36,319 research outputs found
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Beyond current conversational chatbots or task-oriented dialogue systems that
have attracted increasing attention, we move forward to develop a dialogue
system for automatic medical diagnosis that converses with patients to collect
additional symptoms beyond their self-reports and automatically makes a
diagnosis. Besides the challenges for conversational dialogue systems (e.g.
topic transition coherency and question understanding), automatic medical
diagnosis further poses more critical requirements for the dialogue rationality
in the context of medical knowledge and symptom-disease relations. Existing
dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017)
mostly rely on data-driven learning and cannot be able to encode extra expert
knowledge graph. In this work, we propose an End-to-End Knowledge-routed
Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical
knowledge graph into the topic transition in dialogue management, and makes it
cooperative with natural language understanding and natural language
generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to
manage topic transitions, which integrates a relational refinement branch for
encoding relations among different symptoms and symptom-disease pairs, and a
knowledge-routed graph branch for topic decision-making. Extensive experiments
on a public medical dialogue dataset show our KR-DS significantly beats
state-of-the-art methods (by more than 8% in diagnosis accuracy). We further
show the superiority of our KR-DS on a newly collected medical dialogue system
dataset, which is more challenging retaining original self-reports and
conversational data between patients and doctors.Comment: 8 pages, 5 figues, AAA
Multi-Agent Deep Reinforcement Learning with Human Strategies
Deep learning has enabled traditional reinforcement learning methods to deal
with high-dimensional problems. However, one of the disadvantages of deep
reinforcement learning methods is the limited exploration capacity of learning
agents. In this paper, we introduce an approach that integrates human
strategies to increase the exploration capacity of multiple deep reinforcement
learning agents. We also report the development of our own multi-agent
environment called Multiple Tank Defence to simulate the proposed approach. The
results show the significant performance improvement of multiple agents that
have learned cooperatively with human strategies. This implies that there is a
critical need for human intellect teamed with machines to solve complex
problems. In addition, the success of this simulation indicates that our
multi-agent environment can be used as a testbed platform to develop and
validate other multi-agent control algorithms.Comment: 2019 IEEE International Conference on Industrial Technology (ICIT),
Melbourne, Australi
A survey on utilization of data mining approaches for dermatological (skin) diseases prediction
Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data
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