Biomedical subjects

C F Aliferis

Publications and source records attributed to C F Aliferis.

9 recordsLinked to original sources

PubMed2002

Machine learning models for lung cancer classification using array comparative genomic hybridization.

Array CGH is a recently introduced technology that measures changes in the gene copy number of hundreds of genes in a single experiment. The primary goal of this study was to develop machine learning models that classify non-small Lung Cancers according to histopathology types and to compare several machine learning methods in this learning task. DNA from tumors of 37 patients (21 squamous carcinomas, and 16 adenocarcinomas) were extracted and hybridized onto a 452 BAC clone array. The following algorithms were used: KNN, Decision Tree Induction, Support Vector Machines and Feed-Forward Neural Networks. Performance was measured via leave-one-out classification accuracy. The best multi-gene model found had a leave-one-out accuracy of 89.2%. Decision Trees performed poorer than the other methods in this learning task and dataset. We conclude that gene copy numbers as measured by array CGH are, collectively, an excellent indicator of histological subtype. Several interesting research directions are discussed.

Algorithms↗

PubMed1998

Temporal representation design principles: an assessment in the domain of liver transplantation.

Time modeling is an important aspect of medical decision-support systems engineering. At the core of effective time modeling lies the challenge of proper knowledge representation design. In this paper, we focus on two important principles for effective time-modeling languages: (a) hybrid temporal representation, and (b) dynamic temporal abstraction. To explore the significance of these design principles, we extend a previously-defined formalism (single-granularity modifiable temporal belief networks--MTBN-SGs) to accommodate multiple temporal granularities and dynamic query and domain-specific model creation. We call the new formalism multiple-granularity MTBNs (MTBN-MGs). We develop a prototype system for modeling aspects of liver transplantation and analyze the resulting model with respect to its representation power, representational tractability, and inferential tractability. Our experiment demonstrates that the design of formalisms is crucial for effective time modeling. In particular: (i) Hybrid temporal representation is a desirable property of time-modeling languages because it makes knowledge acquisition easier, and increases representational tractability. (ii) Dynamic temporal abstraction improves inferential and representational tractability significantly. We discuss a high-level procedure for extending existing languages to incorporate hybrid temporal representation and dynamic temporal abstraction.

Computer Simulation↗

PubMed1997

Representing and developing temporally abstracted knowledge as a means towards facilitating time modeling in medical decision-support systems.

The utilization of the appropriate level of temporal abstraction is an important aspect of time modeling. We discuss some aspects of the relation of temporal abstraction to important knowledge engineering parameters such as model correctness, ease of model specification, knowledge availability, query completeness, inference tractability, and semantic clarity. We propose that versatile and efficient time-modeling formalisms should encompass ways to represent and reason at more than one level of abstraction, and we discuss such a hybrid formalism. Although many research efforts have concentrated on the automation of specific temporal abstractions, much research needs to be done in understanding and developing provably optimal abstractions. We provide an initial framework for studying this problem in a manner that is independent of the particular problem domain and knowledge representation, and suggest several research challenges that appear worth pursuing.

Artificial Intelligence↗

PubMed1997

An evaluation of machine-learning methods for predicting pneumonia mortality.

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model's potential to assist a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each model's predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30% of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.

Artificial Intelligence↗

PubMed1995

A new formalism for temporal modeling in medical decision-support systems.

We present a new mathematical formalism, which we call modifiable temporal belief networks (MTBNs) that extends the concept of an ordinary belief network (BN) to incorporate a dynamic causal structure and explicit temporal semantics. An important feature of MTBNs is that they allow portions of the model to be abstract and portions of it to be temporally explicit. We show how this property can lead to substantial knowledge acquisition and computational complexity savings. In addition to temporal modeling, the language of MTBNs can be an important analytical tool, as well as temporal language for causal discovery.

Decision Making, Computer-Assisted↗

PubMed1995

Temporal reasoning abstractions in QMR.

