Text Mining Electronic Health Records to Identify Hospital Adverse Events

被引：11
|
作者：
Gerdes, Lars Ulrik [1 ]
Hardahl, Christian [2 ]
机构：
[1] Reg Southern Denmark, Ctr Qual, Kolding, Denmark
[2] SAS Inst, Kolding, Denmark
来源：
MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2 | 2013年 / 192卷
关键词：
Text mining; natural language processing; electronic health records; quality of health care; patient safety;
D O I：
10.3233/978-1-61499-289-9-1145
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
Manual reviews of health records to identify possible adverse events are time consuming. We are developing a method based on natural language processing to quickly search electronic health records for common triggers and adverse events. Our results agree fairly well with those obtained using manual reviews, and we therefore believe that it is possible to develop automatic tools for monitoring aspects of patient safety.
引用
收藏
页码：1145 / 1145
页数：1
相关论文
共 50 条
  • [21] The Analysis of Medical Adverse Events Related to Electronic Health Records in Nursing Services
    Tomita, Mika
    Kishi, Natsumi
    Iwasawa, Mariko
    [J]. MEDINFO 2017: PRECISION HEALTHCARE THROUGH INFORMATICS, 2017, 245 : 1340 - 1340
  • [22] Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register
    Iqbal, Ehtesham
    Mallah, Robbie
    Jackson, Richard George
    Ball, Michael
    Ibrahim, Zina M.
    Broadbent, Matthew
    Dzahini, Olubanke
    Stewart, Robert
    Johnston, Caroline
    Dobson, Richard J. B.
    [J]. PLOS ONE, 2015, 10 (08):
  • [23] ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records
    Iqbal, Ehtesham
    Mallah, Robbie
    Rhodes, Daniel
    Wu, Honghan
    Romero, Alvin
    Chang, Nynn
    Dzahini, Olubanke
    Pandey, Chandra
    Broadbent, Matthew
    Stewart, Robert
    Dobson, Richard J. B.
    Ibrahim, Zina M.
    [J]. PLOS ONE, 2017, 12 (11):
  • [24] Electronic Health Record Adoption and Rates of In-hospital Adverse Events
    Furukawa, Michael F.
    Eldridge, Noel
    Wang, Yun
    Metersky, Mark
    [J]. JOURNAL OF PATIENT SAFETY, 2020, 16 (02) : 137 - 142
  • [25] Using text-mining techniques in electronic patient records to identify ADRs from medicine use
    Warrer, Pernille
    Hansen, Ebba Holme
    Juhl-Jensen, Lars
    Aagaard, Lise
    [J]. BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2012, 73 (05) : 674 - 684
  • [26] Machine Learning Based Text Mining in Electronic Health Records: Cardiovascular Patient Cases
    Sikorskiy, Sergey
    Metsker, Oleg
    Yakovlev, Alexey
    Kovalchuk, Sergey
    [J]. COMPUTATIONAL SCIENCE - ICCS 2018, PT III, 2018, 10862 : 818 - 824
  • [27] Learning to extract adverse drug reaction events from electronic health records in Spanish
    Casillas, Arantza
    Perez, Alicia
    Oronoz, Maite
    Gojenola, Koldo
    Santiso, Sara
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2016, 61 : 235 - 245
  • [28] Adverse health correlates of intimate partner violence against older women: Mining electronic health records
    Yilmaz, Serhan
    Gunay, Erkan
    Lee, Da Hee
    Whiting, Kathleen M.
    Silver, Kristin
    Koyuturk, Mehmet
    Karakurt, Gunnur
    [J]. PLOS ONE, 2023, 18 (03):
  • [29] Mining Electronic Health Records to Identify Key Factors Influencing Diagnostic Errors in the Emergency Department
    Patel, S.
    Khalili, M.
    Huschka, T.
    Parker, S.
    Cabrera, D.
    Pasupathy, K.
    Singh, H.
    Mahajan, P.
    Bellolio, F.
    Enayati, M.
    [J]. ANNALS OF EMERGENCY MEDICINE, 2023, 82 (04) : S52 - S52
  • [30] Data mining clinical records to detect adverse events.
    Coster, TS
    Szarfman, A
    Russell, C
    [J]. CLINICAL PHARMACOLOGY & THERAPEUTICS, 2005, 77 (02) : P1 - P1
12345