Dmitriy Dligach

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium

RESEARCH OBJECTIVE To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

A common type system for clinical natural language processing

BackgroundOne challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings.ResultsWe describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later.ConclusionsWe have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.

Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record

OBJECTIVES To improve the accuracy of mining structured and unstructured components of the electronic medical record (EMR) by adding temporal features to automatically identify patients with rheumatoid arthritis (RA) with methotrexate-induced liver transaminase abnormalities. MATERIALS AND METHODS Codified information and a string-matching algorithm were applied to a RA cohort of 5903 patients from Partners HealthCare to select 1130 patients with potential liver toxicity. Supervised machine learning was applied as our key method. For features, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) was used to extract standard vocabulary from relevant sections of the unstructured clinical narrative. Temporal features were further extracted to assess the temporal relevance of event mentions with regard to the date of transaminase abnormality. All features were encapsulated in a 3-month-long episode for classification. Results were summarized at patient level in a training set (N=480 patients) and evaluated against a test set (N=120 patients). RESULTS The system achieved positive predictive value (PPV) 0.756, sensitivity 0.919, F1 score 0.829 on the test set, which was significantly better than the best baseline system (PPV 0.590, sensitivity 0.703, F1 score 0.642). Our innovations, which included framing the phenotype problem as an episode-level classification task, and adding temporal information, all proved highly effective. CONCLUSIONS Automated methotrexate-induced liver toxicity phenotype discovery for patients with RA based on structured and unstructured information in the EMR shows accurate results. Our work demonstrates that adding temporal features significantly improved classification results.

Large-scale identification of patients with cerebral aneurysms using natural language processing

Objective: To use natural language processing (NLP) in conjunction with the electronic medical record (EMR) to accurately identify patients with cerebral aneurysms and their matched controls. Methods: ICD-9 and Current Procedural Terminology codes were used to obtain an initial data mart of potential aneurysm patients from the EMR. NLP was then used to train a classification algorithm with .632 bootstrap cross-validation used for correction of overfitting bias. The classification rule was then applied to the full data mart. Additional validation was performed on 300 patients classified as having aneurysms. Controls were obtained by matching age, sex, race, and healthcare use. Results: We identified 55,675 patients of 4.2 million patients with ICD-9 and Current Procedural Terminology codes consistent with cerebral aneurysms. Of those, 16,823 patients had the term aneurysm occur near relevant anatomic terms. After training, a final algorithm consisting of 8 coded and 14 NLP variables was selected, yielding an overall area under the receiver-operating characteristic curve of 0.95. After the final algorithm was applied, 5,589 patients were classified as having aneurysms, and 54,952 controls were matched to those patients. The positive predictive value based on a validation cohort of 300 patients was 0.86. Conclusions: We harnessed the power of the EMR by applying NLP to obtain a large cohort of patients with intracranial aneurysms and their matched controls. Such algorithms can be generalized to other diseases for epidemiologic and genetic studies.

Multilayered temporal modeling for the clinical domain

OBJECTIVE To develop an open-source temporal relation discovery system for the clinical domain. The system is capable of automatically inferring temporal relations between events and time expressions using a multilayered modeling strategy. It can operate at different levels of granularity--from rough temporality expressed as event relations to the document creation time (DCT) to temporal containment to fine-grained classic Allen-style relations. MATERIALS AND METHODS We evaluated our systems on 2 clinical corpora. One is a subset of the Temporal Histories of Your Medical Events (THYME) corpus, which was used in SemEval 2015 Task 6: Clinical TempEval. The other is the 2012 Informatics for Integrating Biology and the Bedside (i2b2) challenge corpus. We designed multiple supervised machine learning models to compute the DCT relation and within-sentence temporal relations. For the i2b2 data, we also developed models and rule-based methods to recognize cross-sentence temporal relations. We used the official evaluation scripts of both challenges to make our results comparable with results of other participating systems. In addition, we conducted a feature ablation study to find out the contribution of various features to the systems performance. RESULTS Our system achieved state-of-the-art performance on the Clinical TempEval corpus and was on par with the best systems on the i2b2 2012 corpus. Particularly, on the Clinical TempEval corpus, our system established a new F1 score benchmark, statistically significant as compared to the baseline and the best participating system. CONCLUSION Presented here is the first open-source clinical temporal relation discovery system. It was built using a multilayered temporal modeling strategy and achieved top performance in 2 major shared tasks.

Association of intracranial aneurysm rupture with smoking duration, intensity, and cessation

Objective: Although smoking is a known risk factor for intracranial aneurysm (IA) rupture, the exact relationship between IA rupture and smoking intensity and duration, as well as duration of smoking cessation, remains unknown. Methods: In this case-control study, we analyzed 4,701 patients with 6,411 IAs diagnosed at the Brigham and Womens Hospital and Massachusetts General Hospital between 1990 and 2016. We divided individuals into patients with ruptured aneurysms and controls with unruptured aneurysms. We performed univariable and multivariable logistic regression analyses to determine the association between smoking status and ruptured IAs at presentation. In a subgroup analysis among former and current smokers, we assessed the association between ruptured aneurysms and number of packs per day, duration of smoking, and duration since smoking cessation. Results: In multivariable analysis, current (odds ratio [OR] 2.21, 95% confidence interval [CI] 1.89–2.59) and former smoking status (OR 1.56, 95% CI 1.31–1.86) were associated with rupture status at presentation compared with never smokers. In a subgroup analysis among current and former smokers, years smoked (OR 1.02, 95% CI 1.01–1.03) and packs per day (OR 1.46, 95% CI 1.25–1.70) were significantly associated with ruptured aneurysms at presentation, whereas duration since cessation among former smokers was not significant (OR 1.00, 95% CI 0.99–1.02). Conclusions: Current cigarette smoking, smoking intensity, and smoking duration are significantly associated with ruptured IAs at presentation. However, the significantly increased risk persists after smoking cessation, and smoking cessation does not confer a reduced risk of aneurysmal subarachnoid hemorrhage beyond that of reducing the cumulative dose.

