Dataset Information

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach.

ABSTRACT: BACKGROUND:Suicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum. OBJECTIVE:This study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events. METHODS:We extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words-based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance. RESULTS:The area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance. CONCLUSIONS:The strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.

SUBMITTER: Obeid JS

PROVIDER: S-EPMC7426805 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach.

Obeid Jihad S JS Dahne Jennifer J Christensen Sean S Howard Samuel S Crawford Tami T Frey Lewis J LJ Stecker Tracy T Bunnell Brian E BE

JMIR medical informatics 20200730 7

<h4>Background</h4>Suicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum.<h4>Objective</h4>This study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identi ...[more]

PMID: 32729840

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Project description:BackgroundPharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.ObjectiveWe aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.MethodsWe employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.ResultsSince extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.ConclusionsDeep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.

Project description:ImportanceThere is a need for representative research on serious adverse outcomes following discharge from psychiatric hospitalization.ObjectiveTo compare rates of premature death, suicide, and nonlethal intentional self-harm after psychiatric discharge with rates in the general population and investigate associations of these outcomes with relevant variables associated with the index psychiatric hospitalization.Design, setting, and participantsThis retrospective cohort study included all residents from Catalonia, Spain (7.6 million population), who had psychiatric hospitalizations between January 1, 2014, and December 31, 2018, and were older than 10 years at the index (first) hospitalization. Follow-up was until December 31, 2019. Statistical analysis was performed from December 1, 2022, through April 11, 2024.ExposuresSocioeconomic status, psychiatric diagnoses, duration of index hospitalization, and number of previous psychiatric hospitalizations.Main outcomes and measuresPostdischarge premature death (ie, all-cause death before age 70 years) and suicide (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code range X60-X84), identified using mortality data, and postdischarge nonlethal intentional self-harm, identified using electronic health record and self-harm case register data. Standardized mortality ratios (SMRs) compared rates of premature death and suicide between the cohort and the general population. Fully adjusted, multivariable, cause-specific Cox proportional hazards regression models for the 3 outcomes were fitted.ResultsA total of 49 108 patients discharged from psychiatric hospitalization were included (25 833 males [52.6%]; mean [SD] age at discharge, 44.2 [18.2] years). During follow-up, 2260 patients (4.6%) died prematurely, 437 (0.9%) died by suicide, and 4752 (9.7%) had an episode of nonlethal intentional self-harm. The overall SMR for premature death was 7.5 (95% CI, 7.2-7.9). For suicide, SMR was 32.9 (95% CI, 29.9-36.0) overall and was especially high among females (47.6 [95% CI, 40.2-54.9]). In fully adjusted sex-stratified hazard models, postdischarge premature death was associated with cognitive disorders (adjusted hazard ratio [AHR], 2.89 [95% CI, 2.24-3.74] for females; 2.59 [95% CI, 2.17-3.08] for males) and alcohol-related disorders (AHR, 1.41 [95% CI, 1.18-1.70] for females; 1.22 [95% CI, 1.09-1.37] for males). Postdischarge suicide was associated with postdischarge intentional self-harm (AHR, 2.83 [95% CI, 1.97-4.05] for females; 3.29 [95% CI, 2.47-4.40] for males), with depressive disorders (AHR, 2.13 [95% CI, 1.52-2.97]) and adjustment disorders (AHR, 1.94 [95% CI, 1.32-2.83]) among males, and with bipolar disorder among females (AHR, 1.94 [95% CI, 1.21-3.09]). Postdischarge intentional self-harm was associated with index admissions for intentional self-harm (AHR, 1.95 [95% CI, 1.73-2.21] for females; 2.62 [95% CI, 2.20-3.13] for males) as well as for adjustment disorders (AHR, 1.48 [95% CI, 1.33-1.65] for females; 1.99 [95% CI, 1.74-2.27] for males), anxiety disorders (AHR, 1.24 [95% CI, 1.10-1.39] for females; 1.36 [95% CI, 1.18-1.58] for males), depressive disorders (AHR, 1.54 [95% CI, 1.40-1.69] for females; 1.80 [95% CI, 1.58-2.04] for males), and personality disorders (AHR, 1.59 [95% CI, 1.46-1.73] for females; 1.43 [95% CI, 1.28-1.60] for males).Conclusions and relevanceIn this cohort study of patients discharged from psychiatric hospitalization, risk for premature death and suicide was significantly higher compared with the general population, suggesting individuals discharged from psychiatric inpatient care are a vulnerable population for premature death and suicidal behavior.

Project description:RationalePatients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.ObjectivesWe aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.MethodsThis observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).ResultsEleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.ConclusionsA machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Dataset Information

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach.

Publications

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach.

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