Biomedical subjects

L Ohno-Machado

Publications and source records attributed to L Ohno-Machado.

44 records · Page 3Linked to original sources

PubMed1995

Hierarchical neural networks for survival analysis.

Neural networks offer the potential of providing more accurate predictions of survival time than do traditional methods. Their use in medical applications has, however, been limited, especially when some data is censored or the frequency of events is low. To reduce the effect of these problems, we have developed a hierarchical architecture of neural networks that predicts survival in a stepwise manner. Predictions are made for the first time interval, then for the second, and so on. The system produces a survival estimate for patients at each interval, given relevant covariates, and is able to handle continuous and discrete variables, as well as censored data. We compared the hierarchical system of neural networks with a nonhierarchical system for a data set of 428 AIDS patients. The hierarchical model predicted survival more accurately than did the nonhierarchical (although both had low sensitivity). The hierarchical model could also learn the same patterns in less than half the time required by the nonhierarchical model. These results suggest that the use of hierarchical systems is advantageous when censored data is present, the number of events is small, and time-dependent variables are necessary.

Acquired Immunodeficiency Syndrome↗

PubMed1994

Identification of low frequency patterns in backpropagation neural networks.

Although neural networks have been widely applied to medical problems in recent years, their applicability has been limited for a variety of reasons. One of these barriers has been the inability to discriminate rare classes of solutions (i.e., the identification of categories that are infrequent). In this article, I demonstrate that a system of hierarchical neural networks (HNN) can overcome the problem of recognizing low frequency patterns, and therefore can improve the prediction power of neural-network systems. HNN are designed according to a divide-and-conquer approach: Triage networks are able to discriminate supersets that contain the infrequent pattern, and these supersets are then used by Specialized networks, which discriminate the infrequent pattern from the other ones in the superset. The supersets that are discriminated by the Triage networks are based on pattern similarity. The application of multilayered neural networks in more than one step allows the prior probability of a given pattern to increase at each step, provided that the predictive power of the network at the previous level is high. The method has been applied to one artificial set and one real set of data. In the artificial set, the distribution of the patterns was known and no noise was present. In this experiment, the HNN provided better discrimination than a standard neural network for all classes. In a real data set of nine thousand patients who were suspected of having thyroid disorders, the HNN also provided higher sensitivity than its corresponding standard neural network (without a corresponding decay in specificity) given the same time constraints.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗

PubMed1993

Prognostic classification for AIDS patients in Brazil.

We studied the survival rates and the prognostic variables that corresponded to death during hospitalization in 312 AIDS CDC group IV patients in São Paulo. Discriminant analysis proved to be a good tool to perform the exploratory data analysis that guided the survival analysis groups. It selected nine variables that were important in the progress of the disease: age, time elapsed from the first manifestations of the disease, gender, infection by helminths, number of risk groups to which the patient belonged, number of infections by fungi, history of transfusion, presence of esophageal candidiasis, and infection by Cryptosporidium sp. Although some of these variables may be of limited importance in developed countries, and some variables that we expected to be important were not present in the final discriminant function, we believe these results may guide future research in prognosis of death in hospitalized AIDS patients.

AIDS-Related Opportunistic Infections↗

PubMed1993

AIDS2: a decision-support tool for decreasing physicians' uncertainty regarding patient eligibility for HIV treatment protocols.

We have developed a decision-support tool, the AIDS Intervention Decision-Support System (AIDS2), to assist in the task of matching patients to therapy-related research protocols. The purposes of AIDS2 are to determine the initial eligibility status of HIV-infected patients for therapy-related research protocols, and to suggest additional data-gathering activities that will decrease uncertainty related to the eligibility status. AIDS2 operates in either a patient-driven or protocol-driven mode. We represent the system knowledge in three combined levels: a classification level, where deterministic knowledge is represented; a belief-network level, where probabilistic knowledge is represented; and a control level, where knowledge about the system's operation is stored. To determine whether the design specifications were met, we presented a series of 10 clinical cases based on actual patients to the system. AIDS2 provided meaningful advice in all cases.

Acquired Immunodeficiency Syndrome↗

PubMedDate not supplied

Neural network applications in physical medicine and rehabilitation.

The purpose of this article is to provide an overview of neural networks and their applications in physical medicine and rehabilitation. Conventional statistical models may present certain limitations that can be overcome by neural networks. We show what neural networks are, how they "learn" regularities from the data, and how they can classify previously unseen cases. We present advantages and disadvantages of using neural networks and compare them with regression models. We explain how neural networks can be used as statistical tools for making inferences using the example of a prognostic model that predicts ambulation after spinal cord injury.

Humans↗

PubMedDate not supplied

The guideline interchange format: a model for representing guidelines.

OBJECTIVE: To allow exchange of clinical practice guidelines among institutions and computer-based applications. DESIGN: The GuideLine Interchange Format (GLIF) specification consists of GLIF model and the GLIF syntax. The GLIF model is an object-oriented representation that consists of a set of classes for guideline entities, attributes for those classes, and data types for the attribute values. The GLIF syntax specifies the format of the test file that contains the encoding. METHODS: Researchers from the InterMed Collaboratory at Columbia University, Harvard University (Brigham and Women's Hospital and Massachusetts General Hospital), and Stanford University analyzed four existing guideline systems to derive a set of requirements for guideline representation. The GLIF specification is a consensus representation developed through a brainstorming process. Four clinical guidelines were encoded in GLIF to assess its expressivity and to study the variability that occurs when two people from different sites encode the same guideline. RESULTS: The encoders reported that GLIF was adequately expressive. A comparison of the encodings revealed substantial variability. CONCLUSION: GLIF was sufficient to model the guidelines for the four conditions that were examined. GLIF needs improvement in standard representation of medical concepts, criterion logic, temporal information, and uncertainty.

Decision Making, Computer-Assisted↗

PubMedDate not supplied

Comparing three-class diagnostic tests by three-way ROC analysis.

Three-way ROC surfaces are based on a generalization of dichotomous ROC analysis to three-class diagnostic tests. The discriminatory power of three-class diagnostic tests is measured by the volume under the ROC surface. This measure can be given a probabilistic interpretation similar to the equivalence of the c-index to the area under the ROC curve. This article presents a method to calculate nonparametric estimates of the variance of the volume under the surface using Mann-Whitney U statistics. As a simple extension of this result, it is possible to calculate covariance estimates for the volume under the surface. This allows the statistical comparison of two tests used for diagnostic tasks with three possible outcomes. The formulas derived are validated on synthetic data and applied to a three-class data set of pigmented skin lesions. It is shown that a neural network algorithm trained on clinical data and lesion features performs better than one trained on only the lesion features.

Humans↗

PubMedDate not supplied

The decision systems group: creating a framework for decision making.

The Decision Systems Group is pursuing a vision in which every available medical resource can be brought together at the point of need. The DSG focuses on the components and tools in specific areas as well as on the models, approaches, and infrastructure that make this type of integration possible. Its academic mission is to train individuals with dual expertise: those who understand and appreciate the issues involved in developing and validating a method or technique, plus the issues of deployment, operation, and interaction in practical environments. Ultimately, the application of this vision can only serve to enhance the current level of healthcare.

Boston↗