Extracting Time Expressions from Clinical Text

Temporal information extraction is important to understanding text in clinical documents. Temporal expression extraction provides explicit grounding of events in a narrative. In this work we provide a direct comparison of various ways of extracting temporal expressions, using similar features as much as possible to explore the advantages of the methods themselves. We evaluate these systems on both the THYME (Temporal History of Your Medical Events) and i2b2 Challenge corpora. Our main findings are that simple sequence taggers outperform conditional random fields on the new data, and higher-level syntactic features do not seem to improve performance.

Alcohol Consumption and Aneurysmal Subarachnoid Hemorrhage

Alcohol consumption may be a modifiable risk factor for rupture of intracranial aneurysms. Our aim is to evaluate the association between ruptured aneurysms and alcohol consumption, intensity, and cessation. The medical records of 4701 patients with 6411 radiographically confirmed intracranial aneurysms diagnosed at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016 were reviewed. Individuals were divided into cases with ruptured aneurysms and controls with unruptured aneurysms. Univariable and multivariable logistic regression analyses were performed to determine the association between alcohol consumption and rupture of intracranial aneurysms. In multivariable analysis, current alcohol use (OR 1.36, 95% CI 1.17–1.58) was associated with rupture status compared with never drinkers, whereas former alcohol use was not significant (OR 1.23, 95% CI 0.92–1.63). In addition, the number of alcoholic beverages per day among current alcohol users (OR 1.13, 95% CI 1.04–1.23) was significantly associated with rupture status, whereas alcohol use intensity was not significant among former users (OR 1.02, 95% CI 0.94–1.11). Current alcohol use and intensity are significantly associated with intracranial aneurysm rupture. However, this increased risk does not persist in former alcohol users, emphasizing the potential importance of alcohol cessation in patients harboring unruptured aneurysms.

Heroin Use Is Associated with Ruptured Saccular Aneurysms

While cocaine use is thought to be associated with aneurysmal rupture, it is not known whether heroin use increases the risk of rupture in patients with non-mycotic saccular aneurysms. Our goal was to investigate the association between heroin and cocaine use and the rupture of saccular non-mycotic aneurysms. The medical records of 4701 patients with 6411 intracranial aneurysms, including 1201 prospective patients, diagnosed at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016 were reviewed and analyzed. Patients were separated into ruptured and non-ruptured groups. Univariable and multivariable logistic regression analyses were performed to determine the association between heroin, cocaine, and methadone use and the presence of ruptured intracranial aneurysms. In multivariable analysis, current heroin use was significantly associated with rupture status (OR 3.23, 95% CI 1.33–7.83) whereas former heroin use (with and without methadone replacement therapy), and current and former cocaine use were not significantly associated with intracranial aneurysm rupture. In the present study, heroin rather than cocaine use is significantly associated with intracranial aneurysm rupture in patients with non-mycotic saccular cerebral aneurysms, emphasizing the possible role of heroin in the pathophysiology of aneurysm rupture and the importance of heroin cessation in patients harboring unruptured intracranial aneurysms.

Antihyperglycemic Agents Are Inversely Associated With Intracranial Aneurysm Rupture

Background and Purpose— Previous studies have suggested a protective effect of diabetes mellitus on aneurysmal subarachnoid hemorrhage risk. However, reports are inconsistent, and objective measures of hyperglycemia in these studies are lacking. Our aim was to investigate the association between aneurysmal subarachnoid hemorrhage and antihyperglycemic agent use and glycated hemoglobin levels. Methods— The medical records of 4701 patients with 6411 intracranial aneurysms, including 1201 prospective patients, diagnosed at the Massachusetts General Hospital and Brigham and Women’s Hospital between 1990 and 2016 were reviewed and analyzed. Patients were separated into ruptured and nonruptured groups. Univariate and multivariate logistic regression analyses were performed to determine the association between aneurysmal subarachnoid hemorrhage and antihyperglycemic agents and glycated hemoglobin levels. Propensity score weighting was used to account for selection bias. Results— In both unweighted and weighted multivariate analysis, antihyperglycemic agent use was inversely and significantly associated with ruptured aneurysms (unweighted odds ratio, 0.58; 95% confidence interval, 0.39–0.87; weighted odds ratio, 0.57; 95% confidence interval, 0.34–0.96). In contrast, glycated hemoglobin levels were not significantly associated with rupture status. Conclusions— Antihyperglycemic agent use rather than hyperglycemia is associated with decreased risk of aneurysmal subarachnoid hemorrhage, suggesting a possible protective effect of glucose-lowering agents in the pathogenesis of aneurysm rupture.