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Biomedical subjects

G E Davis

Publications and source records attributed to G E Davis.

85 records · Page 5Linked to original sources
PubMed1978

Quantitative high-performance liquid chromatography of nucleosides in biological materials.

A rigorous, comprehensive, and reliable reversed-phase high-performance liquid chromatographic (HPLC) method has been developed for the analysis of ribonucleosides in urine (psi, m1A, m1I, m2G, A, m2(2)G). An initial isolation of ribonucleosides with an affinity gel containing an immobilized phenylboronic acid was used to improve selectivity and sensitivity. Response for all nucleosides was linear from 0.1 to 50 nmoles injected and good quantitation was obtained for 25 microliter or less of sample placed on the HPLC column. Excellent precision of analysis for urinary nucleosides was achieved on matrix dependent and independent samples, and the high resolution of the reversed-phase column allowed the complete separation of 9 nucleosides from other unidentified UV absorbing components at the 1-ng level. Supporting experimental data are presented on precision, recovery, chromatographic methods, minimum detection limit, retention time, relative molar response, sample clean-up, stability of nucleosides, boronate gel capacity, and application to analysis of urine from patients with leukemia and breast cancer. This method is now being used routinely for the determination of the concentration and ratios of nucleosides in urine from patients with different types of cancer and in chemotherapy response studies.

Boron Compounds↗
PubMedDate not supplied

Predicting length of stay for psychiatric diagnosis-related groups using neural networks.

OBJECTIVE: To test the effect of diagnosis on training an artificial neural network (ANN) to predict length of stay (LOS) for psychiatric patients involuntarily admitted to a state hospital. DESIGN: A series of ANNs were trained representing schizophrenia, affective disorders, and diagnosis-related group (DRG) 430. In addition to diagnosis, variables used in training included demographics, severity of illness, and others identified to be significant in predicting LOS. RESULTS: Depending on diagnosis, ANN-predictions compared with actual LOS indicated accuracy rates ranging from 35% to 70%. The validity of ANN predictions was determined by comparing LOS estimates with the treatment team's predictions at 72 hours following admission, with the ANN predicting as well as or better than did the treatment team in all cases. CONCLUSIONS: One problem in traditional approaches to predicting LOS is the inability of a derived predictive model to maintain accuracy in other independently derived samples. The ANN reported here was capable of maintaining the same predictive efficiency in an independently derived cross-validation sample. The results of ANNs in a cross-validation sample are discussed and the application of this tool in augmenting clinical decision is presented.

Adult↗
PubMedDate not supplied

Using artificial neural networks and the Gutenberg-Richter power law to "rightsize" a behavioral health care system.

The authors propose a new paradigm for designing and managing behavioral health care systems by using artificial neural networks to measure quality of care (Q), using length-of-stay (LOS) prediction and the variation in LOS prediction, and subsequently using the variation of Q to obtain a measure of uncertainty in treatment. The paper proposes that mental illness is fractal in nature (self-similar at all scales) and conforms to power laws like the Gutenberg-Richter (G-R) law, whereby there is a log-log relationship between frequency of episodes (i.e., admissions) and the severity of those episodes. The paper also hypothesizes that 28% is the average uncertainty (residual or excess entropy) in the treatment of mental illness. The authors use the G-R paradigm to calculate the severity of admission and, subsequently, the minimum number of beds for different behavioral health care facilities and propose the optimal partition of beds between community and state services, thereby "balancing" the delivery system. The data presented support the notion that mental illness manifests complexity and "self-organized criticality." The authors hypothesize that correcting deviations from the theoretical G-R curve for each level of care will allow optimum resource distribution, improve quality of care, and reduce costs.

Behavioral Medicine↗
PubMedDate not supplied

A neural network that predicts psychiatric length of stay.

We developed a neural network to predict length of stay after admission to a state psychiatric hospital. The network was trained with data from 1064 cases randomly selected over a 3 1/2-year period, and its accuracy was tested against actual length-of-stay patterns and predictions made by a team of clinicians 72 hours after admission. The network performed at least as well as the team of clinicians. Successful prediction of length of stay could result in more appropriate use of services, more timely initiation of treatment, better resource planning, and cost control--highly sought dividends in an era of diminishing fiscal resources.

Adult↗