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M S Tuttle

Publications and source records attributed to M S Tuttle.

At least 19 recordsLinked to original sources
PubMed2001

hVPS41 is expressed in multiple isoforms and can associate with vesicles through a RING-H2 finger motif.

Vps41p, the protein encoded by the yeast gene VPS41, has been shown to mediate formation of AP-3 transport vesicles from the Golgi apparatus and to facilitate the docking and fusion of lysosomal vesicles. Although both of these activities involve transient association with membrane structures, the mechanisms that mediate those interactions have not been determined. Orthologues of VPS41 have been identified in humans, Drosophila, tomato, and Arabidopsis; the degree of sequence similarity among these genes suggests a highly conserved function. Here we provide evidence that hVps41, the human homologue of Vps41p, is expressed in two isoforms that differ in that one contains a C-terminal RING-H2 sequence motif. Transient expression analysis suggests that this RING-H2 domain is responsible for membrane association. This observation was further supported by the cytosolic localization of site-specific mutants. A truncated construct containing only the hVps41 RING-H2 domain was found to associate with a class of intracellular vesicles that originated from the Golgi and showed partial coincidence with the delta subunit of the adaptor protein complex-3. Together with information from the homologous yeast system, these results suggest that hVps41 may also be involved in the formation and fusion of transport vesicles from the Golgi.

Amino Acid Sequence↗
PubMed2000

Potato-lactulose breath hydrogen testing as a function of gastric motility in diabetes mellitus.

OBJECTIVE: Scintigraphic determination of gastric emptying is the current standard for the assessment of gastric motility and the diagnosis of diabetic gastroparesis. However, such studies are expensive, inconvenient, and involve exposure to radiation. Because the time course of breath hydrogen (H2) excretion after ingestion of lactulose correlates with upper gastrointestinal transit time, we hypothesized that patients with diabetic gastroparesis would exhibit prolonged breath H2 excretion after ingestion of a test meal containing complex carbohydrate and lactulose compared to subjects without diabetes and subjects with diabetes but without gastroparesis. RESEARCH DESIGN AND METHODS: Ten healthy subjects without diabetes, 10 subjects with diabetes but without gastroparesis (gastric emptying T1/2,T1/2 < 90 minutes), and 10 subjects with diabetes and previously diagnosed gastroparesis (T1/2 > 90 minutes) were admitted for a single 24-hour study. Gastric motility agents were withheld 24 hours prior to the study. Euglycemia was established and maintained overnight in subjects with diabetes with continuous intravenous insulin infusion. At 6:00 AM, all subjects ingested a breakfast containing 100 g of cooked potato starch and 20 g lactulose. Breath H2 excretion was monitored at baseline and every 30 minutes for 12 hours after ingestion of the test meal. RESULTS: Twelve hours after ingestion of the test meal, raw and baseline adjusted breath H2 excretion was significantly elevated in the gastroparesis group compared to the unaffected group with diabetes and the group without diabetes (p < 0.001). The baseline and 12-hour data points were adequate to discriminate between normal and delayed gastric emptying. CONCLUSIONS: We conclude that patients with previously diagnosed gastroparesis exhibit prolonged breath H2 excretion after ingestion of a test meal. This test may prove to be a safe, reliable, and affordable outpatient screening test for diabetic gastroparesis.

Adult↗
PubMed1999

Desiderata for a clinical terminology server.

Clinical terminology servers are distinguished from more broadly based terminology servers intended for nomenclature development or mediation across classifications. Focusing upon the consistent and comparable entry of clinical observations, findings, and events, key desiderata are enumerated and expanded. These include 1) word normalization, 2) word completion, 3) target terminology specification, 4) spelling correction, 5) lexical matching, 6) term completion, 7) semantic locality, 8) term composition and 9) decomposition. Comparisons of this functionality to previously published models and specifications are made. Experience with a clinical terminology server, Metaphrase, is described.

Abstracting and Indexing↗
PubMed1998

Metaphrase: an aid to the clinical conceptualization and formalization of patient problems in healthcare enterprises.

Patient descriptors, or "problems," such as "brain metastases of melanoma" are an effective way for caregivers to describe patients. But most problems, e.g., "cubital tunnel syndrome" or "ulnar nerve compression," found in problem lists in an Electronic Medical Record (EMR) are not comparable computationally--in general, a computer cannot determine whether they describe the same or a related problem, or whether the user would have preferred "ulnar nerve compression syndrome." Metaphrase is a scalable, middleware component designed to be accessed from problem-manager applications in EMR systems. In response to caregivers' informal descriptors it suggests potentially equivalent, authoritative, and more formally comparable descriptors. Metaphrase contains a clinical subset of the 1997 UMLS Metathesaurus and some 10,000 "problems" from the Mayo Clinic and Harvard Beth Israel Hospital. Word and term completion, spelling correction, and semantic navigation, all combine to ease the burden of problem conceptualization, entry and formalization.

Humans↗
PubMed1998

A "lexically-suggested logical closure" metric for medical terminology maturity.

Medical Terminologies are becoming increasingly expressive secondary to their increase in size, and are becoming increasingly difficult to analyze secondary to inconsistencies in their use and complex interrelationships that are often not explicitly defined. To address these problems, SNOMED-RT is being developed to allow consistent use, and to define explicitly interrelationships between terms. Ensuring the quality of a terminology system like SNOMED-RT presents new challenges which we are trying to address with theoretically-grounded methodologies for quality management. Here we describe an initial metric toward achieving this goal called "lexically-suggested logical closure." We explain how this metric can be useful for tracking the maturity and quality of a terminology, and apply this metric to track the progress of SNOMED-RT development over a portion of its life-cycle.

Algorithms↗
PubMed1997

Standardized problem list generation, utilizing the Mayo canonical vocabulary embedded within the Unified Medical Language System.

UNLABELLED: VOCABULARY: The Mayo problem list vocabulary is a clinically derived lexicon created from the entries made to the Mayo Clinic's Master Sheet Index and the problem list entries made to the Impression/ Report/Plan section of the Clinical Notes System over the last three years. The vocabulary was reduced by eliminating repetition including lexical variants, spelling errors, and qualifiers (Administrative or Operational terms). Qualifiers are re-coordinated with other terms, at run-time, which greatly increased the number of input strings which our system is capable of recognizing. IMPLEMENTATION: The Problem Manager is implemented using standard windows tools in a Windows NT environment. The interface is designed using Object Pascal. HTTP calls are passed over the World Wide Web to a UNIX based vocabulary server. The server returns a document, which is read into Object Pascal structures, parsed, filtered and displayed. STUDY: This paper reports the results of a recent Usability Trial focused on assessing the viability of this mechanism for standardized problem entry. Eight clinicians engaged in eleven scenarios and responded as to their satisfaction with the systems performance. These responses were observed, videotaped and tabulated. Clinicians in this study were able to find acceptable diagnoses in 91.1% of the scenarios. The response time was acceptable in 92.5% of the scenarios. The presentation of related terms was stated to be useful in at least one scenario by seven of the eight participants. All clinicians wanted to make use of shortcuts which would minimize the amount of typing necessary to encode the concept they were searching for (e.g. Abbreviations, Word Completion). CONCLUSIONS: Clinicians are willing to choose a canonical term from a suggested list (as opposed to their own wording). Clinicians want an "intelligent" system, which would suggest terms within a category (e.g. Types of "Migraine"). They are able to make functional use of our system, in its current state of development. Finally, all clinicians appreciate the value of encoding their problems in a standardized vocabulary, toward improved research, education and practice.

Computer Communication Networks↗
PubMed1996

MEME-II supports the cooperative management of terminology.

Health care enterprises need enterprise-wide terminologies to compare, reuse and repurpose health care descriptions. But once they are created, these terminologies need to be maintained and enhanced to sustain their utility and that of the descriptions encoded with them. MEME II (Metathesaurus Enhancement and Maintenance Environment, Version II) supports the required activities and enables enterprises to leverage their investment in terminology and descriptions by permitting remote-extra-enterprise-enhancements to terminology to be incorporated locally, and local-intra-enterprise-enhancements to be shared remotely. MEME II represents all changes to terminologies as data, or "actions," that can be interpreted by an "action engine." These actions, or messages, represent semantic "units of work" that can be interpreted by other copies of MEME II. The exchange of update messages increases the likelihood that the comparability of terminology-based health care descriptions can be sustained.

Information Systems↗
PubMed1996

Toward reusable software components at the point of care.

