Biomedical subjects

G Hripcsak

Publications and source records attributed to G Hripcsak.

At least 19 recordsLinked to original sources

PubMed2001

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.

Natural Language Processing↗

PubMed2001

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.

Artificial Intelligence↗

PubMed2001

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.

Aged↗

PubMed2000

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.

Brain↗

PubMed2000

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.

Adult↗

PubMed2000

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.

Asthma↗

PubMed2000

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.

Cluster Analysis↗

PubMed2000

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.

Algorithms↗

PubMed1999

A health information network for managing innercity tuberculosis: bridging clinical care, public health, and home care.

The purpose of this study was to use a health information network and innovative technology to coordinate tuberculosis care. An innercity medical center, a local health department, and a home care nurse service in northern Manhattan were used. The organizations were linked with computer networks. An automated decision support system with a natural language processor was used to detect tuberculosis cases and report them to the health department, and to select patients for respiratory isolation. Educational materials were placed on the World Wide Web and a Web-based kiosk. Home care nurses were outfitted with wireless pen-based computers, and data were relayed to the medical center. Automated tuberculosis case reporting resulted in time savings but not improved accuracy. Automated rules resulted in significant improvements in respiratory isolation. Kiosk educational materials were well-used. Wireless computing led to better access to information for both nurses and physicians, but not to reduction of workload. The key success element was recognition of critical priorities. It is concluded that innovative technology can facilitate the coordination of clinical care, public health, and home care.

Academic Medical Centers↗

PubMed1999

Natural language processing and its future in medicine.

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine.

Forecasting↗

PubMed1999

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.

Community-Acquired Infections↗

PubMed1999

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.

Algorithms↗

PubMed1999

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.

Clinical Medicine↗

PubMed1998

Developing online support for clinical information system developers: the FAQ approach.

OBJECTIVE: We investigate a knowledge-based help system for developers of an integrated clinical information system (CIS). The first objective in the study was to determine the system's ability to answer users' questions effectively. User performance and behavior were studied. The second objective was to evaluate the effect of using questions and answers to augment or replace traditional program documentation. DESIGN: A comparative study of user and system effectiveness using a collection of 47 veritable questions regarding the CIS, solicited from various CIS developers, is conducted. Most questions were concerning the clinical data model and acquiring the data. MEASUREMENTS: Answers using current documentation known by users were compared to answers found using the help system. Answers existing within traditional documentation were compared to answers existing within question-answer exchanges (Q-A's). RESULTS: The support system augmented 39% of users' answers to test questions. Though the Q-A's were less than 5% of the total documentation collected, these files contained answers to nearly 50% of the questions in the test group. The rest of the documentation contained about 75% of the answers. CONCLUSIONS: A knowledge-based help system built by collecting questions and answers can be a viable alternative to large documentation files, providing the questions and answers can be collected effectively.

Artificial Intelligence↗

PubMed1998

Respiratory isolation of tuberculosis patients using clinical guidelines and an automated clinical decision support system.

OBJECTIVE: To evaluate a clinical guideline and an automated computer protocol for detection and respiratory isolation of tuberculosis (TB) patients. DESIGN: An automated computer protocol was tested on a retrospective cohort of adult culture-positive TB patients admitted from 1992 to 1993 to Columbia-Presbyterian Medical Center and evaluated prospectively from July 1995 until July 1996. SETTING: A large teaching hospital in New York City. PATIENTS: 171 adult patients admitted from 1992 to 1993 and 43 patients admitted between July 1995 and July 1996. INTERVENTIONS: The 1990 Centers for Disease Control and Prevention guidelines for preventing transmission of TB were adapted to formulate clinical guidelines to ensure early isolation of TB patients at Columbia-Presbyterian Medical Center. RESULTS: Implementation of a clinical respiratory isolation protocol resulted in a significant improvement in TB patient isolation rates, from 45 (51%) of 88 in 1992 to 62 (75%) of 83 in 1993 (P<.001). In testing automated protocols, the theoretical improvement would have identified an additional 27 patients not isolated by clinicians, making the overall isolation rate 134 (78%) of 171. For the prospective evaluation, 30 (70%) of 43 TB patients were isolated by clinicians adhering to the clinical protocol. Four additional patients were identified by the automated TB protocol, making the combined isolation rate 34 (79%) of 43. CONCLUSIONS: A clinical policy to isolate TB patients and suspected human immunodeficiency virus-infected patients with cough, fever, or radiographic abnormalities improved isolation of culture-documented TB patients from 1992 to 1993. Automated computer protocols were successful in identifying additional potentially infectious patients that clinicians failed to place on respiratory isolation. Clinical and automated protocols combined resulted in better isolation rates than a clinical protocol alone.

