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Biomedical subjects

George Hripcsak

Publications and source records attributed to George Hripcsak.

At least 19 recordsLinked to original sources
PubMed2026

Inherited Susceptibility to Urinary Tract Infections from Kidney Papilla to Bladder.

Urinary tract infections (UTIs) are traditionally viewed as environmentally driven, yet their inherited susceptibility remains largely unexplored. We conducted a cross-biobank genome-wide association study of recurrent UTIs in 1,860,836 individuals (213,869 cases and 1,646,967 controls). We identified 36 genetic susceptibility loci and performed tissue-based multi-omic mapping to prioritize candidate causal genes. UTI risk alleles preferentially modulated epithelial gene expression in kidney and bladder, converging on urinary epithelia structure and function. PSCA, encoding a secreted epithelial surface protein, emerged as the strongest candidate under genetic control; the gene product is constitutively secreted into the urine from kidney papilla and bladder epithelia, binds uropathogenic E. coli, and inhibits bacterial growth in vitro. Our findings define the polygenic architecture of UTIs and highlight the critical role of uroepithelial surface defenses, providing a new framework for host-directed, non-antibiotic interventions.

Journal Article↗
PubMed2025

Genome-wide association meta-regression identifies stem cell lineage orchestration as a key driver of acne risk.

Over 85% of the population experience acne at some point in their lives, with its severity spanning a quantitative spectrum, from mild, transient outbreaks to more persistent, severe forms of the condition. Moderate to severe disease poses a substantial global burden arising from both the physical and psychological impacts of this highly visible condition. The analytical approach taken in this study aimed to address the impact of variation in the dichotomisation of acne case control status, driven by ascertainment and study design, on effect size estimates across independent genetic association studies of acne. Through a fixed intercept meta-regression framework, we combined evidence genome-wide for association with acne across studies in which case-control status had been ascertained in different settings, allowing for different severity threshold definitions. Across a combined sample of 73,997 cases and 1,103,940 controls of European, South Asian and African American ancestry we identify genetic variation at 165 genomic loci that influence acne risk. There is evidence for both shared and ancestry specific components to the genetic susceptibility to acne and for sex differences in the magnitude of effect of risk alleles at three loci. We observe that common genetic variation explains 13.4% of acne heritability on the liability scale. Consistent with the hypothesis that genetic risk primarily operates at the level of individual pilosebaceous units, a polygenic score derived from this case-control study of acne susceptibility is associated with both self-reported and clinically assessed acne severity in adolescence, further strengthening the link between genetic risk and disease severity. Prioritisation of causal genes at the identified acne risk loci, provides genetic validation of the targets of established and emerging acne therapies, including retinoid treatments. The identified acne risk loci are enriched for genes encoding downstream effectors of RXRA signalling, including SOX9 and components of the WNT and p53 pathways. Illustrating that the control of stem cell lineage plasticity and cellular fate are important mechanisms through which genetic variation influences acne susceptibility within the pilosebaceous unit.

Journal Article↗
PubMed2007

Emergency department access to a longitudinal medical record.

Our goal is to assess how clinical information from previous visits is used in the emergency department. We used detailed user audit logs to measure access to different data types. We found that clinician-authored notes and laboratory and radiology data were used most often (common data types were used up to 5% to 20% of the time). Data were accessed less than half the time (up to 20% to 50%) even when the user was alerted to the presence of data. Our access rate indicates that health information exchange projects should be conservative in estimating how often shared data will be used and the wide breadth of data accessed indicates that although a clinical summary is likely to be useful, an ideal solution will supply a broad variety of data.

Emergency Service, Hospital↗
PubMed2006

A statistical methodology for analyzing co-occurrence data from a large sample.

Determining important associations among items in a large database is challenging due to multiple simultaneous hypotheses and the ability to select weak associations that are statistically but not clinically significant. The simple application of the chi2 test among all possible pairs of items results in mostly inappropriate associations surpassing the traditional (alpha=.05, chi2=3.94) threshold. One can choose a stricter threshold to find stronger associations, but the choice may be arbitrary. We combined the volume test of Diaconis and Efron with a p-value plot to select a more rigorous and less arbitrary threshold. The volume test adjusts the p-value of the chi2-statistic. A plot of adjusted p-values (1 - p versus N(p)), where N(p) is the number of test statistics with a p-value greater than p, should be linear if there are no true associations. The point where the plot deviates from a line can be used as a threshold. We used linear regression to select the threshold in a reproducible fashion. In one experiment, we found that the method selected a threshold similar to that previously obtained by manually reviewing associations.

Calibration↗
PubMed2006

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia--in which the ICD9-CM coding was found to be very poor--the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).

Adult↗
PubMed2006

Inter-patient distance metrics using SNOMED CT defining relationships.

BACKGROUND: Patient-based similarity metrics are important case-based reasoning tools which may assist with research and patient care applications. Ontology and information content principles may be potentially helpful tools for similarity metric development. METHODS: Patient cases from 1989 through 2003 from the Columbia University Medical Center data repository were converted to SNOMED CT concepts. Five metrics were implemented: (1) percent disagreement with data as an unstructured "bag of findings," (2) average links between concepts, (3) links weighted by information content with descendants, (4) links weighted by information content with term prevalence, and (5) path distance using descendants weighted by information content with descendants. Three physicians served as gold standard for 30 cases. RESULTS: Expert inter-rater reliability was 0.91, with rank correlations between 0.61 and 0.81, representing upper-bound performance. Expert performance compared to metrics resulted in correlations of 0.27, 0.29, 0.30, 0.30, and 0.30, respectively. Using SNOMED axis Clinical Findings alone increased correlation to 0.37. CONCLUSION: Ontology principles and information content provide useful information for similarity metrics but currently fall short of expert performance.

Algorithms↗
PubMed2006

Terminology model discovery using natural language processing and visualization techniques.

Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.

Automation↗
PubMed2005

A temporal constraint structure for extracting temporal information from clinical narrative.

INTRODUCTION: Time is an essential element in medical data and knowledge which is intrinsically connected with medical reasoning tasks. Many temporal reasoning mechanisms use constraint-based approaches. Our previous research demonstrates that electronic discharge summaries can be modeled as a simple temporal problem (STP). OBJECTIVE: To categorize temporal expressions in clinical narrative text and to propose and evaluate a temporal constraint structure designed to model this temporal information and to support the implementation of higher-level temporal reasoning. METHODS: A corpus of 200 random discharge summaries across 18 years was applied in a grounded approach to construct a representation structure. Then, a subset of 100 discharge summaries was used to tally the frequency of each identified time category and the percentage of temporal expressions modeled by the structure. Fifty random expressions were used to assess inter-coder agreement. RESULTS: Six main categories of temporal expressions were identified. The constructed temporal constraint structure models time over which an event occurs by constraining its starting time and ending time. It includes a set of fields for the endpoint(s) of an event, anchor information, qualitative and metric temporal relations, and vagueness. In 100 discharge summaries, 1961 of 2022 (97%) identified temporal expressions were effectively modeled using the temporal constraint structure. Inter-coder evaluation of 50 expressions yielded exact match in 90%, partial match with trivial differences in 8%, partial match with large differences in 2%, and total mismatch in 0%. CONCLUSION: The proposed temporal constraint structure embodies a sufficient and successful implementation method to encode the diversity of temporal information in discharge summaries. Placing data within the structure provides a foundational representation upon which further reasoning, including the addition of domain knowledge and other post-processing to implement an STP, can be accomplished.

Database Management Systems↗
PubMed2005

A draft framework for measuring progress towards the development of a National Health Information Infrastructure.

BACKGROUND: American public policy makers recently established the goal of providing the majority of Americans with electronic health records by 2014. This will require a National Health Information Infrastructure (NHII) that is far more complete than the one that is currently in its formative stage of development. We describe a conceptual framework to help measure progress toward that goal. DISCUSSION: The NHII comprises a set of clusters, such as Regional Health Information Organizations (RHIOs), which, in turn, are composed of smaller clusters and nodes such as private physician practices, individual hospitals, and large academic medical centers. We assess progress in terms of the availability and use of information and communications technology and the resulting effectiveness of these implementations. These three attributes can be studied in a phased approach because the system must be available before it can be used, and it must be used to have an effect. As the NHII expands, it can become a tool for evaluating itself. SUMMARY: The NHII has the potential to transform health care in America--improving health care quality, reducing health care costs, preventing medical errors, improving administrative efficiencies, reducing paperwork, and increasing access to affordable health care. While the President has set an ambitious goal of assuring that most Americans have electronic health records within the next 10 years, a significant question remains "How will we know if we are making progress toward that goal?" Using the definitions for "nodes" and "clusters" developed in this article along with the resulting measurement framework, we believe that we can begin a discussion that will enable us to define and then begin making the kinds of measurements necessary to answer this important question.

