A de-identifier for medical discharge summaries.
Objective: Clinical records contain significant medical information that can be useful to researchers in various disciplines. However, these records also contain personal health information (PHI) whose presence limits the use of the records outside of hospitals. The goal of de-identification is to r...
| Publicado en: | Artificial Intelligence in Medicine Vol. 42; no. 1; pp. 13 - 36 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jan2008
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105746421&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105746421 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jan2008 vid: 42 iid: 1 pid: 1004 pub: Elsevier B.V. artinfo: ui: 105746421 105746421 NLM18053696 2009772171 10.1016/j.artmed.2007.10.001 NLM18053696 PMC2271040 105746421 ppf: 13 ppct: 23 formats: tig: atl: A de-identifier for medical discharge summaries. aug: au: Uzuner O Sibanda TC Luo Y Szolovits P Uzuner, Ozlem Sibanda, Tawanda C Luo, Yuan Szolovits, Peter affil: University at Albany, State University of New York, Draper 114, Albany, NY 12222, USA sug: subj: Natural Language Processing Patient Discharge Patient Record Systems Administration Semantics Information Science Methods Privacy and Confidentiality Research, Medical Human ab: Objective: Clinical records contain significant medical information that can be useful to researchers in various disciplines. However, these records also contain personal health information (PHI) whose presence limits the use of the records outside of hospitals. The goal of de-identification is to remove all PHI from clinical records. This is a challenging task because many records contain foreign and misspelled PHI; they also contain PHI that are ambiguous with non-PHI. These complications are compounded by the linguistic characteristics of clinical records. For example, medical discharge summaries, which are studied in this paper, are characterized by fragmented, incomplete utterances and domain-specific language; they cannot be fully processed by tools designed for lay language.Methods and Results: In this paper, we show that we can de-identify medical discharge summaries using a de-identifier, Stat De-id, based on support vector machines and local context (F-measure=97% on PHI). Our representation of local context aids de-identification even when PHI include out-of-vocabulary words and even when PHI are ambiguous with non-PHI within the same corpus. Comparison of Stat De-id with a rule-based approach shows that local context contributes more to de-identification than dictionaries combined with hand-tailored heuristics (F-measure=85%). Comparison with two well-known named entity recognition (NER) systems, SNoW (F-measure=94%) and IdentiFinder (F-measure=36%), on five representative corpora show that when the language of documents is fragmented, a system with a relatively thorough representation of local context can be a more effective de-identifier than systems that combine (relatively simpler) local context with global context. Comparison with a Conditional Random Field De-identifier (CRFD), which utilizes global context in addition to the local context of Stat De-id, confirms this finding (F-measure=88%) and establishes that strengthening the representation of local context may be more beneficial for de-identification than complementing local with global context. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|