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      A clinical text classification paradigm using weak supervision and deep representation.

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          Abstract

          Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Machine learning approaches have been shown to be effective for clinical text classification tasks. However, a successful machine learning model usually requires extensive human efforts to create labeled training data and conduct feature engineering. In this study, we propose a clinical text classification paradigm using weak supervision and deep representation to reduce these human efforts.

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          Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project.

          Walter A Rocca, Barbara P. Yawn, L. Joseph Melton … (2011)
          The Rochester Epidemiology Project (REP) is a unique research infrastructure in which the medical records of virtually all persons residing in Olmsted County, Minnesota, for over 40 years have been linked and archived. In the present article, the authors describe how the REP links medical records from multiple health care institutions to specific individuals and how residency is confirmed over time. Additionally, the authors provide evidence for the validity of the REP Census enumeration. Between 1966 and 2008, 1,145,856 medical records were linked to 486,564 individuals in the REP. The REP Census was found to be valid when compared with a list of residents obtained from random digit dialing, a list of residents of nursing homes and senior citizen complexes, a commercial list of residents, and a manual review of records. In addition, the REP Census counts were comparable to those of 4 decennial US censuses (e.g., it included 104.1% of 1970 and 102.7% of 2000 census counts). The duration for which each person was captured in the system varied greatly by age and calendar year; however, the duration was typically substantial. Comprehensive medical records linkage systems like the REP can be used to maintain a continuously updated census and to provide an optimal sampling framework for epidemiologic studies.
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            Clinical information extraction applications: A literature review

            Sunghwan Sohn, Hongfang Liu, Yanshan Wang … (2018)
            With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.
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              What can natural language processing do for clinical decision support?

              Dina Demner-Fushman, Wendy W Chapman, Clement J McDonald (2009)
              Computerized clinical decision support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. natural language processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.
              Article 0 comments Cited 127 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (iso-abbrev): BMC Med Inform Decis Mak
                Title: BMC medical informatics and decision making
                Publisher: Springer Science and Business Media LLC
                ISSN (Electronic): 1472-6947
                ISSN (Print): 1472-6947
                Publication date (Electronic): January 07 2019
                Volume: 19
                Issue: 1
                Affiliations
                [1 ] Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st ST SW, Rochester, MN, 55905, USA. wang.yanshan@mayo.edu.
                [2 ] Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st ST SW, Rochester, MN, 55905, USA.
                [3 ] Division of Rheumatology, Department of Medicine, Mayo Clinic, 200 1st ST SW, Rochester, MN, 55905, USA.
                [4 ] Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, 200 1st ST SW, Rochester, MN, 55905, USA.
                [5 ] Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st ST SW, Rochester, MN, 55905, USA. liu.hongfang@mayo.edu.
                Article
                Publisher Item ID: 10.1186/s12911-018-0723-6
                DOI: 10.1186/s12911-018-0723-6
                PMC ID: 6322223
                PubMed ID: 30616584
                SO-VID: a4edfc04-84f1-417a-85e2-3d9c846f0218
                History

                Keywords: Clinical text classification,Electronic health records,Machine learning,Natural language processing,Weak supervision
                Data availability:
                Keywords: Clinical text classification, Electronic health records, Machine learning, Natural language processing, Weak supervision

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