A microcomputer-based system for carrying out learned helplessness experiments is described. The hardware is inexpensive and easily available. The program is written in BASIC and must be executed in the compiled form in order to keep up with real-time events. It contains an algorithm for calculating inter-trial intervals and allows the experimentor to freely select experimental conditions such as duration of shock, number of trials, etc. The task that the rat is required to do to stop the shock is also selectable, thus giving the experimentor the opportunity to maximize behavioral differences between escapable and inescapable rats while these differences are being induced. Experimental data on 12 rats that went through this automated protocol are analyzed and discussed.

Analysis of Variance↗

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Identifying personality disorders: towards the development of a clinical screening instrument.

The study objective was to identify a set of personality disorder (PD) criteria from the DSM PD diagnostic sets that can be used to detect subjects with an increased likelihood of having a PD diagnosis. In a series of outpatients evaluated systematically in two waves for every criteria item for 12 DSM-III-R PDs, stepwise logistic regression identified 45 criteria as discriminative for their specific PDs, which are selected for further analysis to assess their ability to discriminate for any PD. Receiver operating characteristic (ROC) analysis is used to evaluate their discriminative power in an independent conjoined sample (N = 1,342) from six centers that assessed every PD criteria item by structured instrument (Structured Clinical Interview for DSM-III-R PDs [SCID-II, Personality Disorder Examination [PDE], and Structured Interview for DSM-III-R PDs [SIDP-R]). The cutoff that maximizes information gain is used to determine the diagnostic threshold (DT). Initially, 15 of 45 criteria are identified. At the 0.43 PD prevalence, a DT of 2 or more of the 15 PD criteria across samples is optimal. The maximum information gain (MIG) is .42 bits, and the AUR is 0.94+/-.007. Other performance indices at this cutoff are .90 sensitivity, .84 specificity, .81 positive predictive power (PPP), .91 negative predictive power (NPP), and .86 hit rate (HR). Taken collectively, the 15 PD criteria selected by the data reduction techniques suggest a narrowed set to be assessed in screening for the presence or absence of any PD with comparable or better psychometric properties than other tests routinely used for diagnosing medical and psychiatric disorders. If specific PD categorization is needed, a second-step comprehensive assessment should follow.

Algorithms↗

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Treatment outcome of an outpatient treatment program for dually-diagnosed veterans: the Cincinnati Veterans Affairs Medical Center.

The Dual Diagnosis Clinic at the Cincinnati VA Medical Center is an integrated treatment program for veterans who have an Axis I psychiatric illness in addition to a substance dependence problem. With a high staff-to-patient ratio and an individualized approach, the DDC treats many dual diagnosis patients effectively. To demonstrate the effectiveness of this approach, an outcome study was conducted looking at the psychiatric emergency room visits, hospitalization rates, and the average length of stay for 557 DDC patients. Patients were divided into two groups: those who completed the assessment period, and those who did not. Equal periods of time before and after this assessment period were studied. Those who completed were more likely to have had fewer psychiatric emergency room visits (p < 0.000001) after the assessment period than before when compared to dropouts. The engaged group had the same or more hospital admissions after the assessment period than before, while the dropout group had fewer. This suggests that there is a significant disorder of engagement for the dual diagnosis populations, and that the first impact of successful treatment is engagement.

Ambulatory Care↗

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Understanding birthing mode decision making using artificial neural networks.

BACKGROUND: This study examined obstetricians' decisions to perform or not to perform cesarean sections. The aim was to determine whether an artificial neural network could be constructed to accurately and reliably predict the birthing mode decisions of expert clinicians and to elucidate which factors were most important in deciding the birth mode. METHODS: Mothers with singleton, live births who were privately insured, nonclinic, non-Medicaid patients at a major tertiary care private hospital were included in the study (N = 1508). These mothers were patients of 2 physician groups: a 7-obstetrician multispecialtygroup practice and a physician group of 79 independently practicing obstetricians affiliated with the same hospital. A feedforward, multilayer artificial neural network (ANN) was developed and trained. It was then tested and optimized until the most parsimonious network was identified that retained a similar level of predictive power and classification accuracy. The performance of this network was further optimized using the methods of receiver operating characteristic (ROC) analysis and information theory to find the cutoff that maximized the information gain. The performance of the final ANN at this cutoff was measured using sensitivity, specificity, classification accuracy, area under the ROC curve, and maximum information gain. RESULTS: The final neural network had excellent predictive accuracy for the birthing mode (classification accuracy = 83.5%; area under the ROC curve = 0.924; maximum information = 40.4% of a perfect diagnostic test). CONCLUSION: This study demonstrated that a properly optimized ANN is able to accurately predict the birthing mode decisions of expert clinicians. In addition to previously identified clinical factors (cephalopelvic disproportion, maternal medical condition necessitating a cesarean section, arrest of labor, malpresentation of the baby, fetal distress, andfailed induction), nonclinical factors such as the mothers' views on birthing mode were also found to be important in determining the birthing mode.

