Sunday, April 11, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Machine Learning

How Machine Learning Is Being Used To Eradicate Medication Errors

April 11, 2020
in Machine Learning
How Machine Learning Is Being Used To Eradicate Medication Errors
587
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter


People working in the healthcare sector take extra precautions to avoid mistakes and medication errors that can put the lives of patients at risk. Yet, despite this, 2% of patients face preventable medical-related incidents that could be life-threatening. Inadequate systems, tools, processes or working conditions are some of the reasons contributing to these medical mistakes.

You might also like

Can a Machine Learning Model Predict T2D?

Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU

IBM releases Qiskit modules that use quantum computers to improve machine learning

In a bid to solve this problem, Google collaborated with UCSF’s Bakar Computational Health Sciences Institute to publish “Predicting Inpatient Medication Orders in Electronic Health Record Data” in Clinical Pharmacology and Therapeutics. The published paper discusses how machine learning (ML) can be used to anticipate standard prescribing patterns by doctors as per the availability of electronic health records.

Google used clinical data of de-identified patients, which included vital signs, laboratory results, past medications, procedures, diagnoses, and more. Google’s new model was designed to anticipate a physician’s prescription decisions three-quarters of the time, after evaluating the patient’s current state and medical history.


W3Schools


Model Training

To train the model, Google chose a dataset containing approximately three million medication orders from more than 1,00,000 hospitals. The company acquired the retrospective electronic health data through de-identification, by choosing random dates and removing all the identifying checkpoints of the record as per the HIPPA rules and guidelines. The company did not gather any identifying information such as names, addresses, contact details, record numbers, names of physicians, free-text notes, images, etc.

The research by the tech giant was done using the open-sourced Fast Healthcare Interoperability Resources (FHIR) format that the company claims was previously applied to improve healthcare data and make it more useful for machine learning. Google did not restrict the dataset to a particular disease, which made the ML activity more demanding. It also allowed the model to identify a wider variety of medical conditions.

Google approached two different ML models – the long short-term recurrent neural network, and the regularized time-bucketed logistic model, which are often used in clinical research. Both models were put into comparison against a simple baseline, which was ranked as the most commonly ordered medication based on a patient’s hospital service, along with time spent since the admission in the hospital. The models ranked a list of 990 possible medications every time a medication was entered in the retrospective data. The team further assessed if the models assigned high probabilities to the medication that were provided by the doctors for each case. 

Findings

Google’s best performing model was the LSTM model, which is capable of handling sequential data, including text and language. The model has been designed to choose the recent events in data and their order, which makes it an excellent option to deal with this problem. Almost 93% of the top-10 list included at least one medication that a clinician would prescribe to a patient within the next day.

The model rightly forecasted the medications prescribed by a doctor as one of the top-10 most likely medications, which calculated to an accuracy amount of 55%. 75% of the ordered medication were ranked in top-25, whereas false-negative cases, where a doctor’s medication did not make it into the top-25 results, found itself to be in the same 42% of the time as ranked by the model.

Benefits For Patients & Clinicians

These models are trained to mimic a physician’s behavior as it appears in historical data, and did not learn the optimal prescribing pattern. Due to this, the models do not understand how the medications might work, or if they have any side effects or not. As per Google, the learning sequence will take time to show normal behavior in a bid to spot abnormal and potentially dangerous orders. In the next phase, the company will examine the models under different circumstances to understand which medication error can cause harm to patients.

The result of this work by Google is a small step towards testing the hypothesis that machine learning can be applied to build different systems which can prevent mistakes on the part of doctors and clinicians to keep patients safe. Google is looking forward to collaborating with doctors, pharmacists, clinicians and patients to continue the research for a better result.


Provide your comments below

comments


Credit: Google News

Previous Post

Brazilian food and drug regulator bans Zoom

Next Post

Google and Apple Plan to Turn Phones into COVID-19 Contact-Tracking Devices

Related Posts

Can a Machine Learning Model Predict T2D?
Machine Learning

Can a Machine Learning Model Predict T2D?

April 11, 2021
Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU
Machine Learning

Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU

April 10, 2021
IBM releases Qiskit modules that use quantum computers to improve machine learning
Machine Learning

IBM releases Qiskit modules that use quantum computers to improve machine learning

April 10, 2021
One-stop machine learning platform turns health care data into insights | MIT News
Machine Learning

One-stop machine learning platform turns health care data into insights | MIT News

April 10, 2021
Machine learning: is there a limit to technological patents in Brazil?
Machine Learning

Disclosing AI Inventions – Part I: Identifying the Unique Disclosure Issues

April 10, 2021
Next Post
Google and Apple Plan to Turn Phones into COVID-19 Contact-Tracking Devices

Google and Apple Plan to Turn Phones into COVID-19 Contact-Tracking Devices

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Can a Machine Learning Model Predict T2D?
Machine Learning

Can a Machine Learning Model Predict T2D?

April 11, 2021
Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success
Data Science

Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success

April 11, 2021
Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU
Machine Learning

Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU

April 10, 2021
Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison
Data Science

Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison

April 10, 2021
IBM releases Qiskit modules that use quantum computers to improve machine learning
Machine Learning

IBM releases Qiskit modules that use quantum computers to improve machine learning

April 10, 2021
Hackers Tampered With APKPure Store to Distribute Malware Apps
Internet Privacy

Hackers Tampered With APKPure Store to Distribute Malware Apps

April 10, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Can a Machine Learning Model Predict T2D? April 11, 2021
  • Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success April 11, 2021
  • Machine Learning in Finance Market is exclusively demanding in forecast 2029 | Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance – KSU April 10, 2021
  • Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison April 10, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates