Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

DeLisle, Sylvain; South, Brett R.; Anthony, Jill A.; Kalp, Ericka; Gundlapallli, Adi; Curriero, Frank C.; Glass, Greg E.; Samore, Matthew H.; Perl, Trish M.

doi:10.1371/journal.pone.0013377

Cited by 43 publications

(55 citation statements)

References 41 publications

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“…Routine data can cost-effectively facilitate the evaluation of smoking cessation interventions and track trends over time. Smoking status records in secondary care EHRs provide potential supplementary data to those in primary care and such data have been used for monitoring a range of health conditions and behaviours including acute respiratory infections [14] and colonoscopy quality [15], as well as smoking [16]. However, the utility of these data may be challenged by issues of documentation quality and generalisability [17].…”

Section: Introductionmentioning

confidence: 99%

“…Structured fields in EHRs can be used as a source of smoking related information, but there are limitations in the applicability of check boxes in routine clinical practice, particularly for situations such as smoking behaviour where repeated measures may be required for effective surveillance rather than a single collection of information at service entry. Combining structured data with information derived from open-text fields in the EHRs has been found to improve sensitivity and precision in some clinical scenarios [14], but has received relatively little evaluation in mental health care settings for smoking or any other exposure.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

Chang

Robson

et al. 2013

PLoS ONE

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BackgroundHigh smoking prevalence is a major public health concern for people with mental disorders. Improved monitoring could be facilitated through electronic health record (EHR) databases. We evaluated whether EHR information held in structured fields might be usefully supplemented by open-text information. The prevalence and correlates of EHR-derived current smoking in people with severe mental illness were also investigated.MethodsAll cases had been referred to a secondary mental health service between 2008-2011 and received a diagnosis of schizophreniform or bipolar disorder. The study focused on those aged over 15 years who had received active care from the mental health service for at least a year (N=1,555). The ‘CRIS-IE-Smoking’ application used General Architecture for Text Engineering (GATE) natural language processing software to extract smoking status information from open-text fields. A combination of CRIS-IE-Smoking with data from structured fields was evaluated for coverage and the prevalence and demographic correlates of current smoking were analysed.ResultsProportions of patients with recorded smoking status increased from 11.6% to 64.0% through supplementing structured fields with CRIS-IE-Smoking data. The prevalence of current smoking was 59.6% in these 995 cases for whom this information was available. After adjustment, younger age (below 65 years), male sex, and non-cohabiting status were associated with current smoking status.ConclusionsA natural language processing application substantially improved routine EHR data on smoking status above structured fields alone and could thus be helpful in improving monitoring of this lifestyle behaviour. However, limited information on smoking status remained a challenge.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

Chang

Robson

et al. 2013

PLoS ONE

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“…To tackle the integration problem, studies have been conducted on the knowledge level and the data level, respectively. Some rely on domain knowledge to extract a joint patient cohort by dening criteria from dierent data types [4], while others explore the possibility of integrating heterogeneous EHR data prior to or post modeling [5,6,7,8]. The focus of this study is on the latter: analyzing complex longitudinal data.…”

Section: Introductionmentioning

confidence: 99%

Learning from heterogeneous temporal data in electronic health records

Zhao

Papapetrou

Asker

et al. 2017

Journal of Biomedical Informatics

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Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the complex structure of the data, which includes clinical events that are unevenly distributed over time, poses a challenge for standard learning algorithms. Some approaches to modeling temporal data rely on extracting single values from time series; however, this leads to the loss of potentially valuable sequential information. How to better account for the temporality of clinical data, hence, remains an important research question. In this study, novel representations of temporal data in electronic health records are explored. These representations retain the sequential information, and are directly compatible with standard machine learning algorithms. The explored methods are based on symbolic sequence representations of time series data, which are utilized in a number of different ways. An empirical investigation, using 19 datasets comprising clinical measurements observed over time from a real database of electronic health records, shows that using a distance measure to random subsequences leads to substantial improvements in predictive performance compared to using the original sequences or clustering the sequences. Evidence is moreover provided on the quality of the symbolic sequence representation by comparing it to sequences that are generated using domain knowledge by clinical experts. The proposed method creates representations that better account for the temporality of clinical events, which is often key to prediction tasks in the biomedical domain.

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“…Free text has been used for case finding and to assess quality of care in complex conditions such as diabetes and cancer [24,25]. Several authors have shown that including data from free text increases case ascertainment for both acute conditions such as respiratory infections and chronic diseases such as angina [26-28] as well as RA [29] and can enhance estimates of symptoms in cancer presentation by 40% [30]. …”

Section: Discussionmentioning

confidence: 99%

Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?

Ford

Nicholson

Koeling

et al. 2013

BMC Med Res Methodol

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BackgroundPrimary care databases are a major source of data for epidemiological and health services research. However, most studies are based on coded information, ignoring information stored in free text. Using the early presentation of rheumatoid arthritis (RA) as an exemplar, our objective was to estimate the extent of data hidden within free text, using a keyword search.MethodsWe examined the electronic health records (EHRs) of 6,387 patients from the UK, aged 30 years and older, with a first coded diagnosis of RA between 2005 and 2008. We listed indicators for RA which were present in coded format and ran keyword searches for similar information held in free text. The frequency of indicator code groups and keywords from one year before to 14 days after RA diagnosis were compared, and temporal relationships examined.ResultsOne or more keyword for RA was found in the free text in 29% of patients prior to the RA diagnostic code. Keywords for inflammatory arthritis diagnoses were present for 14% of patients whereas only 11% had a diagnostic code. Codes for synovitis were found in 3% of patients, but keywords were identified in an additional 17%. In 13% of patients there was evidence of a positive rheumatoid factor test in text only, uncoded. No gender differences were found. Keywords generally occurred close in time to the coded diagnosis of rheumatoid arthritis. They were often found under codes indicating letters and communications.ConclusionsPotential cases may be missed or wrongly dated when coded data alone are used to identify patients with RA, as diagnostic suspicions are frequently confined to text. The use of EHRs to create disease registers or assess quality of care will be misleading if free text information is not taken into account. Methods to facilitate the automated processing of text need to be developed and implemented.

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Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

Cited by 43 publications

References 41 publications

Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

Learning from heterogeneous temporal data in electronic health records

Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?

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