scholarly journals Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of the Patient Voice (Preprint)

2020 ◽  
Author(s):  
Paul J Barr ◽  
James Ryan ◽  
Nicholas C Jacobson
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Large Scale ◽  
Clinical Phenotype ◽  
Low Cost ◽  
Video Recording ◽  
Severe Infection ◽  
Patient Characteristics ◽  
Clinic Visit ◽  
Clinic Visits

UNSTRUCTURED The novel coronavirus (SARS-CoV-2) and its related disease, COVID-19, are exponentially increasing across the world, yet there is still uncertainty about the clinical phenotype. Natural Language Processing (NLP) and machine learning may hold one key to quickly identify individuals at high risk for COVID-19 and understand key symptoms in its clinical manifestation and presentation. In healthcare, such data often come the medical record, yet when overburdened, clinicians may focus on documenting widely reported symptoms that appear to confirm the diagnosis of COVID-19, at the expense of infrequently reported symptoms. A comprehensive record of the clinic visit is required—an audio recording may be the answer. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, quickly creating a clinical phenotype of COVID-19. We propose the creation of a pipeline from the audio/video recording of clinic visits to the clinical symptomatology model and prediction of COVID-19 infection. With vast amounts of data available, we believe a prediction model can be quickly developed that could promote the accurate screening of individuals at risk of COVID-19 and identify patient characteristics predicting a greater risk of a more severe infection. If clinical encounters are recorded and our NLP is adequately refined, then benchtop-virology will be better informed and risk of spread reduced. While recordings of clinic visits are not the panacea to this pandemic, they are a low cost option with many potential benefits that have only just begun to be explored.

scholarly journals Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice

10.2196/20545 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e20545
Author(s):  
Paul J Barr ◽  
James Ryan ◽  
Nicholas C Jacobson
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Language Processing ◽  
Large Scale ◽  
Clinical Symptoms ◽  
Clinical Phenotype ◽  
Clinic Visit ◽  
Patient Reported ◽  
Audio Recordings ◽  
Clinic Visits

COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required—audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

Autistic Language Processing by Patterns Detection

2018 ◽  
Vol 8 (1) ◽  
pp. 36-61
Author(s):  
Daniela Lopez De Luise ◽  
Ben Raul Saad ◽  
Pablo D Pescio ◽  
Christian Martin Saliwonczyk
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Verbal Behavior ◽  
Language Processing ◽  
Video Recording ◽  
Individual Performance ◽  
Neural Nets ◽  
Autistic Spectrum ◽  
Spectrum Disorders ◽  
Leo Kanner

The main goal of this article is to present an approach that allows the automatic management of autistic communication patterns by processing audio and video from the therapy session of individuals suffering autistic spectrum disorders (ASD). Such patients usually have social and communication alterations that make it difficult to evaluate the meaning of those expressions. As their communicational skills may have different degrees of variation, it is very hard to understand the semantics behind the verbal behavior. The current work is based on previous work on machine learning for individual performance evaluation. Statistics show that autistic verbal behavior are physically expressed by repetitive sounds and related movements that are evident and stereotyped. The works of Leo Kanner and Ángel Riviere are also considered here. Using machine learning and neural nets with certain set of parameters, it is possible to automatically detect patterns in audio and video recording of patient's performance, which is an interesting opportunity to communicate with ASD patients.

Δ-Quantum machine learning for medicinal chemistry

2021 ◽  
Author(s):  
Kenneth Atz ◽  
Clemens Isert ◽  
Markus N. A. Böcker ◽  
José Jiménez-Luna ◽  
Gisbert Schneider
Keyword(s):  
Machine Learning ◽  
Message Passing ◽  
Density Functional ◽  
Large Scale ◽  
Molecular Design ◽  
Low Cost ◽  
Computational Cost ◽  
Three Dimensional ◽  
Biomolecular Systems ◽  
Quantum Observables

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.

scholarly journals Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

2021 ◽  
Vol 15 ◽  
Author(s):  
Nora Hollenstein ◽  
Cedric Renggli ◽  
Benjamin Glaus ◽  
Maria Barrett ◽  
Marius Troendle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Training Data ◽  
Human Cognition ◽  
Special Focus ◽  
Eeg Data

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

scholarly journals Stress Detection using Natural Language Processing and Machine Learning over social Interactions

2021 ◽  
Author(s):  
Tanya Nijhawan ◽  
Girija Attigeri ◽  
Ananthakrishna T
Keyword(s):  
Machine Learning ◽  
Social Interactions ◽  
Language Processing ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Textual Data ◽  
The Status ◽  
Micro Level

Abstract Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Subsequently, it can be used as a very effective tool in detecting the stress levels of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to successfully accomplish sentiment analysis with the aid of machine learning algorithms. We take the help of a capable deep learning pre-trained model called BERT to solve the problems which come with sentiment classification. The BERT model outperforms a lot of other well-known models for this job without any sophisticated architecture. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method that’s skilled in scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us predict which topic is linked to the textual data. With the aid of the models suggested, we will be able to detect the emotion of users online. We are primarily working with Twitter data because Twitter is a website where people express their thoughts often. In conclusion, this proposal is for the well- being of one’s mental health. The results are evaluated using various metric at macro and micro level and indicate that the trained model detects the status of emotions bases on social interactions.

scholarly journals Measurement of Dynamic Responses from Large Structural Tests by Analyzing Non-Synchronized Videos

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3520 ◽  
Author(s):  
Yang
Keyword(s):  
Image Analysis ◽  
Camera Calibration ◽  
Large Scale ◽  
Low Cost ◽  
Rapid Development ◽  
Video Recording ◽  
Dynamic Responses ◽  
Response Monitoring ◽  
Synchronization Method ◽  
Stereo Triangulation

Image analysis techniques have been employed to measure displacements, deformation, crack propagation, and structural health monitoring. With the rapid development and wide application of digital imaging technology, consumer digital cameras are commonly used for making such measurements because of their satisfactory imaging resolution, video recording capability, and relatively low cost. However, three-dimensional dynamic response monitoring and measurement on large-scale structures pose challenges of camera calibration and synchronization to image analysis. Without satisfactory camera position and orientation obtained from calibration and well-synchronized imaging, significant errors would occur in the dynamic responses during image analysis and stereo triangulation. This paper introduces two camera calibration approaches that are suitable for large-scale structural experiments, as well as a synchronization method to estimate the time difference between two cameras and further minimize the error of stereo triangulation. Two structural experiments are used to verify the calibration approaches and the synchronization method to acquire dynamic responses. The results demonstrate the performance and accuracy improvement by using the proposed methods.

scholarly journals Surrogate-guided sampling designs for classification of rare outcomes from electronic medical records data

Biostatistics ◽  
2020 ◽  
Author(s):  
W Katherine Tan ◽  
Patrick J Heagerty
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Language Processing ◽  
Large Scale ◽  
Model Performance ◽  
Learning Performance ◽  
Accurate Identification ◽  
Text Data ◽  
Data Set ◽  
The Impact

Summary Scalable and accurate identification of specific clinical outcomes has been enabled by machine-learning applied to electronic medical record systems. The development of classification models requires the collection of a complete labeled data set, where true clinical outcomes are obtained by human expert manual review. For example, the development of natural language processing algorithms requires the abstraction of clinical text data to obtain outcome information necessary for training models. However, if the outcome is rare then simple random sampling results in very few cases and insufficient information to develop accurate classifiers. Since large scale detailed abstraction is often expensive, time-consuming, and not feasible, more efficient strategies are needed. Under such resource constrained settings, we propose a class of enrichment sampling designs, where selection for abstraction is stratified by auxiliary variables related to the true outcome of interest. Stratified sampling on highly specific variables results in targeted samples that are more enriched with cases, which we show translates to increased model discrimination and better statistical learning performance. We provide mathematical details and simulation evidence that links sampling designs to their resulting prediction model performance. We discuss the impact of our proposed sampling on both model training and validation. Finally, we illustrate the proposed designs for outcome label collection and subsequent machine-learning, using radiology report text data from the Lumbar Imaging with Reporting of Epidemiology study.

scholarly journals Improving non-representative-sample prediction of forest aboveground biomass maps: A combined machine learning and spatial statistical approach

2019 ◽  
Author(s):  
Shaoqing Dai ◽  
Xiaoman Zheng ◽  
Lei Gao ◽  
Shudi Zuo ◽  
Qi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aboveground Biomass ◽  
Representative Sample ◽  
Large Scale ◽  
Statistical Approach ◽  
Low Cost ◽  
Ecological Modelling ◽  
Environmental Covariates ◽  
Remote Sensing Mapping ◽  
Forest Aboveground Biomass

Abstract. High-precision prediction of large-scale forest aboveground biomass (AGB) is important but challenging on account of the uncertainty involved in the prediction process from various sources, especially the uncertainty due to non-representative sample units. Usually caused by inadequate sampling, non-representative sample units are common and can lead to geographic clusters of localities. But they cannot fully capture complex and spatially heterogeneous patterns, in which multiple environmental covariates (such as longitude, latitude, and forest structures) affect the spatial distribution of AGB. To address this challenge, we propose herein a low-cost approach that combines machine learning with spatial statistics to construct a regional AGB map from non-representative sample units. The experimental results demonstrate that the combined methods can improve the accuracy of AGB mapping in regions where only non-representative sample units are available. This work provides a useful reference for AGB remote-sensing mapping and ecological modelling in various regions of the world.

Sign in / Sign up

Export Citation Format

Share Document