AIDS-Related Opportunistic Infections↗

PubMed1998

Telematic system for monitoring of asthma severity in patients' homes.

Despite advances in the treatment of asthma the morbidity and mortality of this disease has increased significantly in the past several years. Recent studies have shown that monitoring of asthma severity in the patient home especially combined with patient education can reduce incidence of asthma exacerbation and subsequent hospitalization. The existing methods for home asthma monitoring are limited by four factors; they completely rely on a patient's ability to document and to evaluate test results; there is no easy way for a physician to review data in a timely manner; they use imprecise tools for evaluation of asthma severity and they don't provide clinical decision support tools. The goal of this study is to develop and to evaluate a telematic system for asthma severity monitoring which will minimize patients' efforts in performing self-testing at their homes and allow prompt reciprocal exchange of all relevant information between patients and health care providers. In our setting, patients use portable spirometer and pocket-sized palmtop computer for data exchange. Our system allows daily serial monitoring of asthma severity at patients' homes using Forced Vital Capacity test and symptom diary. The results of the tests become available for physicians review immediately after completion of self-testing procedures via Web browser. The results can be transmitted from patients' homes (or any other remote location) to the medical records database via landline or wireless networks in several minutes. Each time the remote server receives patient's results, it invokes the application which tests the validity of data, analyzes parameters trends and dispatches corresponding messages for the patient and, if necessary, for physicians. Such an approach provides constant feedback loop between asthma patient and physician. The system has been tested in 10 healthy volunteers and asthma patients. Patients participated in the study from two to 21 days. The test results showed that the system provides reliable reciprocal exchange of all relevant information between a physician and asthma patient in home settings. Average transmission time from the patient's palmtop to the remote central data repository was about 1 minute for 14.4 Kbps landline modem, 6 minutes for cellular network and 8 minutes for RAM Mobile network. After transmission, the test results were immediately available for review at our web site.

Asthma↗

PubMed1998

Extracting findings from narrative reports: software transferability and sources of physician disagreement.

While natural language processing systems are beginning to see clinical use, it remains unclear whether they can be disseminated effectively through the health care community. MedLEE, a general-purpose natural language processor developed for Columbia-Presbyterian Medical Center, was compared to physicians' ability to detect seven clinical conditions in 200 Brigham and Women's Hospital chest radiograph reports. Using the system on the new institution's reports resulted in a small but measurable drop in performance (it was distinguishable from physicians at p = 0.011). By making adjustments to the interpretation of the processor's coded output (without changing the processor itself), local behavior was better accommodated, and performance improved so that it was indistinguishable from the physicians. Pairs of physicians disagreed on at least one condition for 22% of reports; the source of disagreement appeared to be interpretation of findings, gauging likelihood and degree of disease, and coding errors.

Diagnosis, Computer-Assisted↗

PubMed1998

Evaluating natural language processors in the clinical domain.

Evaluating natural language processing (NLP) systems in the clinical domain is a difficult task which is important for advancement of the field. A number of NLP systems have been reported that extract information from free-text clinical reports, but not many of the systems have been evaluated. Those that were evaluated noted good performance measures but the results were often weakened by ineffective evaluation methods. In this paper we describe a set of criteria aimed at improving the quality of NLP evaluation studies. We present an overview of NLP evaluations in the clinical domain and also discuss the Message Understanding Conferences (MUC) [1-4]. Although these conferences constitute a series of NLP evaluation studies performed outside of the clinical domain, some of the results are relevant within medicine. In addition, we discuss a number of factors which contribute to the complexity that is inherent in the task of evaluating natural language systems.

Evaluation Studies as Topic↗