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Biomedical subjects

R S Patil

Publications and source records attributed to R S Patil.

23 records · Page 2Linked to original sources
PubMedDate not supplied

Viral infections in newborns through exchange transfusion.

Preprocedure sera of thirty one neonates requiring exchange transfusion were tested for serological markers of HBV, HCV, CMV, HIV and LFT. All the babies were investigated for these parameters one week and two months after transfusion to evaluate the risk of transmission of viral infection. Serological markers for these viral infections were also studied in the mothers and donors' blood to establish the route of infection. Donors' blood used for transfusion was pretested for HBsAg, VDRL and anti-HIV. HBsAg was detected one week post exchange in one baby and two months post exchange in two babies. Exchange transfusion was implicated in two of them, where one donor had HBsAg and the other anti-HBc. Vertical transmission accounted for the remaining one. Out of these HbsAg positive cases, one showed evidence of recently acquired CMV infection. Vertical transmission of anti-HCV was observed in one case. None of the neonates, mothers and donors were positive for anti-HIV. In view of probable serious consequences of HBV and HCV infections, blood used for exchange transfusion ought to be screened for anti-HBc and anti-HCV, besides routine HBsAg, VDRL and anti-HIV screening.

Blood-Borne Pathogens↗
PubMedDate not supplied

Causal reasoning in computer programs for medical diagnosis.

Over the last decade substantial advances have been made in the use of causal pathophysiological knowledge in artificial intelligence-based programs for medical diagnosis. Various forms of causal representations have been used. They include probabilistic models, quantitative models, qualitative models, and models that describe causal relations at multiple levels of detail. This paper briefly analyses these methods using three representative systems. Outstanding problems and possible direction in further exploitation of causal reasoning for medical decision-support systems are also discussed.

Artificial Intelligence↗
PubMedDate not supplied

Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information within an expert system.

The authors are developing an expert-system electrocardiogram (ECG) arrhythmia detector (HOBBES) for automated, long-term rhythm analysis. HOBBES employs rules and procedures that emulate how human experts analyze ECGs. This paper describes methods that HOBBES employs for improving error detection and correction in processing noisy ECGs. During periods of clean data, HOBBES develops a knowledge base that describes typical beat shapes, typical interbeat intervals between beats of different types, and patterns of beat sequences that it has observed. During periods of noisy data, HOBBES applies the information learned from the clean data to reject artifact and classify beats. HOBBES was evaluated in a noise-stress test using 35 half-hour ECG records containing a mixture of supraventricular and ventricular ectopy in normal sinus rhythm. In comparison with a classical arrhythmia detector (ARISTOTLE), HOBBES increased the number of correctly classified beats and enhanced the rejection of artifact.

Arrhythmias, Cardiac↗