Biomedical subjects

C L Tsien

Publications and source records attributed to C L Tsien.

7 recordsLinked to original sources

PubMed2002

CHIP TUNER: a web tool for evidence-based noise reduction in gene discovery.

The potential for gene discovery, fueled by DNA microchip technology and the sequencing of hundreds of genomes, is unprecedented. In this context, trying to discover genes that are actually of significance rather than merely appearing so due to noise is of utmost importance. We present a web application, CHIP TUNER, which assists in this gene discovery process. Our system uses evidence-based noise reduction to help delineate candidate target genes of biological importance. Specifically, CHIP TUNER learns from redundant experiments an "identity mask" that defines a region of noise inherent to biological sampling and DNA microarray processing; it then takes this into account during actual sample comparisons. The goal of CHIP TUNER is to improve the chances that newly discovered "important" genes are actually of importance before large amounts of time and resources are invested.

Computational Biology↗

PubMed2001

On reporting fold differences.

As we enter an age in which genomics and bioinformatics make possible the discovery of new knowledge about the biological characteristics of an organism, it is critical that we attempt to report newly discovered "significant" phenotypes only when they are actually of significance. With the relative youth of genome-scale gene expression technologies, how to make such distinctions has yet to be better defined. We present a "mask technology" by which to filter out those levels of gene expression that fall within the noise of the experimental techniques being employed. Conversely, our technique can lend validation to significant fold differences in expression level even when the fold value may appear quite small (e.g. 1.3). Given array-organized expression level results from a pair of identical experiments, our ID Mask Tool enables the automated creation of a two-dimensional "region of insignificance" that can then be used with subsequent data analyses. Fundamentally, this should enable researchers to report on findings that are more likely to be in nature truly meaningful. Moreover, this can prevent major investments of time, energy, and biological resources into the pursuit of candidate genes that represent false positives.

Computational Biology↗

PubMed2001

Building ICU artifact detection models with more data in less time.

As many as 86% of intensive care unit (ICU) alarms are false. Multiple signal integration of temporal monitor data by decision tree induction may improve artifact detection. We explore the effect of data granularity on model-building by comparing models made from 1-second versus 1-minute data. Models developed from 1-minute data remained effective when tested on 1-second data. Model development using 1-minute data means that more hours of ICU monitoring (including more artifacts) can be processed in less time. Compression of temporal data by arithmetic mean, therefore, can be an effective method for decreasing knowledge discovery processing time without compromising learning.

Artifacts↗

PubMed2000

Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit.

The high incidence of false alarms in the intensive care unit (ICU) necessitates the development of improved alarming techniques. This study aimed to detect artifact patterns across multiple physiologic data signals from a neonatal ICU using decision tree induction. Approximately 200 h of bedside data were analyzed. Artifacts in the data streams were visually located and annotated retrospectively by an experienced clinician. Derived values were calculated for successively overlapping time intervals of raw values, and then used as feature attributes for the induction of models trying to classify 'artifact' versus 'not artifact' cases. The results are very promising, indicating that integration of multiple signals by applying a classification system to sets of values derived from physiologic data streams may be a viable approach to detecting artifacts in neonatal ICU data.

Artifacts↗

PubMed2000

Event discovery in medical time-series data.

Vast amounts of clinical information are generated daily on patients in the health care setting. Increasingly, this information is collected and stored for its potential utility in advancing health care. Knowledge-based systems, for example, might be able to apply rules to the collected data to determine whether a patient has a certain condition. Often, however, the underlying knowledge needed to write such rules is not well understood. How could these clinical data be useful then? Use of machine learning is one answer. We present a pipeline for discovering the knowledge needed for event detection in medical time-series data. We demonstrate how this process can be applied in the development of intelligent patient monitoring for the intensive care unit (ICU). Specifically, we develop a system for detecting Otrue alarmO situations in the ICU, where currently as many as 86% of bedside monitor alarms are false.

Artificial Intelligence↗

PubMed1998

Using classification tree and logistic regression methods to diagnose myocardial infarction.

Early and accurate diagnosis of myocardial infarction (MI) in patients who present to the Emergency Room (ER) complaining of chest pain is an important problem in emergency medicine. A number of decision aids have been developed to assist with this problem but have not achieved general use. Machine learning techniques, including classification tree and logistic regression (LR) methods, have the potential to create simple but accurate decision aids. Both a classification tree (FT Tree) and an LR model (FT LR) have been developed to predict the probability that a patient with chest pain is having an MI based solely upon data available at time of presentation to the ER. Training data came from a data set collected in Edinburgh, Scotland. Each model was then tested on a separate Edinburgh data set, as well as on a data set from a different hospital in Sheffield, England. Previously published models, the Goldman classification tree[1] and Kennedy LR equation[2], were evaluated on the same test data sets. On the Edinburgh test set, results showed that the FT Tree, FT LR, and Kennedy LR performed equally well, with ROC curve areas of 94.04%, 94.28%, and 94.30%, respectively, while the Goldman Tree's performance was significantly poorer, with an area of 84.03%. The difference in ROC areas between the first three models and the Goldman model is significant beyond the 0.0001 level. On the Sheffield test set, results showed that the FT Tree, FT LR, and Kennedy LR ROC areas were not significantly different (p > = 0.17), while the FT Tree again outperformed the Goldman Tree (p = 0.006). Unlike previous work[3], this study indicates that classification trees, which have certain advantages over LR models, may perform as well as LR models in the diagnosis of patients with MI.

Algorithms↗

PubMed1997

Poor prognosis for existing monitors in the intensive care unit.

OBJECTIVE: To identify areas requiring the most urgent improvement in the intensive care unit (ICU); and to accurately determine the positive predictive value of routine critical care patient monitoring alarms, as well as the common causes for false-positive alarms. DESIGN: Prospective, observational study. SETTING: A multidisciplinary ICU in a university-affiliated children's hospital (excluding children with primary heart disease). INTERVENTIONS: The occurrence rate, cause, and appropriateness of all alarms from tracked monitors were recorded by a trained observer and validated by the bedside nurse over a 10-wk period for a single bedspace at a time. MEASUREMENTS AND MAIN RESULTS: After 298 monitored hrs, 86% of a total 2,942 alarms were found to be false-positive alarms, while an additional 6% were classified as clinically irrelevant true alarms. Only 8% of all alarms tracked during the study period were determined to be true alarms with clinical significance. Alarms were also classified according to whether they were clearly associated with a "patient intervention" (18%), were clearly not associated with a patient intervention (74%), or had unclear association to interventions (8%). While 11% of "nonpatient intervention" alarms were clinically significant true alarms, only 2% of "patient intervention" alarms were so. Positive predictive values for the various devices ranged from < 1% for the pulse oximeter's heart rate signal to 74% for the arterial catheter's mean systemic blood pressure signal during periods free from patient interventions. The pulse oximeter caused false-positive alarms most frequently, with common reasons being bad data format/bad connection and poor contact. CONCLUSION: Efforts to develop intelligent monitoring systems have more potential to deliver significantly improved patient care by initially targeting especially weak areas in ICU monitoring, such as pulse oximetry reliability.

Critical Care↗