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
  • [1] Application of Text Mining Methods to Identify Lupus Nephritis from Electronic Health Records
    Gianfrancesco, Milena
    Tamang, Suzanne
    Schmajuk, Gabriela
    Yazdany, Jinoos
    [J]. ARTHRITIS & RHEUMATOLOGY, 2020, 72
  • [2] Development of a text mining algorithm for identifying adverse drug reactions in electronic health records
    van de Burgt, Britt W. M.
    Wasylewicz, Arthur T. M.
    Dullemond, Bjorn
    Jessurun, Naomi T.
    Grouls, Rene J. E.
    Bouwman, R. Arthur
    Korsten, Erik H. M.
    Egberts, Toine C. G.
    [J]. JAMIA OPEN, 2024, 7 (03)
  • [3] Empirical advances with text mining of electronic health records
    Delespierre, T.
    Denormandie, P.
    Bar-Hen, A.
    Josseran, L.
    [J]. BMC MEDICAL INFORMATICS AND DECISION MAKING, 2017, 17
  • [4] Empirical advances with text mining of electronic health records
    T. Delespierre
    P. Denormandie
    A. Bar-Hen
    L. Josseran
    [J]. BMC Medical Informatics and Decision Making, 17
  • [5] Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review
    Feng, Chenchen
    Le, David
    McCoy, Allison B.
    [J]. APPLIED CLINICAL INFORMATICS, 2019, 10 (01): : 123 - 128
  • [6] USING ELECTRONIC HEALTH RECORDS TO IDENTIFY ADVERSE DRUG EVENTS IN AMBULATORY CARE: A SYSTEMATIC REVIEW
    Feng, C.
    McCoy, A. B.
    [J]. JOURNAL OF INVESTIGATIVE MEDICINE, 2018, 66 (02) : 424 - 425
  • [7] Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
    Zhou, Shang-Ming
    Muhammad, Rahman A.
    Sheppard, Samuel
    Howe, Robin
    Lyons, Ronan A.
    Brophy, Sinead
    [J]. LANCET, 2017, 390 : S99 - S99
  • [8] Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus
    Brunekreef, Tammo E.
    Otten, Henny G.
    van den Bosch, Suzanne C.
    Hoefer, Imo E.
    van Laar, Jacob M.
    Limper, Maarten
    Haitjema, Saskia
    [J]. ACR OPEN RHEUMATOLOGY, 2021, 3 (02) : 65 - 71
  • [9] Research on Text Data Mining of Hospital Patient Records Within Electronic Medical Records
    Kushima, Muneo
    Araki, Kenji
    Suzuki, Muneou
    Yamazaki, Tomoyoshi
    Sonehara, Noboru
    [J]. 2014 JOINT 7TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 15TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS), 2014, : 1500 - 1505
  • [10] Mining Adverse Drug Reactions from Electronic Health Records
    Lo, Henry Z.
    Ding, Wei
    Nazeri, Zohreh
    [J]. 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW), 2013, : 1137 - 1140
12345