Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.

We study the question of how to represent or summarize raw laboratory data taken from an electronic health record (EHR) using parametric model selection to reduce or cope with biases induced through clinical care. It has been previously demonstrated that the health care process (Hripcsak and Albers, 2012, 2013), as defined by measurement context (Hripcsak and Albers, 2013; Albers et al., 2012) and measurement patterns (Albers and Hripcsak, 2010, 2012), can influence how EHR data are distributed statistically (Kohane and Weber, 2013; Pivovarov et al., 2014). We construct an algorithm, PopKLD, which is based on information criterion model selection (Burnham and Anderson, 2002; Claeskens and Hjort, 2008), is intended to reduce and cope with health care process biases and to produce an intuitively understandable continuous summary. The PopKLD algorithm can be automated and is designed to be applicable in high-throughput settings; for example, the output of the PopKLD algorithm can be used as input for phenotyping algorithms. Moreover, we develop the PopKLD-CAT algorithm that transforms the continuous PopKLD summary into a categorical summary useful for applications that require categorical data such as topic modeling. We evaluate our methodology in two ways. First, we apply the method to laboratory data collected in two different health care contexts, primary versus intensive care. We show that the PopKLD preserves known physiologic features in the data that are lost when summarizing the data using more common laboratory data summaries such as mean and standard deviation. Second, for three disease-laboratory measurement pairs, we perform a phenotyping task: we use the PopKLD and PopKLD-CAT algorithms to define high and low values of the laboratory variable that are used for defining a disease state. We then compare the relationship between the PopKLD-CAT summary disease predictions and the same predictions using empirically estimated mean and standard deviation to a gold standard generated by clinical review of patient records. We find that the PopKLD laboratory data summary is substantially better at predicting disease state. The PopKLD or PopKLD-CAT algorithms are not meant to be used as phenotyping algorithms, but we use the phenotyping task to show what information can be gained when using a more informative laboratory data summary. In the process of evaluation our method we show that the different clinical contexts and laboratory measurements necessitate different statistical summaries. Similarly, leveraging the principle of maximum entropy we argue that while some laboratory data only have sufficient information to estimate a mean and standard deviation, other laboratory data captured in an EHR contain substantially more information than can be captured in higher-parameter models.

MAO inhibitory activity of bromo-2-phenylbenzofurans: synthesis, in vitro study, and docking calculations.

Monoamine oxidase (MAO) is an enzyme responsible for metabolism of monoamine neurotransmitters which play an important role in brain development and function. This enzyme exists in two isoforms, and it has been demonstrated that MAO-B activity, but not MAO-A activity, increases with aging. MAO inhibitors show clinical value because besides the monoamine level regulation they reduce the formation of by-products of the MAO catalytic cycle, which are toxic to the brain. A series of 2-phenylbenzofuran derivatives was designed, synthesized and evaluated against hMAO-A and hMAO-B enzymes. A bromine substituent was introduced in the 2-phenyl ring, whereas position 5 or 7 of the benzofuran moiety was substituted with a methyl group. Most of the tested compounds inhibited preferentially MAO-B in a reversible manner, with IC50 values in the low micro or nanomolar range. The 2-(2'-bromophenyl)-5-methylbenzofuran (5) was the most active compound identified (IC50 = 0.20 µM). In addition, none of the studied compounds showed cytotoxic activity against the human neuroblastoma cell line SH-SY5Y. Molecular docking simulations were used to explain the observed hMAO-B structure-activity relationship for this type of compounds.

Systems pharmacology augments drug safety surveillance.

Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology-the integration of systems biology and chemical genomics-can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

Similarity-based modeling applied to signal detection in pharmacovigilance.

One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.

A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records.

OBJECTIVE: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example. METHODS: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values. RESULTS: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. CONCLUSIONS: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generalizability of clinical trials and for defining population-representative clinical trial eligibility criteria.

Medication-wide association studies.

