726 research outputs found
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
Tacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug
User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation
Kan bare brukes i forskningssammenheng, ikke kommersielt. Les mer her: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsBuilding predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.acceptedVersio
Video Analytics in Elite Soccer: A Distributed Computing Perspective
Ubiquitous sensors and Internet of Things (IoT)technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post-game. New methods, including machine learning, image, and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA’s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing, and its importance in video analytics and propose a future research perspective.acceptedVersio
Fishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishing
Detection of anomalies within data streams is an
important task that is useful for different important societal
challenges such as in traffic control and fraud detection. To
be able to perform anomaly detection, unsupervised analysis of
data is an important key factor, especially in domains where
obtaining labelled data is difficult or where the anomalies that
should be detected are often changing or are not clearly definable
at all. In this article, we present a complete machine learning
based pipeline for real-time unsupervised anomaly detection
that can handle different input data streams simultaneously. We
evaluate the usefulness of the proposed method using three wellknown datasets (fall detection, crime detection, and sport event
detection) and a completely new and unlabelled dataset within
the domain of commercial fishing. For all datasets, our method
outperforms the baselines significantly and is able to detect
relevant anomalies while simultaneously having low numbers of
false positives. In addition to the good detection performance,
the presented system can operate in real-time and is also very
flexible and easy to expand
File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments
In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features
Enhancing questioning skills through child avatar chatbot training with feedback
Training child investigative interviewing skills is a specialized task. Those being
trained need opportunities to practice their skills in realistic settings and receive
immediate feedback. A key step in ensuring the availability of such opportunities
is to develop a dynamic, conversational avatar, using artificial intelligence (AI)
technology that can provide implicit and explicit feedback to trainees. In the
iterative process, use of a chatbot avatar to test the language and conversation
model is crucial. The model is fine-tuned with interview data and realistic
scenarios. This study used a pre-post training design to assess the learning
effects on questioning skills across four child interview sessions that involved
training with a child avatar chatbot fine-tuned with interview data and realistic
scenarios. Thirty university students from the areas of child welfare, social
work, and psychology were divided into two groups; one group received direct
feedback (n = 12), whereas the other received no feedback (n = 18). An automatic
coding function in the language model identified the question types. Information
on question types was provided as feedback in the direct feedback group only.
The scenario included a 6-year-old girl being interviewed about alleged physical
abuse. After the first interview session (baseline), all participants watched a video
lecture on memory, witness psychology, and questioning before they conducted
two additional interview sessions and completed a post-experience survey. One
week later, they conducted a fourth interview and completed another postexperience survey. All chatbot transcripts were coded for interview quality. The
language model’s automatic feedback function was found to be highly reliable
in classifying question types, reflecting the substantial agreement among the
raters [Cohen’s kappa (κ) = 0.80] in coding open-ended, cued recall, and closed
questions. Participants who received direct feedback showed a significantly
higher improvement in open-ended questioning than those in the non-feedback
group, with a significant increase in the number of open-ended questions used
between the baseline and each of the other three chat sessions. This study
demonstrates that child avatar chatbot training improves interview quality with
regard to recommended questioning, especially when combined with direct
feedback on questioning
Njord: a fishing trawler dataset
Fish is one of the main sources of food worldwide. The commercial
fishing industry has a lot of different aspects to consider, ranging
from sustainability to reporting. The complexity of the domain also
attracts a lot of research from different fields like marine biology,
fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and
classification of fish from images or videos using machine learning
or other analysis methods attracts growing attention. Surprisingly,
little work has been done that considers what is happening on
board the fishing vessels. On the deck of the boats, a lot of data and
important information are generated with potential applications,
such as automatic detection of accidents or automatic reporting of
fish caught. This paper presents Njord, a fishing trawler dataset
consisting of surveillance videos from a modern off-shore fishing
trawler at sea. The main goal of this dataset is to show the potential
and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several
possible research questions this dataset could help answer
Artificial intelligence in dry eye disease
Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation
Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data
For the LMRT task at ImageCLEFlifelog 2020, LIFER 3.0, a new version of the LIFER system with improvements in the user interface and system affordance, is used and evaluated via feedback from a user experiment. In addition, since both tasks share a common dataset, LIFER 3.0 borrows some features from the LifeSeeker system deployed for the Lifelog Search Challenge; which are free-text search, visual similarity search and elastic sequencing filter. For the SPLL task, we proposed a naive solution by capturing the rate of change in running speed and weight, then obtain the target changes for each subtask using average computation and linear regression model. The results presented in this paper can be used as comparative baselines for other participants in the ImageCLEFlifelog 2020 challenge.publishedVersio
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