An architecture built from five software components -a Router, Parser, Matcher, Mapper, and Server -fulfills key requirements common to several point-of-care information and knowledge processing tasks. The requirements include problem-list creation, exploiting the contents of the Electronic Medical Record for the patient at hand, knowledge access, and support for semantic visualization and software agents. The components use the National Library of Medicine Unified Medical Language System to create and exploit lexical closure-a state in which terms, text and reference models are represented explicitly and consistently. Preliminary versions of the components are in use in an oncology knowledge server.

Computer Systems↗
PubMed1996

A poor precedent.

The single greatest impediment to the successful leveraging of computer-based patient records (CPRs) is the difficulty of creating and maintaining comparable patient descriptions. Specifically, it will be hard to justify the investment required to deploy CPRs widely if the patient descriptions they store are not comparable across successive releases of controlled health-care vocabularies. Thus, it is necessary to solve the controlled health-care vocabulary update problem for CPRs before the comparability of patient descriptions can be sustained. What may seem to be a narrow technical problem of interest only to maintainers of health-care enterprise systems is, instead, a central problem of medical informatics. Cimino's "Formal descriptions and adaptive mechanisms for changes in controlled medical vocabularies" describes a classification of the changes appearing in the 1994 Edition of the International Classification of Diseases (ICD-9-CM). His paper describes the conversion of differences detected between the 1993 and 1994 releases of ICD-9-CM and a conversion of the elements of the classification into the required formal changes to the Medical Entities Dictionary (MED), part of the CPR in use at Columbia Presbyterian Medical Center. Because the process of detecting differences begins with an empirical analysis of the ASCII representations of the 1993 and 1994 releases of ICD-9-CM, it is impossible for a computer program to infer the intent of the changes that caused the differences; instead, a content expert must infer the intent and then update the MED accordingly. A typical task is to infer whether a change in naming also reflects a change in the meaning named. While Cimino's methods and their execution are exemplary in every respect, they nevertheless constitute a kind of "reverse engineering"-an ad hoc attempt to infer intent from details. Reverse engineering of changes to controlled medical vocabularies is a poor precedent. Such methods should be viewed as necessary short-term expedients only, and all parties concerned should work toward an incremental plan by which the intent of changes to controlled health-care vocabularies can be made both explicit and machine processible. Only then can the comparability of patient descriptions be sustained.

Disease↗
PubMed1995

Categorization of free-text problem lists: an effective method of capturing clinical data.

Problem lists assist in organizing patient information in computer based medical records. However, in order to use problem lists for billing, research, decision support and standardization, a categorization of the problems entered is required. We describe the problem list component of our computerized patient record, the On-line Medical Record (OMR), which combines a free-text entry mechanism with a categorization scheme, using a dictionary containing 846 terms. All 118,040 problems entered during the system's six years of use have been analyzed, 477 clinicians have entered a mean +/- S.D. of 238 +/- 604 problems into 22,311 patient records. The average number of problems in each patient's file was 5.1 +/- 3.9. Comments were typed for 80,281 (68%) of the problems, ranging in length from 1 to 2456 characters, with a mean length of 98 +/- 110 characters. Half the problems were entered on the day of the encounter with the patient. Overall, 66% of all problems were categorized in relation to terms from the problem dictionary. Lexical analysis of all problem names showed that 80% could be mapped to Meta 1.4, Snomed 3.0 or a pre-release version of Read 3.0. We conclude that a problem list entry scheme combining free-text entry and optional categorization using a dictionary can result in a high proportion of problems being categorized as desired. Improvement of the system by elimination of unused dictionary terms and addition of 1000 terms identified by the lexical analysis is likely to result in even higher categorization rates.

Humans↗
PubMed1995

Semantic visualization of oncology knowledge sources.

Visualization of knowledge sources can have a substantial impact on the use of such sources at the point of care. This is because barriers to use at the point of care include hours required to master the electronic interfaces to those sources, and minutes required to master the electronic interfaces to those sources, and minutes required to accomplish any one retrieval. For a system to be used regularly at the point of care, therefore, it must be intuitive and fast. This paper presents a three dimensional interface to oncology knowledge sources that aims to meet this challenge.

Databases, Factual↗
PubMed1995

Merging terminologies.

A terminology is a systematic, authoritative collection of concept names, or terms, in some domain. No single terminology names all the important concepts in biomedicine. One approach to creating a more comprehensive biomedical terminology is to merge existing biomedical terminologies, as the UMLS( Metathesaurus( has done for the last six years. Because existing terminologies may overlap--for example, one terminology may name a concept also named by another terminologyQthe terminologies in the Metathesaurus must be merged. Some terminologies suggest merges through their structure or content e.g., they suggest synonyms or connections to other terminologies; other merges can be suggested by algorithm. Regardless, all merges in the Metathesaurus must be approved by a human editor with appropriate domain knowledge. By the time Meta-U96 is released early in 1996, one prototype and seven released versions of the Metathesaurus will have been produced by a sequence of four qualitatively different methods, named for the way in which they merge terms: #1 "Term Rewrite Rules, #2 "Transitive Closure on Facts," #3 "Fact-at-a-Time Concept Merging," and #4 "Action-at-a-Time Object Processing." The development of each method has been constrained by the annual Metathesaurus release schedule. The first two methods made the best use of limited computational resources, and the last two make better use of human editing resources.

National Library of Medicine (U.S.)↗
PubMed1995

Identifying concepts in medical knowledge.

The barrier word method of identifying nominal phrases in text, using a very long barrier word list, was evaluated in two different sets of text. In a sample of 10 paragraphs from the Medical Knowledge Self-Assessment Program of the American College of Physicians, the yield of nominal phrases as a percent of total chunks isolated was 66%. Some 500,000 chunks were isolated from Principles and Practice of Oncology (PPO). 38% of these chunk-occurrences were of chunks which matched to 10,000 concept names in Meta-1.4, the most recent version of the UMLS Metathesaurus. 50 paragraphs from PPO were chosen at random. Co-occurrences of concepts in those paragraphs were reviewed. 42 of the paragraphs had unique or infrequently occurring co-occurrences which described closely the major thrust of the paragraph.

Medical Oncology↗
PubMed1995

Accessing oncology information at the point of care: experience using speech, pen, and 3-D interfaces with a knowledge server.

Oncologists' information needs arise at diverse times and settings. For example: "Is superior vena cava syndrome a medical emergency?" Our collaborative group is developing a system that supports an interface with combinations of spoken, gestural, and simulated three-dimensional manipulation to help an oncologist focus on the information need, not the system. The system requires a small amount of input from the oncologist, and then anticipates what information is pertinent to the patient at hand, based on the sources it has available. The system makes use of a "Knowledge Server" to find relevant information. The Knowledge Server uses selected data for the particular patient from a Computer-based Patient Record (CPR) to provide context for the information needs. The Knowledge Server leverages the Unified Medical Language System (UMLS) resources as well as relevant communications standards. A layered, interaction protocol is used to help manage the fulfillment of information needs. Each of the oncology knowledge sources is transformed into a uniform representation that utilizes both its formal schema (e.g., its table of contents) and its concepts and words indexed through the UMLS Metathesaurus. Our focus on the appropriate use of information from a CPR, and on anticipating oncologists' information needs, resulted from our study of several longitudinal patient scenarios. We believe that our use of scenario-based design techniques will help to ensure the system's success.

California↗
PubMed1995

Navigating to knowledge.

One way to fulfill point-of-care knowledge needs is to present caregivers with a visual representation of the available "answers". Using such a representation, caregivers can recognize what they want, rather than have to recall what they need, and then navigate to an appropriate answer. Given selected pieces of information from a computer-based patient record, an interface can anticipate certain knowledge needs by initializing caregiver navigation in a semantic neighborhood of answers likely to be relevant to the patient at hand. These notions draw heavily on two collaborative projects--the U.S. National Library of Medicine Unified Medical Language System and the U.S. National Cancer Institute Knowledge Server. Both of these projects support navigation because they make the structure of medical knowledge explicit in a way that can be exploited by human interfaces.

Artificial Intelligence↗
PubMed1994

The role of the UMLS in 'storing' and 'sharing' across systems.

We will argue that 'sharing', 're-use', 're-purposing', and 'addition' of health care information is difficult, intrinsically; that the best way to overcome the difficulty is to start doing it, as soon as possible, and that the UMLS Knowledge Sources provide the best place to start. We recommend that the UMLS be used as a default source of biomedical concept names and relationships, as a comprehensive, data-based, 'reference model', and as an example of a large, ecumenical, evolving, continuously updated source of re-usable health care information.

Computer User Training↗