Recommending treatments for comorbid patients using word-based and phrase-based alignment methods

The problem of finding treatments for patients diagnosed with multiple diseases (i.e.~a comorbidity) is an important research topic in the medical literature. In this paper, we propose a new data driven approach to recommend treatments for these comorbidities using word-based and phrase-based alignment methods. The most popular methods currently rely on combining specific information from individual diseases (e.g.~procedures, tests, etc.), then aim to detect and repair the conflicts that arise in the combined treatments. This proves to be a challenge especially in the cases where the studied comorbidities contain large numbers of diseases. In contrast, our methods rely on training a translation model using previous medical records to find treatments for newly diagnosed comorbidities. We also explore the use of additional criteria in the form of a drug interactions penalty and a treatment popularity score to select the best treatment in the case where multiple valid translations for a single comorbidity are available

Generating conflict-free treatments for patients with comorbidity using ASP

Conflicts in recommended medical interventions regularly arise when multiple treatments are simultaneously needed for patients with comorbid diseases. An approach that can automatically repair such inconsistencies and generate conflict-free combined treatments is thus a valuable aid for clinicians. In this paper we propose an answer set programming based method that detects and repairs conflicts between treatments. The answer sets of the program directly correspond to proposed treatments, accounting for multiple possible solutions if they exist. We also include the possibility to take preferences based on drug-drug interactions into account while solving inconsistencies. We show in a case study that our method results in more preferred treatments

Repairing inconsistent answer set programs using rules of thumb : a gene regulatory networks case study

Answer set programming is a form of declarative programming that can be used to elegantly model various systems. When the available knowledge about these systems is imperfect, however, the resulting programs can be inconsistent. In such cases, it is of interest to find plausible repairs, i.e. plausible modifications to the original program that ensure the existence of at least one answer set. Although several approaches to this end have already been proposed, most of them merely find a repair which is in some sense minimal. In many applications, however, expert knowledge is available which could allow us to identify better repairs. In particular, we consider the scenario where this expert knowledge is formulated as rules of thumb, but no training data is available to learn how these rules of thumb interact. The main question we address in this paper is whether we can then still aggregate the rules of thumb in a useful way. In addition to standard aggregation techniques, we present a novel statistical approach that assigns weights to these rules of thumb, by sampling, in a particular way, from a pool of possible repairs. In particular, we evaluate how frequently each given rule of thumb is violated in the sample of repairs, and use the Z-score of this distribution to set the weight of that rule. We analyze the potential of using expert knowledge in this way, by focusing on a specific case study: Gene Regulatory Networks. We describe the rules of thumb that express available expert knowledge from the biological literature and explain how they can be encoded while repairing inconsistencies. Finally, we experimentally compare the proposed repair strategies using rules of thumb against the baseline strategy of identifying minimal repairs

Repairing inconsistent taxonomies using MAP inference and rules of thumb

Several authors have developed relation extraction methods for automatically learning or refining taxonomies from large text corpora such as the Web. However, without appropriate post-processing, such taxonomies are often inconsistent (e.g. they contain cycles). A standard approach to repairing such inconsistencies is to identify a minimally consistent subset of the extracted facts. For example, we could aim to minimize the sum of the confidence weights of the facts that are removed for restoring consistency. In this paper, we present MAP inference as a base method for this approach, and analyze how it can be improved by taking into account dependencies between the extracted facts. These dependencies correspond to rules of thumb such as “if a given fact is wrong then all facts that have been extracted from the same sentence are also likely to be wrong", which we encode in Markov logic. We present experimental results to demonstrate the potential of this idea.status: publishe

Repairing inconsistent taxonomies using MAP inference and rules of thumb

Several authors have developed relation extraction methods for automatically learning or refining taxonomies from large text corpora such as the Web. However, without appropriate post-processing, such taxonomies are often inconsistent (e.g. they contain cycles). A standard approach to repairing such inconsistencies is to identify a minimally consistent subset of the extracted facts. For example, we could aim to minimize the sum of the confidence weights of the facts that are removed for restoring consistency. In this paper, we present MAP inference as a base method for this approach, and analyze how it can be improved by taking into account dependencies between the extracted facts. These dependencies correspond to rules of thumb such as “if a given fact is wrong then all facts that have been extracted from the same sentence are also likely to be wrong", which we encode in Markov logic. We present experimental results to demonstrate the potential of this idea