Model Initiative, American Medical Association, Boston, MA, USA
* Corresponding authors & Senior Authors
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2. Introduction to Children Hospital of Shanghai
Medication Use Pattern Mining for Childhood Pneumonia Using Six Year Inpatient Electronic Medical Records in a Shanghai Hospital, China Chunlei Tang, PhD1,2,3, Jiahong Yang, MPH4¶, Christopher J. Vitale, PharmD5,#a, Lu Ruan6,7¶, Angela C. Ai1, Yun Xiong, PhD6,7*, Guangjun Yu, MD8,9*, David W. Bates, MD1,2,3&, Jing Ma, MD, PhD10&
Key Points
Introduction to Children Hospital of Shanghai
Funding Supports
Question: What are the common in-hospital medication or treatment patterns for pediatric pneumonia in Chinese hospitals?
Findings: We applied three pattern mining algorithms on 680,138 electronic medical records from 30,512 pediatric inpatient cases with a diagnosis of pneumonia over a six year period. Each algorithm selected the top ten patterns used for six age groups to form a total of 180 distinct medication combinations. Results included five medication use patterns and two treatment patterns which deserve further investigation.
Meaning: Pattern mining identified expected and unexpected medication use patterns by which more well-defined research questions can be formulated for further study.
Supported by grant AA from the National High Technology Research and Development Program of China
The Children’s Hospital of Shanghai (CHS), one of the top comprehensive children’s hospitals in China, admits approximately 5,000 inpatients annually. Formerly known as Underprivileged Children’s Hospital, it was one of the earliest founded children’s hospitals in Asia.
Author Affiliations
Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA;
Harvard Medical School, Boston, MA, USA;
Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA;
Shanghai Shenkang Hospital Development Center, Shanghai, CHN;
Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA;
Shanghai Key Laboratory of Data Science, Shanghai, CHN;
School of Computer Science, Fudan University, Shanghai, CHN;
Children’s Hospital of Shanghai, Shanghai, CHN;
Shanghai Jiaotong University School of Medicine, Shanghai, CHN.
Department of Population Medicine, Harvard Medical School, Boston, MA, USA;
#a Current Address: Clinical Informatician for the Integrated Health Model Initiative, American Medical Association, Boston, MA, USA
* Corresponding authors & Senior Authors
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3. Medication Use Pattern Mining for Childhood Pneumonia Using Six Year Inpatient Electronic Medical Records in a Shanghai Hospital, China Chunlei Tang, PhD1,2,3, Jiahong Yang, MPH4¶, Christopher J. Vitale, PharmD5,#a, Lu Ruan6,7¶, Angela C. Ai1, Yun Xiong, PhD6,7*, Guangjun Yu, MD8,9*, David W. Bates, MD1,2,3&, Jing Ma, MD, PhD10&
Importance
Main Outcome and Measures
Objective
Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns of real world data. This technique could have a huge potential application in mining electronic medical records (EMRs); but it first requires a careful clinical assessment and validation.
Among the FP-Growth, PrefixSpan, and USpan pattern mining algorithms, the first two more traditional methods mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these three methods identified the top ten patterns from six age groups, forming a total of 180 distinct medication combinations, covering the top 40 (72.0%) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize five medication and two treatment patterns.
Using pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for pediatric pneumonia.
Author Affiliations
Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA;
Harvard Medical School, Boston, MA, USA;
Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA;
Shanghai Shenkang Hospital Development Center, Shanghai, CHN;
Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA;
Shanghai Key Laboratory of Data Science, Shanghai, CHN;
School of Computer Science, Fudan University, Shanghai, CHN;
Children’s Hospital of Shanghai, Shanghai, CHN;
Shanghai Jiaotong University School of Medicine, Shanghai, CHN.
Department of Population Medicine, Harvard Medical School, Boston, MA, USA;
#a Current Address: Clinical Informatician for the Integrated Health Model Initiative, American Medical Association, Boston, MA, USA
* Corresponding authors & Senior Authors
Design, Setting, Participants
We applied three pattern mining algorithms on 680,138 electronic medical records (EMRs) from 30,512 pediatric inpatient cases with diagnosis of pneumonia during a six-year period at a children’s hospital in China. Patients’ age ranged from 0-17 years; 37.5% were 0-3 months old; 86.5% were under 5 years old; 60.3% were male; and 60.1% had a hospital stay of 9-15 days. 3

4. Medication Use Pattern Mining for Childhood Pneumonia Using Six Year Inpatient Electronic Medical Records in a Shanghai Hospital, China Chunlei Tang, PhD1,2,3, Jiahong Yang, MPH4¶, Christopher J. Vitale, PharmD5,#a, Lu Ruan6,7¶, Angela C. Ai1, Yun Xiong, PhD6,7*, Guangjun Yu, MD8,9*, David W. Bates, MD1,2,3&, Jing Ma, MD, PhD10&
Importance
Results
Results
Objective
Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns of real world data. This technique could have a huge potential application in mining electronic medical records (EMRs); but it first requires a careful clinical assessment and validation.
We identified five medication use patterns including the following: 1) antiasthmatics AND expectorants AND corticosteroids; 2) antibiotics AND (antiasthmatics OR expectorants OR corticosteroids); 3) third-generation cephalosporin antibiotics followed by traditional antibiotics; 4) antibiotics AND (medications for enteritis OR skin diseases); and 5) (antiasthmatics OR expectorants OR corticosteroids) AND (medications for enteritis OR skin diseases). We also identified two frequent treatment patterns: 1) 42.1% of medical orders were of intravenous therapy with antibiotics, diluents, and nutritional supplements; 2) 13.1% were of various combinations of inhalation of antiasthmatics, expectorants, and/or corticosteroids.
Using pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for pediatric pneumonia.
Author Affiliations
Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA;
Harvard Medical School, Boston, MA, USA;
Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA;
Shanghai Shenkang Hospital Development Center, Shanghai, CHN;
Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA;
Shanghai Key Laboratory of Data Science, Shanghai, CHN;
School of Computer Science, Fudan University, Shanghai, CHN;
Children’s Hospital of Shanghai, Shanghai, CHN;
Shanghai Jiaotong University School of Medicine, Shanghai, CHN.
Department of Population Medicine, Harvard Medical School, Boston, MA, USA;
#a Current Address: Clinical Informatician for the Integrated Health Model Initiative, American Medical Association, Boston, MA, USA
* Corresponding authors & Senior Authors
Design, Setting, Participants
We applied three pattern mining algorithms on 680,138 electronic medical records (EMRs) from 30,512 pediatric inpatient cases with diagnosis of pneumonia during a six-year period at a children’s hospital in China. Patients’ age ranged from 0-17 years; 37.5% were 0-3 months old; 86.5% were under 5 years old; 60.3% were male; and 60.1% had a hospital stay of 9-15 days.
Conclusions and Relevance 4
Utilizing a pattern mining approach, we summarized five medication patterns, one of which was unexpected. These together with the two treatment patterns warrant further investigation.