172,965 research outputs found
Sparse Predictive Modeling : A Cost-Effective Perspective
Many real life problems encountered in industry, economics or engineering are complex and difficult to model by conventional mathematical methods. Machine learning provides a wide variety of methods and tools for solving such problems by learning mathematical models from data. Methods from the field have found their way to applications such as medical diagnosis, financial forecasting, and web-search engines. The predictions made by a learned model are based on a vector of feature values describing the input to the model. However, predictions do not come for free in real world applications, since the feature values of the input have to be bought, measured or produced before the model can be used. Feature selection is a process of eliminating irrelevant and redundant features from the model. Traditionally, it has been applied for achieving interpretable and more accurate models, while the possibility of lowering prediction costs has received much less attention in the literature.
In this thesis we consider novel feature selection techniques for reducing prediction costs. The contributions of this thesis are as follows. First, we propose several cost types characterizing the cost of performing prediction with a trained model. Particularly, we consider costs emerging from multitarget prediction problems as well as a number of cost types arising when the feature extraction process is structured. Second, we develop greedy regularized least-squares methods to maximize the predictive performance of the models under given budget constraints. Empirical evaluations are performed on numerous benchmark data sets as well as on a novel water quality analysis application. The results demonstrate that in settings where the considered cost types apply, the proposed methods lead to substantial cost savings compared to conventional methods
On the Generation of Medical Question-Answer Pairs
Question answering (QA) has achieved promising progress recently. However,
answering a question in real-world scenarios like the medical domain is still
challenging, due to the requirement of external knowledge and the insufficient
quantity of high-quality training data. In the light of these challenges, we
study the task of generating medical QA pairs in this paper. With the insight
that each medical question can be considered as a sample from the latent
distribution of questions given answers, we propose an automated medical QA
pair generation framework, consisting of an unsupervised key phrase detector
that explores unstructured material for validity, and a generator that involves
a multi-pass decoder to integrate structural knowledge for diversity. A series
of experiments have been conducted on a real-world dataset collected from the
National Medical Licensing Examination of China. Both automatic evaluation and
human annotation demonstrate the effectiveness of the proposed method. Further
investigation shows that, by incorporating the generated QA pairs for training,
significant improvement in terms of accuracy can be achieved for the
examination QA system.Comment: AAAI 202
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
Physiology-Aware Rural Ambulance Routing
In emergency patient transport from rural medical facility to center tertiary
hospital, real-time monitoring of the patient in the ambulance by a physician
expert at the tertiary center is crucial. While telemetry healthcare services
using mobile networks may enable remote real-time monitoring of transported
patients, physiologic measures and tracking are at least as important and
requires the existence of high-fidelity communication coverage. However, the
wireless networks along the roads especially in rural areas can range from 4G
to low-speed 2G, some parts with communication breakage. From a patient care
perspective, transport during critical illness can make route selection patient
state dependent. Prompt decisions with the relative advantage of a longer more
secure bandwidth route versus a shorter, more rapid transport route but with
less secure bandwidth must be made. The trade-off between route selection and
the quality of wireless communication is an important optimization problem
which unfortunately has remained unaddressed by prior work.
In this paper, we propose a novel physiology-aware route scheduling approach
for emergency ambulance transport of rural patients with acute, high risk
diseases in need of continuous remote monitoring. We mathematically model the
problem into an NP-hard graph theory problem, and approximate a solution based
on a trade-off between communication coverage and shortest path. We profile
communication along two major routes in a large rural hospital settings in
Illinois, and use the traces to manifest the concept. Further, we design our
algorithms and run preliminary experiments for scalability analysis. We believe
that our scheduling techniques can become a compelling aid that enables an
always-connected remote monitoring system in emergency patient transfer
scenarios aimed to prevent morbidity and mortality with early diagnosis
treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare
Informatics (ICHI 2017), Park City, Utah, 201
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