دورية أكاديمية
Early Detection of Post-Surgical Complications using Time-series Electronic Health Records.
| العنوان: | Early Detection of Post-Surgical Complications using Time-series Electronic Health Records. |
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| المؤلفون: | Chen D; Division of Digital Health Sciences., Jiang J; Division of Digital Health Sciences., Fu S; Division of Digital Health Sciences., Demuth G; Department of Health Science Research., Liu S; Division of Digital Health Sciences., Schaeferle GM; Department of Health Science Research., Wilson PM; Department of Health Science Research., Habermann E; Department of Health Science Research., Larson DW; Department of Colorectal Surgery, Mayo Clinic, Rochester, MN, USA., Storlie C; Department of Health Science Research., Liu H; Division of Digital Health Sciences. |
| المصدر: | AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2021 May 17; Vol. 2021, pp. 152-160. Date of Electronic Publication: 2021 May 17 (Print Publication: 2021). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: AMIA Country of Publication: United States NLM ID: 101539486 Publication Model: eCollection Cited Medium: Internet ISSN: 2153-4063 (Electronic) NLM ISO Abbreviation: AMIA Jt Summits Transl Sci Proc Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bethesda, MD : AMIA, [2011]- |
| مواضيع طبية MeSH: | Electronic Health Records*, Early Diagnosis ; Humans |
| مستخلص: | Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets. (©2021 AMIA - All rights reserved.) |
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| تواريخ الأحداث: | Date Created: 20210830 Date Completed: 20210910 Latest Revision: 20230920 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC8378618 |
| PMID: | 34457129 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2153-4063 |
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