Decision Making↗

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Comparing and optimizing diagnostic tests: an information-theoretical approach.

An ideal method for assessing performance of non-binary diagnostic tests would specify each test's optimal operating point and would tell a diagnostician which of many tests was the best one to use in a particular clinical situation. This article shows how information theory and receiver operating characteristic (ROC) analysis can be combined to evaluate and compare diagnostic tests at their optimum cutoffs once disease prevalence and test properties are specified. Though it is not appropriate for all clinical situations, the method can be used for most diagnostic tests whenever information is desired for its own sake or when reducing uncertainty is the goal of testing. The method also is appropriate in those situations where benefits and costs cannot be specified precisely enough to permit test optimization based on a balancing of anticipated goods and evils.

Diagnostic Tests, Routine↗

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A neural-network approach to predicting admission decisions in a psychiatric emergency room.

Clinical decision making is based on recognizing complex patterns of patients' signs and symptoms. Neural networks have been shown to be very effective at this type of pattern recognition, and in this study a neural-network approach was used to predict which patients seen in a psychiatric emergency room required admission and which did not. Data from all walk-in patients (N = 658) evaluated during normal working hours in a psychiatric emergency room during a one-year period were used either to train a neural network or to test its performance. The network had 53 input nodes, one hidden layer, and an output layer with a single node. The back-propagation method was used to train the network. The neural network's admitting decisions were in substantial agreement with those of the clinicians (kappa coefficient = 0.63). When used as a diagnostic test for admission it had a specificity of 94%, a sensitivity of 70%, and an overall accuracy of 91%. The information gain was 35% of that of a perfect diagnostic test. These results show that a neural network can be trained to make clinical decisions that are in substantial agreement with those of experienced clinicians.

Adolescent↗

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Classifying binormal diagnostic tests using separation-asymmetry diagrams with constant-performance curves.

A method is proposed for classifying diagnostic tests that have underlying binormal distributions. The method involves using the parameters that characterize these distributions as axes of a two-dimensional graph called a separation-asymmetry (S-A) diagram. Each point on an S-A diagram corresponds to a possible diagnostic test. The diagram also has superimposed on it any of three possible families of curves of constant performance: curves of constant area under the ROC graph (iso-AUR), curves of constant overlap area (iso-OA), and curves of constant maximum information (iso-MaxInfo). Thus, the performance of any test can be determined immediately by identifying the iso-performance curve of the desired type that passes through the corresponding point of the S-A diagram. The concept of "eccentric" diagnostic tests is defined and incorporated into the S-A diagrams. The classification scheme is applied to 28 diagnostic tests. Excellent agreement is found in the ranking between the iso-OA and iso-MaxInfo measures of performance, but the iso-AUR produces markedly different results. Only three of the 28 tests were found to be eccentric. Several other interesting patterns emerged.

Bias↗

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Eccentric diagnostic tests: redefining sensitivity and specificity.

Diagnostic tests exist for which the current definitions of sensitivity and specificity are inadequate. Such tests are called eccentric to distinguish them from normal diagnostic tests, for which the standard definitions are satisfactory. It is shown that when the standard definitions are used with eccentric tests there will be an appreciable range of cutoff values over which the test has no discriminating power. This is demonstrated with ROC analysis and information theory. New definitions of sensitivity and specificity that are valid for both normal and eccentric tests are proposed. These definitions require the use of two cutoffs to distinguish test-positive from test-negative individuals. When the new definitions are used with eccentric tests, performance improves significantly and the anomalous regions of zero discriminating power disappear. In the limit of zero eccentricity the new definitions become identical to the old.

Diagnostic Tests, Routine↗