scholarly journals Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

2020 ◽  
Vol 33 (11) ◽  
pp. 2169-2185 ◽  
Author(s):  
Andrew J. Schaumberg ◽  
Wendy C. Juarez-Nicanor ◽  
Sarah J. Choudhury ◽  
Laura G. Pastrián ◽  
Bobbi S. Pritt ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Patient Care ◽  
Real Time ◽  
Improve Patient Care ◽  
Low Grade ◽  
Search Performance ◽  
Cell Nuclei ◽  
Disease States ◽  
Share Data

Abstract Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805–0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

scholarly journals Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

2018 ◽  
Author(s):  
Andrew J. Schaumberg ◽  
Wendy C. Juarez-Nicanor ◽  
Sarah J. Choudhury ◽  
Laura G. Pastrián ◽  
Bobbi S. Pritt ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Patient Care ◽  
Real Time ◽  
Improve Patient Care ◽  
Low Grade ◽  
Search Performance ◽  
Cell Nuclei ◽  
Disease States ◽  
Share Data

AbstractPathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k=1 = 0.7618±0.0018 (chance 0.397±0.004, mean±stdev). The classifiers find texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g. cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, pre-neoplastic/benign/ low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e. from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data throughpathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

scholarly journals Opinion of Healthcare Professionals in the Usage of Social Media for Patient Care in Majmaah, Saudi Arabia

2017 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Syed Meraj Ahmed ◽  
Faisal Alhumaidi Alruways ◽  
Thamer Fahad Alsallum ◽  
Meshal Munahi Almutairi ◽  
Abdullah Saif Al-Subhi ◽  
...  
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Patient Care ◽  
Healthcare Professionals ◽  
Healthcare Providers ◽  
Improve Patient Care ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Validated Questionnaire ◽  
Use Of Social Media

<span lang="EN-US">Use of social media for patient care is the new frontier in the healthcare indus-try. Sharing of information between the clinicians and their patients is now so much easier. In slowly gaining a foothold worldwide it needs a healthy push to make it universally accepta-ble. Study the knowledge, attitude, and practices of healthcare providers on the usage of social media in their clinical practice.</span><span lang="EN-US">A baseline cross – sectional study was conducted among 200 healthcare professionals from March 2015 to September 2015 on their knowledge, attitude, and practices in the use of social media for patient care in Majmaah, Saudi Arabia. A close ended self – administered validated questionnaire was used to gather data which was analyzed by using the SPSS ver. 21.0 software. 55.3% participants used social media for both professional and personal reasons. Some (25.3%) specified using it for patient care while a significant majority (52.9%) opined that it can be successfully used for patient interaction. Nearly 55% agreed that social media should not be banned due to its benefits as an efficient tool for patient communication. </span><span>S</span><span lang="EN-US">ocial media use for pa-tient doctor interaction should be encouraged to improve patient care through effective com-munication.</span>

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

scholarly journals Monitoring global trends in Covid-19 vaccination intention and confidence: a social media-based deep learning study

2021 ◽  
Author(s):  
Xinyu Zhou ◽  
Alex de Figueiredo ◽  
Qin Xu ◽  
Leesa Lin ◽  
Per E Kummervold ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Eastern Mediterranean ◽  
Learning Models ◽  
Social Media Data ◽  
Emerging Trends ◽  
The Us ◽  
Media Monitoring ◽  
Media Data

