Public Health - Santé Publique
84.8K views | +0 today
Follow
Public Health - Santé Publique
Health
Your new post is loading...
Your new post is loading...
Rescooped by Lionel Reichardt / le Pharmageek from healthcare technology
Scoop.it!

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department | Public Health - Santé Publique | Scoop.it
From www.nature.com - June 21, 2021 2:09 AM

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making.


 


We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables.


 


Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours.


 


The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.


 


 


In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.


 


read the open article at https://www.nature.com/articles/s41746-021-00453-0


 

Lire l'article complet sur : www.nature.com


Via nrip
more...
No comment yet.
Sign up to comment
Rescooped by Lionel Reichardt / le Pharmageek from healthcare technology
Scoop.it!

Shortcomings with the AI Tools and Devices Preventing COVID-19?

Shortcomings with the AI Tools and Devices Preventing COVID-19? | Public Health - Santé Publique | Scoop.it
From www.altoros.com - May 10, 2021 10:52 AM

Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection.

Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures.

 

As an example, numerous airlines, hotels, subways, shopping malls, and other institutions are already using thermal cameras to measure an individual’s temperature before people are allowed entry. In its turn, public transport in France relies on AI-based surveillance cameras to monitor whether or not people are social-distancing or wearing masks. Another example is requiring the download of contact-tracing apps delivered by governments across the globe.

 

However, there are a number of issues.

 

While many of these solutions help to ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic.

 

Issue #1. Manual temperature scanning is tricky

Issue #2. Monitoring crowds is even more complex

Issue #3. Contact tracing leads to privacy concerns

Issue #4. UV rays harm eyes and skin

Issue #5. UVC robots are extremely expensive

Issue #6. No integration, no compliance, no transparency

Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal.

 

read the details at https://www.altoros.com/blog/whats-wrong-with-ai-tools-and-devices-preventing-covid-19/

 

Lire l'article complet sur : www.altoros.com


Via nrip
more...
nrip's curator insight, May 8, 2021 1:54 AM

Yes, there are issues with some of the innovations being used. But a faster response is a useful response. I found this post extremely well researched and accurate , and not necessarily negetive. We need criticism of good intentions to make them better. This post does that. These is a valuable list of some shortcomings and some mistakes which will be worked on and improved. Sometimes by changing the system, sometimes by changing the financial model, and sometimes by changing behaviour and mindset.

 

The future of healthcare contains a lot of AI. That bit is true.
Richard Platt's curator insight, May 10, 2021 11:29 AM

Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection.

Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures.  While there are many AI solutions to help ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic.   

Issue #1. Manual temperature scanning is tricky
Issue #2. Monitoring crowds is even more complex
Issue #3. Contact tracing leads to privacy concerns
Issue #4. UV rays harm eyes and skin
Issue #5. UVC robots are extremely expensive
Issue #6. No integration, no compliance, no transparency
Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal.
Sign up to comment
Rescooped by Lionel Reichardt / le Pharmageek from healthcare technology
Scoop.it!

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events | Public Health - Santé Publique | Scoop.it
From publichealth.jmir.org - June 17, 2021 2:35 AM

Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.



Objective: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.



Methods: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.



Results: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.



Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems.


 


Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether.


 


The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus.


 


Such systems may aid future efforts to prevent and contain the spread of infectious diseases.


 


read the study at https://publichealth.jmir.org/2021/3/e26719


 

Lire l'article complet sur : publichealth.jmir.org


Via nrip
more...
nrip's curator insight, June 15, 2021 11:26 PM

Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. Using algorithms and/or learning models to extract travel related information from EHR's is not a novel concept but it has come into the spotlight(like most of digital health) in the past 18 months.

 

We should be adding short travel related questionnaires in patient intake forms going forward. The symptoms which trigger this sort of an intake form for a particular patient can change with time, month to month preferably, and be governed by a multi regional , multi national approach. What do you think?

 

 

 
Sign up to comment
About Follow us Resources Terms Mobile Features
Facebook
X
LinkedIn