Undiscovered side effects of drugs can have a profound effect on the health of the nation, and electronic health-care databases offer opportunities to speed up the discovery of these side effects. We applied a "medication-wide association study" approach that combined multivariate analysis with exploratory visualization to study four health outcomes of interest in an administrative claims database of 46 million patients and a clinical database of 11 million patients. The technique had good predictive value, but there was no threshold high enough to eliminate false-positive findings. The visualization not only highlighted the class effects that strengthened the review of specific products but also underscored the challenges in confounding. These findings suggest that observational databases are useful for identifying potential associations that warrant further consideration but are unlikely to provide definitive evidence of causal effects.

Improving access to longitudinal patient health information within an emergency department.

We designed and implemented an electronic patient tracking system with improved user authentication and patient selection. We then measured access to clinical information from previous clinical encounters before and after implementation of the system. Clinicians accessed longitudinal information for 16% of patient encounters before, and 40% of patient encounters after the intervention, indicating such a system can improve clinician access to information. We also attempted to evaluate the impact of providing this access on inpatient admissions from the emergency department, by comparing the odds of inpatient admission from an emergency department before and after the improved access was made available. Patients were 24% less likely to be admitted after the implementation of improved access. However, there were many potential confounders, based on the inherent pre-post design of the evaluation. Our experience has strong implications for current health information exchange initiatives.

Biclustering of adverse drug events in the FDA's spontaneous reporting system.

In this article, we present a new pharmacovigilance data mining technique based on the biclustering paradigm, which is designed to identify drug groups that share a common set of adverse events (AEs) in the spontaneous reporting system (SRS) of the US Food and Drug Administration (FDA). A taxonomy of biclusters is developed, revealing that a significant number of bona fide adverse drug event (ADE) biclusters have been identified. Statistical tests indicate that it is extremely unlikely that the bicluster structures thus discovered, as well as their content, could have arisen by mere chance. Some of the biclusters classified as indeterminate provide support for previously unrecognized and potentially novel ADEs. In addition, we demonstrate the potential importance of the proposed methodology in several important aspects of pharmacovigilance such as providing insight into the etiology of ADEs, facilitating the identification of novel ADEs, suggesting methods and a rationale for aggregating terminologies, highlighting areas of focus, and providing an exploratory tool for data mining.

Evaluating the UMLS as a source of lexical knowledge for medical language processing.

Medical language processing (MLP) systems rely on specialized lexicons in order to recognize, classify, and normalize medical terminology, and the performance of an MLP system is dependent on the coverage and quality of such lexicons. However, the acquisition of lexical knowledge is expensive and time-consuming. The UMLS is a comprehensive resource that can be used to acquire lexical knowledge needed for medical language processing. This paper describes methods that use these resources to automatically create lexical entries and generate two lexicons. The first lexicon was created primarily using the UMLS, whereas the second was created by supplementing the lexicon of an existing MLP system called MedLEE with entries based on the UMLS. We subsequently carried out a study, which is the primary focus of this paper, using MedLEE with each of the two lexicons and also the current MedLEE lexicon to measure performance. Overall accuracy, sensitivity, and specificity using the lexicon primarily based on the UMLS were.86,.60, and.96 respectively. Those measures using the MedLEE lexicon alone were.93,.81, and.93, which was significantly better except for specificity; performance using the supplemental lexicon was exactly the same as performance using solely the MedLEE lexicon.

A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries.

At our institution, a Natural Language Processing (NLP) tool called MedLEE is used on a daily basis to parse medical texts including complete discharge summaries. MedLEE transforms written text into a generic structured format, which preserves the richness of the underlying natural language expressions by the use of concept modifiers (like change, certainty, degree and status). As a tradeoff, extraction of application-specific medical information is difficult without a clear understanding of how these modifiers combine. We report on a knowledge model for MedLEE modifiers that is helpful for a high level interpretation of NLP data and is used for the generation of two distinct views on NLP-parsed discharge summaries: A physician view offering a condensed overview of the severity of patient problems and a data mining view featuring binary problem states useful for machine learning.

Making grandma's data secure: a security architecture for home telemedicine.

Home telemedicine presents special challenges for data security and privacy. Experience in the Informatics for Diabetes Education And Telemedicine (IDEATel) project has demonstrated that data security is not a one-size-fits-all problem. The IDEATel users include elderly patients in their homes, nurse case managers, physicians, and researchers. The project supports multiple computer systems that require a variety of user interactions, including: data entry, data review, patient education, videoconferencing, and electronic monitoring. To meet these various needs, a number of different of security solutions were utilized, including: UserID/Password, PKI certificates, time-based tokens, IP filtering, VPNs, symmetric and asymmetric encryption schemes, firewalls and dedicated connections. These were combined in different ways to meet the needs of each user groups.

