A
body fluid movement apparatus includes a
body fluid movement apparatus tube with a lumen, a proximal end, a distal end and a
balloon coupled to the proximal end. The
balloon is configured to be positioned in an interior of a bladder. The proximal end is configured to provide flow of
body fluid from the bladder through the lumen, with a draining bag collecting body fluid from the bladder through the lumen. The drainage bag has an inlet port for receiving body fluid and an outlet port for draining body fluid from the drainage bag. The
urinary catheter tube includes the proximal end and the proximal end, with a plurality of body fluid draining holes that receive body fluid from the bladder and allow it to be transported to and though the body fluid movement apparatus tube. One or more sensors are positioned in an interior of the
catheter tube and are in contact with the patient's
urine. The one or more sensors provide sensor data, at least a portion of sensor data being
noisy data that contains one or more of errors, outliers, and inconsistencies. Logic resources provide preprocessing of the
noisy data to create cleaned sensor data used for one or more of: identification, cleaning, and transforming of
noisy data for the
machine learning algorithms to produce the cleaned sensor data. An
artificial intelligence system coupled to or including an AI
database. The AI engine. with a plurality of
machine learning algorithms, provide analysis of the cleaned sensor data used for medical monitoring of one or more medical conditions of the patient by the
machine learning algorithms, the analysis of the cleaned sensor data being used for the medical monitoring of the patient.