Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study

Huang, Kexin; Gray, Tamryn F.; Romero-Brufau, Santiago; Tulsky, James A.

doi:10.1093/jamia/ocab051

Cited by 16 publications

(25 citation statements)

References 33 publications

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“…Neural language models are the default choice for building predictive models that exploit text. [11][12][13][14][15] An end-to-end deep dynamic neural framework was proposed by Pham et al 11 to predict future medical outcomes such as the next diagnosis, current interventions from the current diagnoses, and future risks like unplanned readmission within a certain period. In 2021, Van Aken et al 12 proposed a transformerbased model for predicting multiple clinical outcomes such as the International Statistical Classification of Diseases and Related Health Problems-Ninth Edition (ICD-9) diagnosis, ICD-9 procedures, in-hospital mortality, and length of ICU stay, using the discharge summary from the MIMIC data set.…”

Section: Impact Statementmentioning

confidence: 99%

“…Chrusciel et al 13 proposed the use of random forest along with a word-embedding algorithm based on the Unified Medical Language System (UMLS) terminology, to predict hospital length of stay from unstructured EHRs. Huang et al 14 also investigated the use of physicians and nursing notes generated within the first 48 h of admissions, in predicting ICU length of stay and mortality. They have also utilized the MIMIC database in their study.…”

Section: Impact Statementmentioning

confidence: 99%

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Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Jana

Dasgupta

Dey

2022

Exp Biol Med (Maywood)

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Effective utilization of premium hospital resources such as intensive care unit (ICU), operating theater (OT), mechanical ventilator, endotracheal tube, and so on plays a significant role in providing high-quality care to critically ill patients within reasonable costs. Non-availability of specialized resources can lead to dire consequences for such patients, and in the worst case, may even turn out to be fatal. However, these resources cannot be kept idle, as they are expensive to maintain. Therefore, one of the core functions of hospital management is targeted at planning and managing these critical resources in order to provide efficient and effective health-care services to the end-users. Predictive technologies play a big role in this. In this article, we present methods for predicting the length of stay in ICU as well as the need for critical interventions for a patient based on the vital signs, laboratory measurements, and the nursing notes of the patient prepared within the first 24 h of ICU stay. The model has been built and cross-validated on the publicly available Medical Information Mart for Intensive Care (MIMIC-III v1.4) data set. We show that the proposed model performs way better than most of the earlier models in the prediction of ICU stay, which had used patient vitals primarily. Experimental results also demonstrate the advantage of using a multiobjective model over independent models for the prediction of ICU stay and critical interventions. The proposed model uses Local Interpretable Model-agnostic Explanations (LIME) that help in identifying the features responsible for predictive decisions. This is very useful in building trust and confidence in the prediction model among clinical practitioners.

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Section: Impact Statementmentioning

confidence: 99%

Section: Impact Statementmentioning

confidence: 99%

Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Jana

Dasgupta

Dey

2022

Exp Biol Med (Maywood)

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“…Approximately 40% of the studies (n = 17) 15,19,24,28,30,33-38, 41,43,47,49,52,53 included only nursing notes, whereas more than 53% of the studies (n = 23) 9,12,[16][17][18]20,22,23,[25][26][27]29,31,32,39,40,42,[44][45][46]48,50,51,56 included nursing notes and other types of notes written by physicians and other allied health professionals. Natural language processing in nursing notes was primarily conducted in inpatient settings (n = 26), 9,[15][16][17][18]20,24,27,31,32,[36][37][38][39][40][41][42][43][44][45][46][47][48]50,…”

Section: Study Purpose Data Source and Patient Populationmentioning

confidence: 99%

Natural Language Processing of Nursing Notes

Mitha

Schwartz

Hobensack

et al. 2022

CIN: Computers, Informatics, Nursing

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Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.

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“…Sentiment analysis, which is a technique that processes natural language, helps to identify the attitudes or impressions of clinicians to patients using computational algorithms for extracting subjective information in the written text and classifying subjective properties ( 13 ). In previous studies, nursing notes were applied in prediction model to improve the clinical outcome prediction of patients with various diseases ( 12 , 14 , 15 ). Whether nursing notes could improve the predictive accuracy of model for the mortality of AKI patients requiring CRRT remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

Zha¹,

Yang²,

Hai-fen³

et al. 2022

Ann Transl Med

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Background: Currently, the prediction values of models for the prognosis of acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT) were ordinary and establishing a better prediction model is necessary. Nursing notes are an important predictor of in-hospital mortality in intensive care unit (ICU) patients. This study established prognostic prediction models for AKI patients receiving CRRT especially using nursing notes.Methods: Totally, 682 AKI patients undergoing CRRT were included. AKI was diagnosed based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Four hundred and twelve patients lacking nursing notes data were excluded. Finally, 270 patients were included and randomly divided into a training set (n=189) and a testing set (n=81) at a ratio of 7:3. Univariate analysis explored the possible predictors of mortality in AKI patients receiving CRRT. Random forest models and broad learning system (BLS) models (with or without sentiment scores) were respectively constructed in the training set and verified in the testing set. The performances of the models were assessed by the sensitivity, specificity, and area under the curve (AUC).Results: For the random forest model including the sentiment scores, the AUC was 0.86 (95% CI: 0.81-0.91), the sensitivity was 0.72 (95% CI: 0.63-0.80), and the specificity was 0.87 (95% CI: 0.80-0.94) in the training set and the AUC was 0.78 (95% CI: 0.68-0.88), the sensitivity was 0.65 (95% CI: 0.49-0.80), and the specificity was 0.75 (95% CI: 0.62-0.88) in the testing set. For the BLS model including the sentiment scores, the AUC was 0.87 (95% CI: 0.82-0.92), the sensitivity was 0.95 (95% CI: 0.91-0.99) and the specificity was 0.48 (95% CI: 0.38-0.59) in the training set and the AUC was 0.82 (95% CI: 0.73-0.91), the sensitivity was 0.41 (95% CI: 0.25-0.56) and the specificity was 0.98 (95% CI: 0.93-1.00) in the testing set. Conclusions:The BLS models including the sentiment scores might offer a tool for quickly identifying patients AKI patients receiving CRRT with high risk of mortality and providing timely interventions to them for improving their prognosis.

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Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study

Cited by 16 publications

References 33 publications

Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Natural Language Processing of Nursing Notes

Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

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