Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke

Sung, Sheng-Feng; Chen, Chih‐Hao; Pan, Ru-Chiou; Hu, Ya-Han; Jeng, Jiann‐Shing

doi:10.1161/jaha.121.023486

Cited by 18 publications

(9 citation statements)

References 44 publications

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“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

Section: Resultsmentioning

confidence: 99%

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,[23][24][25][26][27]29,30,[33][34][35][36]40,[42][43][44][45][46][48][49][50][51][52]54,55,[57][58][59][60][61][62][63][64][66][67][68][70][71][72][73]75,[79][80][81][82][83][84][85][86][87][88][89]…”

Section: Sources Considered For Data Processingmentioning

confidence: 99%

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The use of natural language processing in palliative care research: A scoping review

et al. 2022

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Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

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Section: Resultsmentioning

confidence: 99%

Section: Sources Considered For Data Processingmentioning

confidence: 99%

The use of natural language processing in palliative care research: A scoping review

et al. 2022

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“…Furthermore, the ubiquitous adoption of electronic health record (EHR) systems provides an opportunity to use various types of structured and unstructured data for data-driven prediction of clinical outcomes ( 17 – 19 ). Using natural language processing techniques, information extracted from unstructured clinical text has the potential to improve the performance of clinical prediction models ( 20 , 21 ). Inspired by these ideas, we aimed to explore the value of combining both structured and unstructured textual data in developing ML models to predict SAP.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

Tsai

Hsieh

Sung

2022

Front. Public Health

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BackgroundIdentifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A2DS2, ISAN, PNA, and ACDD4 scores using the area under the receiver operating characteristic curve (AUC).ResultsAmong 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806–0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793–0.863, P = 0.040), ACDD4 (0.807, 95% CI 0.766–0.849, P = 0.041), A2DS2 (0.803, 95% CI 0.762–0.845, P = 0.013), ISAN (0.795, 95% CI 0.752–0.837, P = 0.009), and PNA (0.778, 95% CI 0.735–0.822, P < 0.001). All models demonstrated adequate calibration except for the A2DS2 score.ConclusionsThe ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.

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“…Furthermore, in addition to structured numerical and categorical data, EHRs accommodate a multitude of unstructured textual data such as narrative clinical notes. Combining information extracted from clinical free text through natural language processing with structured data has shown promising results in improving the performance of risk prediction models (26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

“…A higher risk of NDAF has been observed in patients with greater stroke severity (22,31). Previous studies have shown that information extracted from clinical text can be used to represent patients' stroke severity (28,32). Furthermore, stroke patients with AF have a higher prevalence of heart diseases and experience more cardiac events than those without AF (29)(30)(31).…”

Section: Introductionmentioning

confidence: 99%

Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing

Sung

Sung²,

Pan³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundTimely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores.ResultsThe study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores.ConclusionsIt is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.

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Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke

Cited by 18 publications

References 44 publications

The use of natural language processing in palliative care research: A scoping review

The use of natural language processing in palliative care research: A scoping review

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing

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