A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data

BACKGROUND: We consider the user task of designing clinical trial protocols and propose a method that discovers and outputs the most appropriate eligibility criteria from a potentially huge set of candidates. Each document d in our collection D is a clinical trial protocol which itself contains a set of eligibility criteria. Given a small set of sample documents [Formula: see text] , a user has initially identified as relevant e.g., via a user query interface, our scoring method automatically suggests eligibility criteria from D, D ⊃ D', by ranking them according to how appropriate they are to the clinical trial protocol currently being designed. The appropriateness is measured by the degree to which they are consistent with the user-supplied sample documents D'. METHOD: We propose a novel three-step method called LDALR which views documents as a mixture of latent topics. First, we infer the latent topics in the sample documents using Latent Dirichlet Allocation (LDA). Next, we use logistic regression models to compute the probability that a given candidate criterion belongs to a particular topic. Lastly, we score each criterion by computing its expected value, the probability-weighted sum of the topic proportions inferred from the set of sample documents. Intuitively, the greater the probability that a candidate criterion belongs to the topics that are dominant in the samples, the higher its expected value or score. RESULTS: Our experiments have shown that LDALR is 8 and 9 times better (resp., for inclusion and exclusion criteria) than randomly choosing from a set of candidates obtained from relevant documents. In user simulation experiments using LDALR, we were able to automatically construct eligibility criteria that are on the average 75% and 70% (resp., for inclusion and exclusion criteria) similar to the correct eligibility criteria. CONCLUSIONS: We have proposed LDALR, a practical method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data. Results from our experiments suggest that LDALR models can be used to effectively find appropriate eligibility criteria from a large repository of clinical trial protocols

Exploiting monotonicity via logistic regression in Bayesian network learning

An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting background knowledge. In this report, we present a theoretical analysis on the use of constrained logistic regression for estimating conditional probability distribution in Bayesian Networks (BN) by using background knowledge in the form of qualitative monotonicity statements. Such background knowledge is treated as a set of constraints on the parameters of a logistic function during training. Our goal of finding the appropriate BN model is two-fold: (a) we want to exploit any monotonic relationship between random variables that may generally exist as domain knowledge and (b) we want to be able to address the problem of estimating the conditional distribution of a random variable with a large number of parents. We discuss variants of the logistic regression model and present an analysis on the corresponding constraints required to implement monotonicity. More importantly, we outline the problem in some of these variants in terms of the number of parameters and constraints which, in some cases, can grow exponentially with the number of parent variables. To address this problem, we present two variants of the constrained logistic regression model, M[superscipt 2b][subscript CLR] and M³[subscript CLR], in which the number of constraints required to implement monotonicity does not grow exponentially with the number of parents hence providing a practicable method for estimating conditional probabilities with very sparse data.Keywords: logistic regression, Bayesian network learning, monotonicit

An Update Procedure for A Probabilistic Deductive Database

A sound and complete view update procedure for a probabilistic deductive database is formulated using SLDp derivation trees introduced by Ng and Subrahmanian in [8]. In order to reduce the number of valid translations that can satisfy an update request a preference criteria is proposed. Moreover, we introduce a method called Δ-factor to minimize the change effected by updates in the database

Inferring implicit preferences . . .

In this paper we propose to model a negotiator’s decision-making behavior, expressed as preferences between an offer/counter-offer gamble and a certain offer, by learning from implicit choices that can be inferred from observed negotiation actions. The agent’s actions in a negotiation sequence provide information about his preferences and risk-taking behavior. We show how offers and counter-offers in negotiation can be transformed into gamble questions providing a basis for inferring implicit preferences. Finally, we present the results of experiments and evaluation we have undertaken

Eliciting Utilities by Refining Theories of Monotonicity and Risk

Interest in such diverse problems as development of useradaptive software and greater involvement of patients in medical treatment decisions has increased interest in development of automated preference elicitation tools. A design challenge of these tools is to elicit reliable information while not overly fatiguing the interviewee. We address this problem by using domain background knowledge in a flexible manner. In particular, we use knowledge-based artificial neural networks to encode assumptions about a decision maker's preferences

ARGUER: Using Argument Schemas for Argument Detection and Rebuttal in Dialogs

This paper presents a computational method for argumentation on the basis of a declarative characterizatio