Input Clinical Parameters for Cardiac Heart Failure Characterization Using Machine Learning

Congestive Heart Failure (CHF) is a serious chronic cardiac condition that brings high risk of urgent hospi- talization and could lead to death. In this work we show how all the input clinical parameters for classifying CHF using Machine Learning can be acquired. The requested input are Blood Pres- sure, Heart Rate, Brain Natriuretic Peptide, Electrocardio- gram, Blood Oxygen Saturation, Height, Weight and Ejection Fraction. The next step will be designing a novel device and con- necting it to our Machine Learning classifier. A particular at- tention will be put to the assessment of electromagnetic compat- ibility (EMC) with other devices, taking into account that this new device will be used in many different settings (home, out- door, etc.

Monozygotic Twins Discordant for the Acquired Immunodeficiency Syndrome

• Monozygotic twin girls discordant for acquired immunodeficiency syndrome were born to parents with antibodies to human T-cell lymphotropic virus type III. One twin had clinical evidence of the syndrome with tests positive for antibody, whereas the other at the age of 3 years was clinically, serologically, and virologically normal.(AJDC 1986;140:678-679