Health Policy↗
PubMed2005

Automated detection of adverse events using natural language processing of discharge summaries.

OBJECTIVE: To determine whether natural language processing (NLP) can effectively detect adverse events defined in the New York Patient Occurrence Reporting and Tracking System (NYPORTS) using discharge summaries. DESIGN: An adverse event detection system for discharge summaries using the NLP system MedLEE was constructed to identify 45 NYPORTS event types. The system was first applied to a random sample of 1,000 manually reviewed charts. The system then processed all inpatient cases with electronic discharge summaries for two years. All system-identified events were reviewed, and performance was compared with traditional reporting. MEASUREMENTS: System sensitivity, specificity, and predictive value, with manual review serving as the gold standard. RESULTS: The system correctly identified 16 of 65 events in 1,000 charts. Of 57,452 total electronic discharge summaries, the system identified 1,590 events in 1,461 cases, and manual review verified 704 events in 652 cases, resulting in an overall sensitivity of 0.28 (95% confidence interval [CI]: 0.17-0.42), specificity of 0.985 (CI: 0.984-0.986), and positive predictive value of 0.45 (CI: 0.42-0.47) for detecting cases with events and an average specificity of 0.9996 (CI: 0.9996-0.9997) per event type. Traditional event reporting detected 322 events during the period (sensitivity 0.09), of which the system identified 110 as well as 594 additional events missed by traditional methods. CONCLUSION: NLP is an effective technique for detecting a broad range of adverse events in text documents and outperformed traditional and previous automated adverse event detection methods.

Academic Medical Centers↗
PubMed2005

Agreement, the f-measure, and reliability in information retrieval.

Information retrieval studies that involve searching the Internet or marking phrases usually lack a well-defined number of negative cases. This prevents the use of traditional interrater reliability metrics like the kappa statistic to assess the quality of expert-generated gold standards. Such studies often quantify system performance as precision, recall, and F-measure, or as agreement. It can be shown that the average F-measure among pairs of experts is numerically identical to the average positive specific agreement among experts and that kappa approaches these measures as the number of negative cases grows large. Positive specific agreement-or the equivalent F-measure-may be an appropriate way to quantify interrater reliability and therefore to assess the reliability of a gold standard in these studies.

Humans↗
PubMed2005

A lexical metaschema for the UMLS semantic network.

OBJECTIVE: A metaschema is a high-level abstraction network of the UMLS's semantic network (SN) obtained from a partition of the SN's collection of semantic types. Every metaschema has nodes, called meta-semantic types, each of which denotes a group of semantic types constituting a subject area of the SN. A new kind of metaschema, called the lexical metaschema, is derived from a lexical partition of the SN. The lexical metaschema is compared to previously derived metaschemas, e.g., the cohesive metaschema. DESIGN: A new lexical partitioning methodology is presented based on identical word-usage among the names of semantic types and the definitions of their respective children. The lexical metaschema is derived from the application of the methodology. We compare the constituent meta-semantic types and their underlying semantic-type groups with the previously derived cohesive metaschema. A similar comparison of the lexical partition and a published partition of the SN is also carried out. RESULTS: The lexical partition of the SN has 21 semantic-type groups, each of which represents a subject area. The lexical metaschema thus has 21 meta-semantic types, 19 meta-child-of hierarchical relationships, and 86 meta-relationships. Our comparison shows that 15 out of the 21 meta-semantic types in the lexical metaschema also appear in the cohesive metaschema, and 80 semantic types are covered by identical meta-semantic types or refinements between the two metaschemas. The comparison between the lexical partition and the semantic partition shows that they have very low similarity. CONCLUSION: The algorithmically derived lexical metaschema serves as an abstraction of the SN and provides views representing different subject areas. It compares favorably with the cohesive metaschema derived via the SN's relationship configuration.

Abstracting and Indexing↗
PubMed2005

An expert study evaluating the UMLS lexical metaschema.