Generic data modeling for home telemonitoring of chronically ill patients.

Management of many types of chronic diseases such as diabetes and asthma relies heavily on patients' self-monitoring of their disease conditions. In recent years, internet-based home telemonitoring systems that allow transmission of patient data to a central database and offer immediate access to the data by the care providers have become available. However, these systems often work with only one or a few types of medical devices and thus are limited in the types of diseases they can monitor. For example, a system designed to collect spirometry data from asthmatic patients cannot be easily adapted to collect blood glucose data from diabetic patients. This is because different medical devices produce different types of data and the existing telemonitoring systems are generally built around a proprietary data schema specific for the device used. In this paper, we describe a generic data schema for a telemonitoring system that is applicable to different types of medical devices and different diseases, and show an implementation of the schema in a relational database suitable for a variety of telemonitoring activities.

Considering clustering: a methodological review of clinical decision support system studies.

Computer-based clinical decision support systems (CDSSs) are often implemented at a cluster level, but standard statistical methods for sample estimation and analysis may not be appropriate for such studies. This review aims to determine whether the design and analysis methods of cluster-based studies were adequately addressed in reports of CDSS studies. We retrieved 61 reports of the CDSS controlled trials and identified 24 studies meeting our inclusion criteria. Of these, none included sample size calculations that allowed for clustering, while 14 (58%) took account of clustering in the analysis. Although there is increasing recognition of the methodological issues associated with cluster design in health care, many medical informaticians are still not aware of these issues. Investigators should publish estimates of the intracluster correlation coefficients and variance components in their reports to guide the planning of the future studies.

Medical text representations for inductive learning.

Inductive learning algorithms have been proposed as methods for classifying medical text reports. Many of these proposed techniques differ in the way the text is represented for use by the learning algorithms. Slight differences can occur between representations that may be chosen arbitrarily, but such differences can significantly affect classification algorithm performance. We examined 8 different data representation techniques used for medical text, and evaluated their use with standard machine learning algorithms. We measured the loss of classification-relevant information due to each representation. Representations that captured status information explicitly resulted in significantly better performance. Algorithm performance was dependent on subtle differences in data representation.

Coding neuroradiology reports for the Northern Manhattan Stroke Study: a comparison of natural language processing and manual review.

Automated systems using natural language processing may greatly speed chart review tasks for clinical research, but their accuracy in this setting is unknown. The objective of this study was to compare the accuracy of automated and manual coding in the data acquisition tasks of an ongoing clinical research study, the Northern Manhattan Stroke Study(NOMASS). We identified 471 neuroradiology reports of brain images used in the NOMASS study. Using both automated and manual coding, we completed a standardized NOMASS imaging form with the information contained in these reports. We then generated ROC curves for both manual and automated coding by comparing our results to the original NOMASS data, where study in investigators directly coded their interpretations of brain images. The areas under the ROC curves for both manual and automated coding were the main outcome measure. The overall predictive value of the automated system (ROC area 0.85, 95% CI 0.84-0.87) was not statistically different from the predictive value of the manual coding (ROC area 0.87, 95% CI 0.83-0.91). Measured in terms of accuracy, the automated system performed slightly worse than manual coding. The overall accuracy of the automated system was 84% (CI 83-85%). The overall accuracy of manual coding was 86% (CI 84-88%). The difference in accuracy between the two methods was small but statistically significant (P = 0.026). Errors in manual coding appeared to be due to differences between neurologists' and nueroradiologists' interpretation, different use of detailed anatomic terms, and lack of clinical information. Automated systems can use natural language processing to rapidly perform complex data acquisition tasks. Although there is a small decrease in the accuracy of the data as compared to traditional methods, automated systems may greatly expand the power of chart review in clinical research design and implementation.

Internet-based home asthma telemonitoring: can patients handle the technology?

