A new user-oriented electrocardiographic program.

C A Caceres

Home/Archive/PubMed

PubMed · 1102375

A new user-oriented electrocardiographic program.

C A Caceres

View original source ↗Export metadata ↓

Abstract

A new program for electrocardiographic processing will become available in 1975, "tuned", as part of a system, to take advantage of newer computer and data acquisition hardware at the same time as past experience on other programs has been carefully retained. User flexibility is a key feature along with safe methodology. The program is undergoing modification as part of a data pool user plan.

Explore related subjects

Diagnosis, Computer-Assisted Electrocardiography Heart Diseases Humans Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

C A Caceres. 1975. A new user-oriented electrocardiographic program.. https://pubmed.ncbi.nlm.nih.gov/1102375/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed1992

[A method of identifying problems and of detecting diagnostic deficiencies by using the primary care center information system].

Explore the source record for details and available documents.

Diagnosis, Computer-Assisted↗

PubMed1992

Early detection of epidemic influenza.

Explore the source record for details and available documents.

Diagnosis, Computer-Assisted↗

PubMed1992

Using an artificial neural network to diagnose hepatic masses.

Using abdominal ultrasonographic data and laboratory tests, radiologists often find differential diagnoses of hepatic masses difficult. A computerized second opinion would be especially helpful for clinicians in diagnosing liver cancer because of the difficulty of such diagnoses. A back-propagation neural network was designed to diagnose five classifications of hepatic masses: hepatoma, metastatic carcinoma, abscess, cavernous hemangioma, and cirrhosis. The network input consisted of 35 numbers per patient case that represented ultrasonographic data and laboratory tests. The network architecture had 35 elements in the input layer, two hidden layers of 35 elements each, and 5 elements in the output layer. After being trained to a learning tolerance of 1%, the network classified hepatic masses correctly in 48 of 64 cases. An accuracy of 75% is higher than the 50% scored by the average radiology resident in training but lower than the 90% scored by the typical board-certified radiologist. When sufficiently sophisticated, a neural network may significantly improve the analysis of hepatic-mass radiographs.

Diagnosis, Computer-Assisted↗