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At least 883 records · Page 49Linked to original sources
PubMed1996

Logical and statistical fallacies in the use of Cox regression models.

Time-dependent covariates are an essential data analysis tool for modeling the effect of a study factor whose value changes during follow-up. However, survival analysis models can yield conclusions that are contrary to the truth if such time-dependent factors are not defined and used carefully. We outline some of the biases that can occur when time-dependent covariates are used improperly in a Cox regression model. For example, we discuss why one should almost never use a covariate that has been averaged over a patient's entire follow-up time as a baseline covariate. Instead, the baseline value should be used as a covariate, or the cumulative average up to each point in time should be used as a time-dependent covariate. We also document why one should use time-dependent covariates with great caution in analyses when the evaluation of a baseline factor is the primary objective. Several simulated examples are given to illustrate the direction and magnitude of the biases that can result from not adhering to some basic assumptions that underlie all survival analysis methodologies.

Bias↗
PubMed2003

Ordinary logic in unordinary lay theories: a key to understanding proneness to medication nonadherence in schizophrenia.

This article examines attitudes of people with schizophrenia toward their medical treatment within the context of the general theories that they develop about their condition. In-depth interviews were conducted with 36 hospitalized patients with schizophrenia, and the findings were analyzed by the constant comparative method. Five theories, focusing on the basic concepts of body, machine, war, mission, and nature, were detected, thus yielding a typology enabling different patients' proneness to medication nonadherence to be explained and predicted. Conclusions are drawn in relation to improving medication management and communication between patients and health care providers.

Adolescent↗
PubMedDate not supplied

Renoir, Pneumon-IA and Terap-IA: three medical applications based on fuzzy logic.

The research at the IIIA has produced over more than a decade two versions of a tool for developing knowledge-based systems: Milord and Milord II. This tool has been mainly used for the development of medical applications. In this paper we summarize the Milord II approximate reasoning approach based on fuzzy sets, and three medical applications: rheumatology diagnosis (Renoir), pneumonia diagnosis (Pneumon-IA) and pneumonia treatment (Terap-IA).

Artificial Intelligence↗
PubMed1997

Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model.

The process of patient care performed by an anaesthesiologist during high invasive surgery requires fundamental knowledge of the physiologic processes and a long standing experience in patient management to cope with the inter-individual variability of the patients. Biomedical engineering research improves the patient monitoring task by providing technical devices to measure a large number of a patient's vital parameters. These measurements improve the safety of the patient during the surgical procedure, because pathological states can be recognised earlier, but may also lead to an increased cognitive load of the physician. In order to reduce cognitive strain and to support intra-operative monitoring for the anaesthesiologist an intelligent patient monitoring and alarm system has been proposed and implemented which evaluates a patient's haemodynamic state on the basis of a current vital parameter constellation with a knowledge-based approach. In this paper general design aspects and evaluation of the intelligent patient monitoring and alarm system in the operating theatre are described. The validation of the inference engine of the intelligent patient monitoring and alarm system was performed in two steps. Firstly, the knowledge base was validated with real patient data which was acquired online in the operating theatre. Secondly, a research prototype of the whole system was implemented in the operating theatre. In the first step, the anaesthetists were asked to enter a state variable evaluation before a drug application or any other intervention on the patient into a recording system. These state variable evaluations were compared to those generated by the intelligent alarm system on the same vital parameter constellations. Altogether 641 state variable evaluations were entered by six different physicians. In total, the sensitivity of alarm recognition is 99.3%, the specificity is 66% and the predictability is 45%. The second step was performed using a research prototype of the system in anaesthesiological routine. The evaluation of 684 events yielded a sensitivity, specificity and predictability of the alarm recognition of more than 99%.

Anesthesiology↗
PubMed1997

Fundamentals of feedback control: PID, fuzzy logic, and neural networks.

Feedback controllers have been shown to bring blood pressure, muscle relaxation, inhalation drug concentration, and ventilation to the target level and keep it at the target as quickly and as accurately as can a well-trained clinician. Feedback control is an effective and convenient clinical tool for optimizing the day-to-day delivery of anesthetics, reducing induction time, delivering a minimum amount of drug, and avoiding costly delays from failing to keep the patient in a desired state.

Anesthetics↗
PubMed2001

Neural processing: the logic of multiplication in single neurons.

Theory indicates that neural networks can derive considerable computational power from a simple multiplication of their inputs, but the extent to which real neurons do this is unclear. A recent study of the auditory localization pathway of the barn owl has shed new light on this important question.

Animals↗
PubMed2001

Fuzzy logic and mechanical ventilation.

Closed-loop control can achieve appropriate ventilation of the lungs in a healthy person by involving the continuous interaction of three components: sensors, controllers, and effectors. The sensors are the chemo-mechanoreceptors, which continuously monitor key bodily functions affected by ventilation. This information is relayed to the controllers, the respiratory centers in the brain, allowing them to determine how actual ventilation compares with that needed by the body. Finally, the controllers direct the effectors, the muscles of respiration, to adjust the ventilation accordingly. When a patient is in respiratory failure, the effector's role is taken over by a mechanical ventilator. The issue that is considered in this article is how the physician might be taken out of the feedback loop.

Algorithms↗