Text Mining in Electronic Medical Records Enables Quick and Efficient Identification of Pregnancy Cases Occurring After Breast Cancer

Labrosse, Julie; Lam, Thanh; Sebbag, Clara; Benque, Milena; Abdennebi, I; Merckelbagh, Hilde; Osdoit, Marie; Priour, Maël; Guérin, J.; Balezeau, Thomas; Grandal, Beatriz; Bobrie, Angélique; Ferrer, L.; Laas, Enora; Féron, Jean-Guillaume; Reyal, Fabien; Hamy, Anne-Sophie

doi:10.1200/cci.19.00031

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…Ensuring sufficient preparation of such data is particularly important, as research has found that the application of text mining to medical records can expedite information extraction and enhance health-related outcomes. For example, Labrosse et al (57) extracted information from 344 patients' electronic health records using both manual tracking and text mining techniques to identify the rare event of pregnancy following breast cancer (incidence rate of 0.01 pregnancy per person-year after breast cancer diagnosis). The authors found that text mining was more efficient than manual tracking (13 vs. 244 min, respectively) in identifying rare events in electronic health records.…”

Section: Discussionmentioning

confidence: 99%

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Ameli¹,

Gibson²,

Khanna³

et al. 2022

Front. Dent. Med

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ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

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Section: Discussionmentioning

confidence: 99%

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Ameli¹,

Gibson²,

Khanna³

et al. 2022

Front. Dent. Med

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show abstract

“…The number of encounters or ICD-9/10 codes have been found to be a useful component of algorithms used to identify patient populations, but it is widely recognized that more than one type of data are necessary to reliably identify a clinical population [10]. Further, the use of one or two EMR-derived data types is generally limited in classification ability, with better ability to identify pregnancy or birth outcomes achieved when more data types or complex data derivation techniques, such as text mining, are applied [23,24]. Along these lines, we did detect a statistically significant difference in the proportion of women with an EMR HDP diagnosis between women with versus without at least one or two hypertensive EMR BP readings.…”

Section: Discussionmentioning

confidence: 99%

Agreement between blood pressure from research study visits versus electronic medical records and associations with hypertensive disorder diagnoses in pregnant women with overweight/obesity

Lane‐Cordova

Wilcox

Fernhall

et al. 2021

Blood Pressure Monitoring

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Objectives Blood pressure (BP) abstracted from electronic medical records (EMR) is moderately correlated to BP in nonpregnant adults with limited agreement. Little is known about the agreement of research versus EMR BP measured during pregnancy or associations of EMR BP with hypertensive disorder of pregnancy (HDP) diagnoses. Methods BP was measured according to guidelines at in-person research study visits in 214 women with prepregnancy overweight or obesity (44.4% African American, mean age = 29.8 ± 4.8 years) at weeks 16 and 32 of pregnancy. Clinic BP readings that occurred within 1 week of the study visits were abstracted from the EMR. We assessed correlations between sources using Pearson’s coefficients; the agreement was evaluated with Bland–Altman plots. We compared differences in the proportion of women with an HDP diagnosis in the EMR between women with versus without a hypertensive EMR BP measurement. Results SBP and DBP from study visits and the EMR were modestly moderately correlated at both time points; 0.20 < r < 0.44; P < 0.05 for all. The average mean difference was 10.5 mmHg for SBP and <1 mmHg for DBP in early and 7.3 mmHg for SBP and −1.7 mmHg for DBP in late pregnancy. Women with at least one hypertensive BP reading in the EMR were more likely to have an HDP diagnosis recorded in the EMR; 43.5 versus 3.3%; P < 0.01. Conclusion EMR SBP was higher but moderately correlated with research quality BP in early and late pregnancy. Women with a hypertensive EMR BP measurement were more likely to have an HDP diagnosis in the EMR.

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“…Pregnancy cases were identified by text mining technology as previously described [ 35 ]. Briefly, the text mining approach consisted in applying a keyword filter (“accouch *” or “enceinte”, French terms for “deliver *” and “pregnant”, respectively) on the medical electronic records of patients to select a subset of files.…”

Section: Methodsmentioning

confidence: 99%

Time to Pregnancy, Obstetrical and Neonatal Outcomes after Breast Cancer: A Study from the Maternity Network for Young Breast Cancer Patients

Labrosse

Lecourt

Hours

et al. 2021

Cancers

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Although an increasing number of young breast cancer (BC) patients have a pregnancy desire after BC, the time necessary to obtain a pregnancy after treatment and subsequent outcomes remain unknown. We aimed to determine the time to evolutive pregnancy in a cohort of BC survivors and subsequent obstetrical and neonatal outcomes. We analyzed BC patients treated at Institut Curie from 2005–2017, aged 18–43 years old (y.o.) at diagnosis having at least one subsequent pregnancy. 133 patients were included, representing 197 pregnancies. Mean age at BC diagnosis was 32.8 y.o. and at pregnancy beginning was 36.8 y.o. 71% pregnancies were planned, 18% unplanned and 86% spontaneous. 64% pregnancies resulted in live birth (n = 131). Median time from BC diagnosis to pregnancy beginning was 48 months and was significantly associated with endocrine therapy (p < 0.001). Median time to pregnancy was 4.3 months. Median time to evolutive pregnancy 5.6 months. In multivariate analysis, menstrual cycles before pregnancy remained significantly associated with time to pregnancy and endocrine therapy with time evolutive to pregnancy. None of the BC treatments (chemotherapy/endocrine therapy/trastuzumab) was significantly associated with obstetrical nor neonatal outcomes, that seemed comparable to global population. Our findings provide reassuring data for pregnancy counseling both in terms of delay and outcome.

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Text Mining in Electronic Medical Records Enables Quick and Efficient Identification of Pregnancy Cases Occurring After Breast Cancer

Cited by 10 publications

References 20 publications

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Agreement between blood pressure from research study visits versus electronic medical records and associations with hypertensive disorder diagnoses in pregnant women with overweight/obesity

Time to Pregnancy, Obstetrical and Neonatal Outcomes after Breast Cancer: A Study from the Maternity Network for Young Breast Cancer Patients

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