Dataset Information

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study.

ABSTRACT: BACKGROUND:Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE:The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records obtained from Japanese electronic medical records (EMRs)-using a natural language processing (NLP) algorithm and machine learning. METHODS:The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS:The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS:We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

SUBMITTER: Nakatani H

PROVIDER: S-EPMC7203618 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study.

Nakatani Hayao H Nakao Masatoshi M Uchiyama Hidefumi H Toyoshiba Hiroyoshi H Ochiai Chikayuki C

JMIR medical informatics 20200422 4

<h4>Background</h4>Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly.<h4>Objective</h4>The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records ...[more]

PMID: 32319959

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Project description:BackgroundSystemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW).MethodsWe developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC).ResultsOur best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm.ConclusionOur NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.

Project description:ImportanceInternational Classification of Diseases-coded hospital discharge data do not accurately reflect whether firearm injuries were caused by assault, unintentional injury, self-harm, legal intervention, or were of undetermined intent. Applying natural language processing (NLP) and machine learning (ML) techniques to electronic health record (EHR) narrative text could be associated with improved accuracy of firearm injury intent data.ObjectiveTo assess the accuracy with which an ML model identified firearm injury intent.Design, setting, and participantsA cross-sectional retrospective EHR review was conducted at 3 level I trauma centers, 2 from health care institutions in Boston, Massachusetts, and 1 from Seattle, Washington, between January 1, 2000, and December 31, 2019; data analysis was performed from January 18, 2021, to August 22, 2022. A total of 1915 incident cases of firearm injury in patients presenting to emergency departments at the model development institution and 769 from the external validation institution with a firearm injury code assigned according to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM), in discharge data were included.ExposuresClassification of firearm injury intent.Main outcomes and measuresIntent classification accuracy by the NLP model was compared with ICD codes assigned by medical record coders in discharge data. The NLP model extracted intent-relevant features from narrative text that were then used by a gradient-boosting classifier to determine the intent of each firearm injury. Classification accuracy was evaluated against intent assigned by the research team. The model was further validated using an external data set.ResultsThe NLP model was evaluated in 381 patients presenting with firearm injury at the model development site (mean [SD] age, 39.2 [13.0] years; 348 [91.3%] men) and 304 patients at the external development site (mean [SD] age, 31.8 [14.8] years; 263 [86.5%] men). The model proved more accurate than medical record coders in assigning intent to firearm injuries at the model development site (accident F-score, 0.78 vs 0.40; assault F-score, 0.90 vs 0.78). The model maintained this improvement on an external validation set from a second institution (accident F-score, 0.64 vs 0.58; assault F-score, 0.88 vs 0.81). While the model showed some degradation between institutions, retraining the model using data from the second institution further improved performance on that site's records (accident F-score, 0.75; assault F-score, 0.92).Conclusions and relevanceThe findings of this study suggest that NLP ML can be used to improve the accuracy of firearm injury intent classification compared with ICD-coded discharge data, particularly for cases of accident and assault intents (the most prevalent and commonly misclassified intent types). Future research could refine this model using larger and more diverse data sets.

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Dataset Information

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study.

Publications

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study.

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