9,633 research outputs found
Generating explanations for complex biomedical queries
Recent advances in health and life sciences have led to generation of a large amount of biomedical data. To facilitate access to its desired parts, such a big mass of data has been represented in structured forms, like databases and ontologies. On the other hand, representing these databases and ontologies in different formats, constructing them independently from each other, and storing them at different locations have brought about many challenges for answering queries about the knowledge represented in these ontologies and databases. One of the challenges for the users is to be able to represent such a biomedical query in a natural language, and get its answers in an understandable form. Another challenge is to extract relevant knowledge from different knowledge resources, and integrate them appropriately using also definitions, such as, chains of gene-gene interactions, cliques of genes based on gene-gene relations, or similarity/diversity of genes/drugs. Furthermore, once an answer is found for a complex query, the experts may need further explanations about the answer. The first two challenges have been addressed earlier using Answer Set Programming (ASP), with the development of a software system (called BIOQUERY-ASP). This thesis addresses the third challenge: explanation generation in ASP. In this thesis, we extend the earlier work on the first two challenges, to new forms of biomedical queries (e.g., about drug similarity) and to new biomedical knowledge resources. We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for queries in ASP, and provide a comprehensive theoretical analysis of these methods. We implement these algorithms and integrate them in BIOQUERY-ASP, and provide an experimental evaluation of our methods with some complex biomedical queries over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASEONTOLOGY and ORPHADATA
A Relation Extraction Approach for Clinical Decision Support
In this paper, we investigate how semantic relations between concepts
extracted from medical documents can be employed to improve the retrieval of
medical literature. Semantic relations explicitly represent relatedness between
concepts and carry high informative power that can be leveraged to improve the
effectiveness of retrieval functionalities of clinical decision support
systems. We present preliminary results and show how relations are able to
provide a sizable increase of the precision for several topics, albeit having
no impact on others. We then discuss some future directions to minimize the
impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th
Conference on Information and Knowledge Management (CIKM), October 22-26
2018, Lingotto, Turin, Ital
Why do These Match? Explaining the Behavior of Image Similarity Models
Explaining a deep learning model can help users understand its behavior and
allow researchers to discern its shortcomings. Recent work has primarily
focused on explaining models for tasks like image classification or visual
question answering. In this paper, we introduce Salient Attributes for Network
Explanation (SANE) to explain image similarity models, where a model's output
is a score measuring the similarity of two inputs rather than a classification
score. In this task, an explanation depends on both of the input images, so
standard methods do not apply. Our SANE explanations pairs a saliency map
identifying important image regions with an attribute that best explains the
match. We find that our explanations provide additional information not
typically captured by saliency maps alone, and can also improve performance on
the classic task of attribute recognition. Our approach's ability to generalize
is demonstrated on two datasets from diverse domains, Polyvore Outfits and
Animals with Attributes 2. Code available at:
https://github.com/VisionLearningGroup/SANEComment: Accepted at ECCV 202
Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening
Socio-technical systems play an important role in public health screening
programs to prevent cancer. Cervical cancer incidence has significantly
decreased in countries that developed systems for organized screening engaging
medical practitioners, laboratories and patients. The system automatically
identifies individuals at risk of developing the disease and invites them for a
screening exam or a follow-up exam conducted by medical professionals. A triage
algorithm in the system aims to reduce unnecessary screening exams for
individuals at low-risk while detecting and treating individuals at high-risk.
Despite the general success of screening, the triage algorithm is a
one-size-fits all approach that is not personalized to a patient. This can
easily be observed in historical data from screening exams. Often patients rely
on personal factors to determine that they are either at high risk or not at
risk at all and take action at their own discretion. Can exploring patient
trajectories help hypothesize personal factors leading to their decisions? We
present Portinari, a data exploration tool to query and visualize future
trajectories of patients who have undergone a specific sequence of screening
exams. The web-based tool contains (a) a visual query interface (b) a backend
graph database of events in patients' lives (c) trajectory visualization using
sankey diagrams. We use Portinari to explore diverse trajectories of patients
following the Norwegian triage algorithm. The trajectories demonstrated
variable degrees of adherence to the triage algorithm and allowed
epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software
Engineering in Society Track. 10 pages, 5 figure
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming
The multi-agent path finding (MAPF) problem is a combinatorial search problem
that aims at finding paths for multiple agents (e.g., robots) in an environment
(e.g., an autonomous warehouse) such that no two agents collide with each
other, and subject to some constraints on the lengths of paths. We consider a
general version of MAPF, called mMAPF, that involves multi-modal transportation
modes (e.g., due to velocity constraints) and consumption of different types of
resources (e.g., batteries). The real-world applications of mMAPF require
flexibility (e.g., solving variations of mMAPF) as well as explainability. Our
earlier studies on mMAPF have focused on the former challenge of flexibility.
In this study, we focus on the latter challenge of explainability, and
introduce a method for generating explanations for queries regarding the
feasibility and optimality of solutions, the nonexistence of solutions, and the
observations about solutions. Our method is based on answer set programming.
This paper is under consideration for acceptance in TPLP.Comment: Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages, 6 figure
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