STUDY OBJECTIVE: To evaluate the validity of spirometry self-testing during home telemonitoring and to assess the acceptance of an Internet-based home asthma telemonitoring system by asthma patients. DESIGN: We studied an Internet-based telemonitoring system that collected spirometry data and symptom reports from asthma patients' homes for review by physicians in the medical center's clinical information system. After a 40-min training session, patients completed an electronic diary and performed spirometry testing twice daily on their own from their homes for 3 weeks. A medical professional visited each patient by the end of the third week of monitoring, 10 to 40 min after the patient had performed self-testing, and asked the patient to perform the spirometry test again under his supervision. We evaluated the validity of self-testing and surveyed the patients attitude toward the technology using a standardized questionnaire. SETTING: Telemonitoring was conducted in patients' homes in a low-income inner city area. PATIENTS: Thirty-one consecutive asthma patients without regard to computer experience. MEASUREMENT AND RESULTS: Thirty-one asthma patients completed 3 weeks of monitoring. A paired t test showed no difference between unsupervised and supervised home spirometry self-testing. The variability of FVC (4.1%), FEV(1) (3. 7%), peak expiratory flow (7.9%), and other spirometric indexes in our study was similar to the within-subject variability reported by other researchers. Despite the fact that the majority of the patients (71%) had no computer experience, they indicated that the self-testing was "not complicated at all" or only "slightly complicated." The majority of patients (87.1%) were strongly interested in using home asthma telemonitoring in the future. CONCLUSIONS: Spirometry self-testing by asthma patients during telemonitoring is valid and comparable to those tests collected under the supervision of a trained medical professional. Internet-based home asthma telemonitoring can be successfully implemented in a group of patients with no computer background.

Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries.

Obtaining encoded variables is often a key obstacle to automating clinical guidelines. Frequently the pertinent information occurs as text in patient reports, but text is inadequate for the task. This paper describes a retrospective study that automates determination of severity classes for patients with community-acquired pneumonia (i.e. classifies patients into risk classes 1-5), a common and costly clinical problem. Most of the variables for the automated application were obtained by writing queries based on output generated by MedLEE1, a natural language processor that encodes clinical information in text. Comorbidities, vital signs, and symptoms from discharge summaries as well as information from chest x-ray reports were used. The results were very good because when compared with a reference standard obtained manually by an independent expert, the automated application demonstrated an accuracy, sensitivity, and specificity of 93%, 92%, and 93% respectively for processing discharge summaries, and 96%, 87%, and 98% respectively for chest x-rays. The accuracy for vital sign values was 85%, and the accuracy for determining the exact risk class was 80%. The remaining 20% that did not match exactly differed by only one class.

Classification algorithms applied to narrative reports.

Narrative text reports represent a significant source of clinical data. However, the information stored in these reports is inaccessible to many automated decision support systems. Data mining techniques can assist in extracting information from narrative data. Multiple classification methods, such as rule generation, decision trees, Bayesian classifiers, and information retrieval were used to classify a set of 200 chest X-ray reports according to 6 clinical conditions indicated. A general-purpose natural language processor was used to convert the narrative text into a coded form that could be used by the classification algorithms. Significant differences in performance were found between algorithms. The best performing algorithm applied to the processor output was significantly better than information retrieval applied to raw text. Predictor variables from the coded processor output were limited to avoid overfitting. Methods that limited by domain knowledge performed significantly better than those that limited by conditional probabilities of the variables in the training set. Algorithms were also shown to be dependent on training set size.

WebCIS: large scale deployment of a Web-based clinical information system.

WebCIS is a Web-based clinical information system. It sits atop the existing Columbia University clinical information system architecture, which includes a clinical repository, the Medical Entities Dictionary, an HL7 interface engine, and an Arden Syntax based clinical event monitor. WebCIS security features include authentication with secure tokens, authorization maintained in an LDAP server, SSL encryption, permanent audit logs, and application time outs. WebCIS is currently used by 810 physicians at the Columbia-Presbyterian center of New York Presbyterian Healthcare to review and enter data into the electronic medical record. Current deployment challenges include maintaining adequate database performance despite complex queries, replacing large numbers of computers that cannot run modern Web browsers, and training users that have never logged onto the Web. Although the raised expectations and higher goals have increased deployment costs, the end result is a far more functional, far more available system.