A medical decision-support system (MDSS) employs either explicit or implicit temporal representation and reasoning (TRR). In this paper we first examine the factors that make explicit TRR necessary. We argue that for diagnostic MDSSs in large domains, such as internal medicine, implicit TRR is often sufficient for acceptable diagnostic performance. A necessary prerequisite for implementing implicit TRR is the identification of a set of proper TRR abstractions. We analyze the implicit TRR utilized in QMR, a MDSS operating in the domain of general internal medicine, and describe three classes of TRR abstractions. We discuss our findings in relation to work on temporal reasoning in medical informatics.

Animals↗

PubMed1995

On the heuristic nature of medical decision-support systems.

In the realm of medical decision-support systems, the term "heuristic systems" is often considered to be synonymous with "medical artificial intelligence systems" or with "systems employing informal model(s) of problem solving". Such a view may be inaccurate and possibly impede the conceptual development of future systems. This article examines the nature of heuristics and the levels at which heuristic solutions are introduced during system design and implementation. The authors discuss why heuristics are ubiquitous in all medical decision-support systems operating at non-trivial domains, and propose a unifying definition of heuristics that encompasses formal and ad hoc systems. System developers should be aware of the heuristic nature of all problem solving done in complex real world domains, and characterize their own use of heuristics in describing system development and implementation.

Artificial Intelligence↗

PubMed1994

A temporal analysis of QMR: abstracted temporal representation and reasoning and initial assessment of diagnostic performance trade-offs.

Explicit temporal representation and reasoning (TRR) in medical decision-support systems (MDSS) is generally considered to be a useful but often neglected aspect of system design and implementation. Given the great burden of explicit TRR both in knowledge acquisition and computational efficiency, developers of general-purpose large-scale systems typically utilize implicit (i.e., abstracted) forms of TRR. We are interested in understanding better the trade-offs of not incorporating explicit TRR in large general-purpose MDSS along the dimensions of system expressive power and diagnostic accuracy. In particular, we examine the types of abstracted TRR employed in QMR, a diagnostic system in the domain of general internal medicine, and the high-level effects of such an implicit treatment of time in the system's diagnostic performance. We present our findings and discuss implications for MDSS design and implementation practices.

Artificial Intelligence↗

PubMedDate not supplied

A temporal analysis of QMR.

OBJECTIVE: To understand better the trade-offs of not incorporating explicit time in Quick Medical Reference (QMR), a diagnostic system in the domain of general internal medicine, along the dimensions of expressive power and diagnostic accuracy. DESIGN: The study was conducted in two phases. Phase I was a descriptive analysis of the temporal abstractions incorporated in QMR's terms. Phase II was a pseudo-prospective controlled experiment, measuring the effect of history and physical examination temporal content on the diagnostic accuracy of QMR. MEASUREMENTS: For each QMR finding that would fit our operational definition of temporal finding, several parameters describing the temporal nature of the finding were assessed, the most important ones being: temporal primitives, time units, temporal uncertainty, processes, and patterns. The history, physical examination, and initial laboratory results of 105 consecutive patients admitted to the Pittsburgh University Presbyterian Hospital were analyzed for temporal content and factors that could potentially influence diagnostic accuracy (these included: rareness of primary diagnosis, case length, uncertainty, spatial/causal information, and multiple diseases). RESULTS: 776 findings were identified as temporal. The authors developed an ontology describing the terms utilized by QMR developers to express temporal knowledge. The authors classified the temporal abstractions found in QMR in 116 temporal types, 11 temporal templates, and a temporal hierarchy. The odds of QMR's making a correct diagnosis in high temporal complexity cases is 0.7 the odds when the temporal complexity is lower, but this result is not statistically significant (95% confidence interval = 0.27-1.83). CONCLUSIONS: QMR contains extensive implicit time modeling. These results support the conclusion that the abstracted encoding of time in the medical knowledge of QMR does not induce a diagnostic performance penalty.

Artificial Intelligence↗