AbstractBackgroundThis study developed deep learning models to monitor global intention and confidence of Covid-19 vaccination in real time.MethodsWe collected 6.73 million English tweets regarding Covid-19 vaccination globally from January 2020 to February 2021. Fine-tuned Transformer-based deep learning models were used to classify tweets in real time as they relate to Covid-19 vaccination intention and confidence. Temporal and spatial trends were performed to map the global prevalence of Covid-19 vaccination intention and confidence, and public engagement on social media was analyzed.FindingsGlobally, the proportion of tweets indicating intent to accept Covid-19 vaccination declined from 64.49% on March to 39.54% on September 2020, and then began to recover, reaching 52.56% in early 2021. This recovery in vaccine acceptance was largely driven by the US and European region, whereas other regions experienced the declining trends in 2020. Intent to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia, Eastern Mediterranean, and Western Pacific regions, but low in American, European, and African regions. 12.71% tweets expressed misinformation or rumors in South Korea, 14.04% expressed distrust in government in the US, and 16.16% expressed Covid-19 vaccine being unsafe in Greece, ranking first globally. Negative tweets, especially misinformation or rumors, were more engaged by twitters with fewer followers than positive tweets.InterpretationThis global real-time surveillance study highlights the importance of deep learning based social media monitoring to detect emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions.FundingNational Natural Science Foundation of China.Research in contextEvidence before this studyWith COVID-19 vaccine rollout, each country should investigate its vaccination intention in local contexts to ensure massive vaccination. We searched PubMed for all articles/preprints until April 9, 2021 with the keywords “(“Covid-19 vaccines”[Mesh] OR Covid-19 vaccin*[TI]) AND (confidence[TI] OR hesitancy[TI] OR acceptance[TI] OR intention[TI])”. We identified more than 100 studies, most of which are country-level cross-sectional surveys, and the largest global survey of Covid-19 vaccine acceptance only covered 32 countries to date. However, how Covid-19 vaccination intention changes over time remain unknown, and many countries are not covered in previous surveys yet. A few studies assessed public sentiments towards Covid-19 vaccination using social media data, but only targeting limited geographical areas. There is a lack of real-time surveillance, and no study to date has globally monitored Covid-19 vaccination intention in real time.Added value of this studyTo our knowledge, this is the largest global monitoring study of Covid-19 vaccination intention and confidence with social media data in over 100 countries from the beginning of the pandemic to February 2021. This study developed deep learning models by fine-tuning a Bidirectional Encoder Representation from Transformer (BERT)-based model with 8000 manually-classified tweets, which can be used to monitor Covid-19 vaccination beliefs using social media data in real time. It achieves temporal and spatial analyses of the evolving beliefs to Covid-19 vaccines across the world, and also an insight for many countries not yet covered in previous surveys. This study highlights that the intention to accept Covid-19 vaccination have experienced a declining trend since the beginning of the pandemic in all world regions, with some regions recovering recently, though not to their original levels. This recovery was largely driven by the US and European region (EUR), whereas other regions experienced the declining trends in 2020. Intention to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia region (SEAR), Eastern Mediterranean region (EMR), and Western Pacific region (WPR), but low in American region (AMR), EUR, and African region (AFR). Many AFR countries worried more about vaccine effectiveness, while EUR, AMR, and WPR concerned more about vaccine safety (the most concerns with 16.16% in Greece). Online misinformation or rumors were widespread in AMR, EUR, and South Korea (12.71%, ranks first globally), and distrust in government was more prevalent in AMR (14.04% in the US, ranks first globally). Our findings can be used as a reference point for survey data on a single country in the future, and inform timely and specific interventions for each country to address Covid-19 vaccine hesitancy.Implications of all the available evidenceThis global real-time surveillance study highlights the importance of deep learning based social media monitoring as a quick and effective method for detecting emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions, especially in settings with limited sources and urgent timelines. Future research should build multilingual deep learning models and monitor Covid-19 vaccination intention and confidence in real time with data from multiple social media platforms.

scholarly journals Opinion of Healthcare Professionals in the Usage of Social Media for Patient Care in Majmaah, Saudi Arabia

2017 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Syed Meraj Ahmed ◽  
Faisal Alhumaidi Alruways ◽  
Thamer Fahad Alsallum ◽  
Meshal Munahi Almutairi ◽  
Abdullah Saif Al-Subhi ◽  
...  
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Patient Care ◽  
Healthcare Professionals ◽  
Healthcare Providers ◽  
Improve Patient Care ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Validated Questionnaire ◽  
Use Of Social Media

Use of social media for patient care is the new frontier in the healthcare indus-try. Sharing of information between the clinicians and their patients is now so much easier. In slowly gaining a foothold worldwide it needs a healthy push to make it universally accepta-ble. Study the knowledge, attitude, and practices of healthcare providers on the usage of social media in their clinical practice.A baseline cross–sectional study was conducted among 200 healthcare professionals from March 2015 to September 2015 on their knowledge, attitude, and practices in the use of social media for patient care in Majmaah, Saudi Arabia. A close ended self – administered validated questionnaire was used to gather data which was analyzed by using the SPSS ver. 21.0 software. 55.3% participants used social media for both professional and personal reasons. Some (25.3%) specified using it for patient care while a significant majority (52.9%) opined that it can be successfully used for patient interaction. Nearly 55% agreed that social media should not be banned due to its benefits as an efficient tool for patient communication. Social media use for pa-tient doctor interaction should be encouraged to improve patient care through effective communication.

scholarly journals Sentiment Classification System of Twitter Data using Python

2020 ◽  
Vol 8 (6) ◽  
pp. 1042-1044
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Classification System ◽  
Sentiment Classification ◽  
Time Data ◽  
Twitter Data ◽  
Real Time Data ◽  
Share Data ◽  
Day By Day

Social media has developed drastically over the years. These days, individuals from all around the globe utilize online networking destinations to share data and information. Twitter is a well known communication site where users update information or messages known as tweets. Users share their day by day lives, post their opinions on everything, for example, brands and places. Various purchasers and advertisers utilize these tweets to accumulate bits of knowledge of their items and opinions on them. The aim of this paper is to exhibit a model that can perform sentiment analysis of real-time data collected from twitter and classify the tweets into positive, negative or neutral based on the sentiment expressed in them.

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