Biomedical subjects

Mark R Lehto

Publications and source records attributed to Mark R Lehto.

4 recordsLinked to original sources

PubMed2004

Computerized coding of injury narrative data from the National Health Interview Survey.

OBJECTIVE: To investigate the accuracy of a computerized method for classifying injury narratives into external-cause-of-injury and poisoning (E-code) categories. METHODS: This study used injury narratives and corresponding E-codes assigned by experts from the 1997 and 1998 US National Health Interview Survey (NHIS). A Fuzzy Bayesian model was used to assign injury descriptions to 13 E-code categories. Sensitivity, specificity and positive predictive value were measured by comparing the computer generated codes with E-code categories assigned by experts. RESULTS: The computer program correctly classified 4695 (82.7%) of the 5677 injury narratives when multiple words were included as keywords in the model. The use of multiple-word predictors compared with using single words alone improved both the sensitivity and specificity of the computer generated codes. The program is capable of identifying and filtering out cases that would benefit most from manual coding. For example, the program could be used to code the narrative if the maximum probability of a category given the keywords in the narrative was at least 0.9. If the maximum probability was lower than 0.9 (which will be the case for approximately 33% of the narratives) the case would be filtered out for manual review. CONCLUSIONS: A computer program based on Fuzzy Bayes logic is capable of accurately categorizing cause-of-injury codes from injury narratives. The capacity to filter out certain cases for manual coding improves the utility of this process.

Forms and Records Control↗

PubMed2003

Interactive decision support system to predict print quality.

Customers using printers occasionally experience problems such as fuzzy images, bands, or streaks. The customer may call or otherwise contact the manufacturer, who attempts to diagnose the problem based on the customer's description of the problem. This study evaluated Bayesian inference as a tool for identifying or diagnosing 16 different types of print defects from such descriptions. The Bayesian model was trained using 1701 narrative descriptions of print defects obtained from 60 subjects with varying technical backgrounds. The Bayesian model was then implemented as an interactive decision support system, which was used by eight 'agents' to diagnose print defects reported by 16 'customers' in a simulated call centre. The 'agents' and 'customers' in the simulated call centre were all students at Purdue University. Each customer made eight telephone calls, resulting in a total of 128 telephone calls in which the customer reported defects to the agents. The results showed that the Bayesian model closely fitted the data in the training set of narratives. Overall, the model correctly predicted the actual defect category with its top prediction 70% of the time. The actual defect was in the top five predictions 94% of the time. The model in the simulated call centre performed nearly as well for the test subjects. The top prediction was correct 50% of the time, and the defect was one of the top five predictions 80% of the time. Agent accuracy in diagnosing the problem improved when using the tool. These results demonstrated that the Bayesian system learned enough from the existing narratives to accurately classify print defect categories.

Adolescent↗

PubMed2002

Young drivers' decision making and safety belt use.

Past research in safety belt use has primarily focused on describing the relationship between drivers' demographic characteristics and safety belt use. This study compared the impact of situational factors (the direction of collision, the type of road, and the presence of an airbag system), demographic factors, and constructs (criteria) elicited from subjects regarding safety belt use. Based on the results obtained, a conceptual model was developed. The model indicated that drivers' decision-making process when judging the level of accident risk and usefulness of safety belts differs from those that determine actual behavior. Perceived risk was related to road type, perceived consequences of an accident, perceived usefulness of safety belts, self responsibility, the time available for the driver to warn the other driver, dangerous behavior, and gender. These variables showed that people were able to rationally judge the risk. Despite the fact that people judge behavior in what appeared to be a rational manner, risk perception was not a good predictor of belt use. Belt use was mainly influenced by individual factors such as gender, grade point average (GPA), and age. Other factors impacting safety belt use included the perceived frequency of an accident and the S.D. of perceived usefulness of safety belts.

Adolescent↗

PubMed1995

A Distributed Signal Detection Theory Model: Implications for the Design of Warnings.

A distributed signal detection theory model is employed to analyze the effectiveness of warnings under different operating conditions. In particular, the following two cases are examined: (a) the warning on a product is always present and (b) the warning on a product is administered selectively. The comparative effects of warning versus no warning are described. It is established that selectivity always increases effectiveness. The implications to optimal warning design of intermittent hazard versus continuous hazard are discussed. Furthermore, a series of experiments is conducted to compare the behavior of human participants with the prescriptive behavior of the normative model. The changes in the behavior of the human participants response to changes in the warning levels are consistent with the predictions of the model. These changes should be taken into consideration in the design of warnings.

design of warnings↗