Decision Support for Intoxication Prediction Using Graph Convolutional Networks

Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame. Usually the toxin is known and recommendations can be made accordingly. However, in challenging cases only symptoms are mentioned and doctors have to rely on their clinical experience. Medical experts and our analyses of a regional dataset of intoxication records provide evidence that this is challenging, since occurring symptoms may not always match the textbook description due to regional distinctions, inter-rater variance, and institutional workflow. Computer-aided diagnosis (CADx) can provide decision support, but approaches so far do not consider additional information of the reported cases like age or gender, despite their potential value towards a correct diagnosis. In this work, we propose a new machine learning based CADx method which fuses symptoms and meta information of the patients using graph convolutional networks. We further propose a novel symptom matching method that allows the effective incorporation of prior knowledge into the learning process and evidently stabilizes the poison prediction. We validate our method against 10 medical doctors with different experience diagnosing intoxication cases for 10 different toxins from the PCC in Munich and show our method's superiority in performance for poison prediction.Comment: 10 pages, 3 figure

Marijuana Intoxication Detection Using Convolutional Neural Network

Machine learning is a broad study of computer science, widely used for data analysis and algorithms that has the ability to learn and improve by experience through training. Supervised learning, Unsupervised learning, Dimensionality Reduction, Deep Learning, etc are the methods offered by Machine learning. These techniques are applied in fields like medical, automotive finance, and many more. In this thesis, Convolutional neural network (CNN) which is a part of deep learning techniques is applied to identify if a person is under influence of Marijuana or sober, using facial feature changes like redness in eyes, watery eyes, and drowsiness caused after smoking Marijuana. CNN is a state-of-the-art method in tasks like image classification and pattern recognition. CNN’s ability to learn from training the model using image dataset is a suitable method to be used in the problem of identifying a person’s sobriety based on facial features. The proposed methodology is divided into three components. Which are dataset creation, face detection to extract input image from real-time video, and finally, tuning and training CNN model for making a prediction. The purpose of this thesis is to develop a CNN model that may be helpful if implemented in vehicles in the future to reduce impaired driving incidents. Impaired driving is a major criminal cause of vehicle accidents in Canada. Impaired driving is a serious problem that puts the lives of pedestrians on the road and drivers involved in impaired driving themselves in danger. This thesis presents how Machine Learning can be applied to predict driver’s sobriety that may be helpful in reducing impaired driving incidents in the future by implementing in vehicles

A review of recent machine learning advances for forecasting harmful Algal Blooms and shellfish contamination

Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.CEECINST/00102/2018/ UIDB/04516/2020/ UIDB/00297/2020/ UIDB/50021/2020/ UID/Multi/04326/2020info:eu-repo/semantics/publishedVersio

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Guided interactive image segmentation using machine learning and color based data set clustering

We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larger numbers of images thereby enabling interactive image analysis for recent new technological advances in high-throughput imaging. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general

Deep Neural Architectures for End-to-End Relation Extraction

The rapid pace of scientific and technological advancements has led to a meteoric growth in knowledge, as evidenced by a sharp increase in the number of scholarly publications in recent years. PubMed, for example, archives more than 30 million biomedical articles across various domains and covers a wide range of topics including medicine, pharmacy, biology, and healthcare. Social media and digital journalism have similarly experienced their own accelerated growth in the age of big data. Hence, there is a compelling need for ways to organize and distill the vast, fragmented body of information (often unstructured in the form of natural human language) so that it can be assimilated, reasoned about, and ultimately harnessed. Relation extraction is an important natural language task toward that end. In relation extraction, semantic relationships are extracted from natural human language in the form of (subject, object, predicate) triples such that subject and object are mentions of discrete concepts and predicate indicates the type of relation between them. The difficulty of relation extraction becomes clear when we consider the myriad of ways the same relation can be expressed in natural language. Much of the current works in relation extraction assume that entities are known at extraction time, thus treating entity recognition as an entirely separate and independent task. However, recent studies have shown that entity recognition and relation extraction, when modeled together as interdependent tasks, can lead to overall improvements in extraction accuracy. When modeled in such a manner, the task is referred to as end-to-end relation extraction. In this work, we present four studies that introduce incrementally sophisticated architectures designed to tackle the task of end-to-end relation extraction. In the first study, we present a pipeline approach for extracting protein-protein interactions as affected by particular mutations. The pipeline system makes use of recurrent neural networks for protein detection, lexicons for gene normalization, and convolutional neural networks for relation extraction. In the second study, we show that a multi-task learning framework, with parameter sharing, can achieve state-of-the-art results for drug-drug interaction extraction. At its core, the model uses graph convolutions, with a novel attention-gating mechanism, over dependency parse trees. In the third study, we present a more efficient and general-purpose end-to-end neural architecture designed around the idea of the table-filling paradigm; for an input sentence of length n, all entities and relations are extracted in a single pass of the network in an indirect fashion by populating the cells of a corresponding n by n table using metric-based features. We show that this approach excels in both the general English and biomedical domains with extraction times that are up to an order of magnitude faster compared to the prior best. In the fourth and last study, we present an architecture for relation extraction that, in addition to being end-to-end, is able to handle cross-sentence and N-ary relations. Overall, our work contributes to the advancement of modern information extraction by exploring end-to-end solutions that are fast, accurate, and generalizable to many high-value domains

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.Comment: 24 Pages, 8 Tables, 6 Figures; Accepted by PLoS One ; One of the two mentioned Datasets in the manuscript has Closed Access. We will make it public after PLoS One produces the manuscrip

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments