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PubMed1985

GFREG: a computer program for maximum likelihood regression using the Generalized F distribution.

A FORTRAN program is described for maximum likelihood estimation within the Generalized F family of distributions. It can be used to estimate regression parameters in a log-linear model for censored survival times with covariates, for which the error distribution may have a great variety of shapes, including most distributions of current use in biostatistics. The optimization is performed by an algorithm based on the generalized reduced gradient method. A stepwise variable search algorithm for covariate selection is included in the program. Output features include: model selection criteria, standard errors of parameter estimates, quantile and survival rates with their standard errors, residuals and several plots. An example based on data from Princess Margaret Hospital, Toronto, is discussed to illustrate the program's capabilities.

Biometry↗
PubMed1986

A simple computer program for calculating areas under concentration-time curves.

A simple program has been developed for calculating areas under concentration-time curves with a home computer. Although it was primarily produced in BBC BASIC for the BBC microcomputer, an adaptation has been made to allow its use with a limited subset of BASIC commands on other computers. After the time and concentration data have been entered, the computer produces a smooth curve running through all data points and employs a spline-fitting interpolation technique to produce equally spaced points, as required by Simpson's rule which is used as the numerical integration procedure. The areas of the vertical strips so formed are then calculated and added together, the integration times being selected by the investigator himself. Comparison with a traditional method showed a good correlation, as did comparison with a commercial NONLIN program. The simple program allows a large number of observations to be analysed quickly, and has proved very useful in calculating AUC values in individual patients in studies with various new antimicrobial agents.

Anti-Infective Agents↗
PubMedDate not supplied

The early diagnosis of acute myocardial infarction. Comparison of a simple algorithm with a computer program for electrocardiogram interpretation.

The sensitivity and specificity of electrocardiographic (ECG) interpretation by a simple algorithm was compared with a computer read ECG machine. Clinical data and ECG findings on 264 consecutive patients admitted to a coronary care unit with suspected acute myocardial infarction were prospectively entered into an algorithm with 13 end-points. These end-points were compared with the interpretations of a computer read ECG machine (Marquette MAC PC). 86 patients (32.5%) had confirmed acute infarction. 85% of those with infarction had some form of ST elevation on their initial ECG. Patients with ST elevation presented earlier (4.9 +/- 4.9 versus 8.0 +/- 9.7 hours after symptom onset, p < 0.001), and were older (66.5 +/- 11.0 versus 62.0 +/- 12.5 years, p < 0.01) than those without infarction. According to the algorithm 94.2% of patients with infarction had some form of ECG abnormality, compared with 55.6% of those without infarction (p < 0.001). The area under the receiver operating characteristic (ROC) curve of the algorithm was 92.3% of the area of the graph. This was more (p < 0.01) than the area under the ROC curve of the interpretations of the computer read ECG machine (83.9%). Marked ST elevation with reciprocal changes was the most specific indicators of infarction (Likelihood ratio 51.7). The algorithm, therefore, was comparatively sensitive and specific in the early diagnosis of acute infarction.

Adult↗
PubMed1996

Validation of a new computer program for Minnesota coding.

The Minnesota code (MC) is a classification system for electrocardiograms (ECGs) that is used for ECG coding in epidemiologic studies. As the MC measurement procedures and rules are complex, visual coding is time-consuming and error-prone. Automation should reduce measurement and coding errors. The authors developed an MC program, closely adhering to the MC regulations. To validate the program, a test set of 300 ECGs containing a wide variety of codable patterns was collected. The ECGs were coded independently by the program and by an experienced human reader. A reference code ("truth") was established by resolving disagreements through a consensus procedure. If the computer and human agreed, they were considered to be correct. Sensitivity and specificity were computed for each of the nine main code categories of the MC, both for the computer and for visual coding. The results show that the program is as good as or better than the human reader for sensitivity and specificity of all MC categories. Particularly noteworthy is the good program performance for arrhythmia coding. Most coding differences between the program and truth arise from small, borderline measurement differences in combination with the all-or-none character of the coding criteria. In conclusion, computerized Minnesota coding is a valuable alternative or supplement to visual coding.

Arrhythmias, Cardiac↗
PubMed1995

Computer Program for Diagnosing and Teaching Geographic Medicine.

One of the unique aspects of infectious disease is its wide variety, both in time and place. The specialist practicing in India may have little or no expertise in Peruvian disease. A colleague in New York may be called upon to diagnose and treat conditions originating in Africa, Asia, South America, Fiji and Papua, New Guinea. At the same time, this colleague must be familiar with the pathogens that originate in Texas, Hawaii, and Canada. Indeed, even the full-time infectious diseases specialist may not be conversant in diseases such as lagochilascariasis, louping ill, and lobomycosis. War, famine, education, immigration, and business travel have contributed to the advent of specialists in Geographic Medicine and Emporiatrics, otherwise known as Travel medicine. The "art" of diagnosis is largely an ability (albeit subconscious) to rank probabilities based on the incidences of likely diseases and the chance of encountering given clinical features within each disease. In theory, Bayesian analysis could be employed to diagnose disease accurately when given proper input. A multicenter study was undertaken to test a comprehensive computer driven-software program that incorporates worldwide epidemiologic and clinical parameters.

Journal Article↗