scholarly journals Characterizing financial markets from the event driven perspective

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Miha Torkar ◽  
Dunja Mladenic
Keyword(s):  
Financial Markets ◽  
Point Processes ◽  
Feature Vector ◽  
Learning Models ◽  
Textual Data ◽  
Future Events ◽  
News Events ◽  
Improved Performance ◽  
News Event ◽  
Machine Learning Models

AbstractIn this work we study how company co-occurrence in news events can be used to discover business links between them. We develop a methodology that is able to process raw textual data, embed it into a numerical form, and extract a meaningful network of connections. Each news event is considered as a node on the graph and we define the similarity between the two events as the cosine similarity between their vectors in the embedded space. Using this procedure, we contribute to the literature by successfully reconstructing business links between companies, which is usually a difficult task since the data on this topic is either outdated, incomplete or not widely available. We then demonstrate possible uses of this network in two forecasting applications. First, we show how the network can be used as an exogenous feature vector, which improves the prediction of the correlation between companies in the network. This correlation is determined from their realized variance as well as using a wide set of machine learning models for prediction. Second, we demonstrate the use of network for predicting future events with point processes. Our methodology can be applied on any series of events, where we have demonstrated and evaluated its applicability on news events and large market moves. For most of the tested algorithms the experimental results show an improvement in performance when including information from our graphs. More specifically, in certain sectors using Neural Networks shows improved performance by up to 50%.

scholarly journals Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

2020 ◽  
Author(s):  
Gregory Kiar ◽  
Yohan Chatelain ◽  
Ali Salari ◽  
Alan C. Evans ◽  
Tristan Glatard
Keyword(s):  
Monte Carlo ◽  
Data Augmentation ◽  
Learning Models ◽  
Reduction Techniques ◽  
Numerical Noise ◽  
Numerical Instabilities ◽  
Dimensionality Reduction Techniques ◽  
Improved Performance ◽  
Low Dimensional ◽  
Machine Learning Models

AbstractMachine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.

scholarly journals The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance (Preprint)

2019 ◽  
Author(s):  
José Tomás Prieto ◽  
Kenneth Scott ◽  
Dean McEwen ◽  
Laura J Podewils ◽  
Alia Al-Tayyib ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Receiver Operating Curve ◽  
Reference Standard ◽  
Opioid Misuse ◽  
Learning Models ◽  
Heroin Use ◽  
Naloxone Administration ◽  
Improved Performance ◽  
Machine Learning Models

BACKGROUND Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. OBJECTIVE This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. METHODS First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. RESULTS In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). CONCLUSIONS A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities.

scholarly journals On classifying sepsis heterogeneity in the ICU: insight using machine learning

2020 ◽  
Vol 27 (3) ◽  
pp. 437-443 ◽  
Author(s):  
Zina M Ibrahim ◽  
Honghan Wu ◽  
Ahmed Hamoud ◽  
Lukas Stappen ◽  
Richard J B Dobson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organ Dysfunction ◽  
Added Value ◽  
Classification Model ◽  
Superior Performance ◽  
Support Vector ◽  
Learning Models ◽  
Patients At Risk ◽  
Improved Performance ◽  
Machine Learning Models

Abstract Objectives Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data. Materials and Methods Using an ICU dataset of 13 728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. We perform classification experiments with random forest, gradient boost trees, and support vector machines, using the identified subpopulations to distinguish patients who develop sepsis in the ICU from those who do not. Results The classification results show that features selected using sepsis subpopulations as background knowledge yield a superior performance in distinguishing septic from non-septic patients regardless of the classification model used. The improved performance is especially pronounced in specificity, which is a current bottleneck in sepsis prediction machine learning models. Conclusion Our findings can steer machine learning efforts toward more personalized models for complex conditions including sepsis.

scholarly journals Learning to Predict from Textual Data

2012 ◽  
Vol 45 ◽  
pp. 641-684 ◽  
Author(s):  
K. Radinsky ◽  
S. Davidovich ◽  
S. Markovitch
Keyword(s):  
Machine Learning ◽  
Empirical Evaluation ◽  
Language Modeling ◽  
World Knowledge ◽  
Vast Number ◽  
Textual Data ◽  
Modeling Techniques ◽  
Future Events ◽  
News Event ◽  
A Current

Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans.

scholarly journals The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance

10.2196/15645 ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. e15645 ◽  
Author(s):  
José Tomás Prieto ◽  
Kenneth Scott ◽  
Dean McEwen ◽  
Laura J Podewils ◽  
Alia Al-Tayyib ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Receiver Operating Curve ◽  
Reference Standard ◽  
Opioid Misuse ◽  
Learning Models ◽  
Heroin Use ◽  
Naloxone Administration ◽  
Improved Performance ◽  
Machine Learning Models

Background Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. Objective This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. Methods First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. Results In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). Conclusions A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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