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PubMed2006

A taxonomic description of computer-based clinical decision support systems.

OBJECTIVE: Computer-based clinical decision support systems (CDSSs) vary greatly in design and function. Using a taxonomy that we had previously developed, we describe the characteristics of CDSSs reported in the literature. METHODS: We searched PubMed and the Cochrane Library for randomized controlled trials (RCTs) published in English between 1998 and 2003 that evaluated CDSSs. We coded each CDSS using our taxonomy. RESULTS: 58 studies met our inclusion criteria. The 74 reported CDSSs varied greatly in context of use, knowledge and data sources, nature of decision support offered, information delivery, and workflow impact. Two distinct subsets of CDSSs were seen: patient-directed systems that provided decision support for preventive care or health-related behaviors via mail or phone (38% of systems), and inpatient systems targeting clinicians with online decision support and direct online execution of the recommendations (18%). 84% of the CDSSs required extra staffing for handling CDSS-related input or output. CONCLUSION: Reported CDSSs are heterogeneous along many dimensions. Caution should be taken in generalizing the results of CDSS RCTs to different clinical or workflow settings.

Databases, Bibliographic↗
PubMed2006

Using MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles.

Biomedical abbreviations and acronyms are widely used in biomedical literature. Since many of them represent important content in biomedical literature, information retrieval and extraction benefits from identifying the meanings of those terms. On the other hand, many abbreviations and acronyms are ambiguous, it would be important to map them to their full forms, which ultimately represent the meanings of the abbreviations. In this study, we present a semi-supervised method that applies MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles. We first automatically generated from the MEDLINE abstracts a dictionary of abbreviation-full pairs based on a rule-based system that maps abbreviations to full forms when full forms are defined in the abstracts. We then trained on the MEDLINE abstracts and predicted the full forms of abbreviations in full-text journal articles by applying supervised machine-learning algorithms in a semi-supervised fashion. We report up to 92% prediction precision and up to 91% coverage.

Artificial Intelligence↗
PubMed2005

Pre-randomization and de-randomization in emergency medical research: new names and rigorous criteria for old methods.

Clinical trials are performed to determine if a therapy is effective in the treatment of a disease. The methods of randomization and blinding are used to assure that the only planned difference between the two groups is the therapy itself, and differences in outcome cannot be attributed to bias. Emergency medical conditions, and in particular therapies that must be administered in an emergency, present challenges to inclusion, exclusion, randomization, and blinding that are at times insurmountable in the context of available resources. Pre-randomization (that is, assigning the therapy to be used before the event occurs) and de-randomization (that is, removing randomized cases that do not meet established inclusion criteria) may address some of the challenges resulting from emergency enrollment but have the potential to create bias. We describe these techniques, and provide criteria that should be employed if pre-randomization and/or de-randomization are being considered. It is possible to use these techniques to successfully complete clinical trials that would not have been possible using only standard methodology and still ensure that results are without bias.

Emergency Medicine↗
PubMed2005

Surveys.

Explore the source record for details and available documents.

Data Collection↗
PubMedDate not supplied

A mathematical typology analysis of DSM-III-R personality disorder classification: grade of membership technique.

This study employed grade of membership (GoM) analysis in a clinical setting to determine if the DSM-III-R personality disorder (PD) diagnostic criteria cluster into recognizable disorders resembling the official axis II nosology. The GoM model, based on fuzzy-set theoretic concepts, explicitly examines medical diagnostic systems by quantitatively identifying and characterizing subpatterns of illness within a broad class. A semistructured assessment of 110 outpatients was performed for 12 PDs and their 112 diagnostic criteria. GoM analysis was performed using internal variables of the 112 PD criteria rated as present or absent. Demographic variables, axis I and II diagnosis (structured clinical Interview for DSM [SCID]), and treatment response (Global Adjustment Scale [GAS]) information were used as external validators. Four pure types (PT) provided the most satisfactory solution to the data. PT-I is characterized by marked maladaptive personality pathology, which is manipulative, egocentric, impulsive, and alloplastic. PT-II consists primarily of exaggerated socially anxious and detached traits. PT-III is sociably dependent and autoplastic. PT-IV is essentially asymptomatic. GoM provides a more parsimonious handling of the PD criteria than provided by classifying according to DSM categories. The analysis fails to confirm the natural occurrence of any single specific axis II PD or cluster.

Adult↗
PubMed1987

Statistical analysis of channel current from a membrane patch. II. A stochastic theory of a multi-channel system in the steady-state.

A general stochastic theory is presented for analysis of current records of a patch containing an arbitrary number (N) of independent homologous channels in the steady-state. We give the "basic theorem" that at the instant of any open (or shut) transition of a channel, the other N-1 channels are located in each state with a probability equal to those in the steady-state, if enough transitions are observed. Using the "basic theorem", we derived: (a) the time-dependent open and shut frequencies after a definite type of transition, and (b) the probability density functions (pdf) of the duration of any period between two successive transitions. Briefly, the main results obtained were: (1) The time-dependent open (or shut) transition frequency after every shut (or open) transition at t = 0 in an N-channel patch, fJSh,Op(t)(N) (or fJOp,Sh(t)(N)), is the same as that of a one-channel patch except for the value of the constant. (2) In the all-shut (or all-open) period of a patch, the average duration of the period is 1/N, and the slowest exponential decay constant contained in the pdf is N times those of a single channel patch, respectively. (3) An example calculation for small N showed that the stochastic properties of a single channel can be obtained even when N is uncertain, if the channel open probability is small and exponential decay constants are separated. (4) When the channels are in equilibrium, the pdf of duration of every type of period in the patch is described by a sum of exponential terms with positive coefficients. This also holds for fJSh,Op(t)(N) and fJOp,Sh(t)(N).

Ion Channels↗
PubMed1980

Predictive value and efficiency of laboratory testing.

Literature on determining reference values and reference intervals on "normal" or "healthy" individuals is abundant. It is impossible, however, to evaluate a data set of reference values and select a suitable reference interval that will be meaningful for the practice of medicine. The reference interval, no matter how derived statistically, tells us nothing about disease. This is the main reason the concepts of "normal values" have failed us and why "reference values" will prove similarly disappointing. By studying these same constituents in a variety of disease states as well, it will be possible to select "referent values" that will make the test procedure meaningful for diagnostic purposes. In order to obtain meaningful referent values for predicting disease, it is necessary to study not only the "healthy" reference population, but patients with the disease in question, and patients who are free of the disease in question but who have other diseases. Studies of this type are not frequently found for laboratory tests that are in common use today.

Clinical Laboratory Techniques↗