OBJECTIVE: A metaschema is an abstraction network of the UMLS's semantic network (SN) obtained from a connected partition of its collection of semantic types. A lexical metaschema was previously derived based on a lexical partition which partitioned the SN into semantic-type groups using identical word-usage among the names of semantic types and the definitions of their respective children. In this paper, a statistical analysis methodology is presented to evaluate the lexical metaschema based on a study involving a group of established UMLS experts. METHODS: In the study, each expert was asked to identify subject areas of the SN based on his or her understanding of the various semantic types. For this purpose, the expert scans the SN hierarchy top-down, identifying semantic types, which are important and different enough from their parent semantic types, as roots of their groups. From the response of each expert, an "expert metaschema" is constructed. The different experts' metaschemas can vary widely. So, additional metaschemas are obtained from aggregations of the experts' responses. Of special interest is the consensus metaschema which represents an aggregation of a simple majority of the experts' responses. Statistical analysis comparing the lexical metaschema with the experts' metaschemas and the consensus metaschema is presented. RESULTS: The analysis results shows that 17 out of the 21 meta-semantic types in the lexical metaschema also appear in the consensus metaschema (about 81%). There are 107 semantic types (about 79%) covered by identical meta-semantic types and refinements. The results show the high similarity between the two metaschemas. Furthermore, the statistical analysis shows that the lexical metaschema did not grossly underperform compared to the experts. CONCLUSION: Our study shows that the lexical metaschema provides a good approximation for a partition of meaningful subject areas in the SN, when compared to the consensus metaschema capturing the aggregation of a simple majority of the human experts' opinions.

Animals↗
PubMed2005

Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics.

This paper applies co-occurrence statistics to discover disease-finding associations in a clinical data warehouse. We used two methods, chi2 statistics and the proportion confidence interval (PCI) method, to measure the dependence of pairs of diseases and findings, and then used heuristic cutoff values for association selection. An intrinsic evaluation showed that 94 percent of disease-finding associations obtained by chi2 statistics and 76.8 percent obtained by the PCI method were true associations. The selected associations were used to construct knowledge bases of disease-finding relations (KB-chi2, KB-PCI). An extrinsic evaluation showed that both KB-chi2 and KB-PCI could assist in eliminating clinically non-informative and redundant findings from problem lists generated by our automated problem list summarization system.

Chi-Square Distribution↗
PubMed2005

Inter-rater agreement in physician-coded problem lists.

Coded problem lists will be increasingly used for many purposes in healthcare. The usefulness of coded problem lists may be limited by 1) how consistently clinicians enumerate patients' problems and 2) how consistently clinicians choose a given concept from a controlled terminology to represent a given problem. In this study, 10 physicians reviewed the same 5 clinical cases and created a coded problem list for each case using UMLS as a controlled terminology. We assessed inter-rater agreement for coded problem lists by computing the average pair-wise positive specific agreement for each case for all 10 reviewers. We also standardized problems to common terms across reviewers' lists for a given case, adjusting sequentially for synonymy, granularity, and general concept representation. Our results suggest that inter-rater agreement in unstandardized problem lists is moderate at best; standardization improves agreement, but much variability may be attributable to differences in clinicians' style and the inherent fuzziness of medical diagnosis.

Forms and Records Control↗
PubMed2005

System architecture for temporal information extraction, representation and reasoning in clinical narrative reports.

Exploring temporal information in narrative Electronic Medical Records (EMRs) is essential and challenging. We propose an architecture for an integrated approach to process temporal information in clinical narrative reports. The goal is to initiate and build a foundation that supports applications which assist healthcare practice and research by including the ability to determine the time of clinical events (e.g., past vs. present). Key components include: (1) an annotation schema for temporal expressions and the development of an associated tagger; (2) a natural language processing (NLP) system for encoding and extracting medical events and associating them with formalized temporal data; (3) a post-processor, with a knowledge-based subsystem to help discover implicit information, that resolves temporal expressions and deals with issues such as granularity and vagueness; and (4) a reasoning mechanism which models clinical reports as Simple Temporal Problems (STPs).

Humans↗
PubMed2005

Electronic discharge summaries.

Timely completion of Discharge Summaries is a requirement of high quality care. We developed a system for writing electronic Discharge Summaries. DSUM Writer has generated 2464 of 7349 total summaries (34%) and has paid for itself during its first 8 weeks in production. DSUM Writer is a component of a suite of tools (eNote) for electronic physician documentation used to support clinical care, billing and narrative analysis research.

Cost Savings↗