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Big Data in the ER

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Research
16 August 2022
Scientists from the Department of Traumatology and Acute Critical Medicine at the Osaka University Graduate School of Medicine developed an AI algorithm to predict the risk of mortality for patients suffering a major injury. Using the Japan Trauma Data Bank for the years 2013 to 2017, they were able to obtain records for over 70,000 patients who had experienced blunt-force trauma, which allowed the researchers to identify critical factors that could guide treatment strategies more precisely.
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Ultra-Thin but Tough Implantable Material could Treat Spinal Cord Injury and Parkinson's Disease

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Research
11 August 2022
Flexible implanted electronics are a step closer toward clinical applications thanks to a recent breakthrough technology developed by a research team from Griffith University and UNSW Sydney.

The work was pioneered by Dr Tuan-Khoa Nguyen, Professor Nam-Trung Nguyen and Dr Hoang-Phuong Phan (currently a senior lecturer at the University of New South Wales) from Griffith University's Queensland Micro and Nanotechnology Centre (QMNC) using in-house silicon carbide technology as a new platform for long-term electronic biotissue interfaces.

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AI Algorithm that Detects Brain Abnormalities could Help Cure Epilepsy

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Research
10 August 2022
An artificial intelligence (AI) algorithm that can detect subtle brain abnormalities which cause epileptic seizures has been developed by a UCL-led team of international researchers.

The Multicentre Epilepsy Lesion Detection project (MELD) used over 1,000 patient MRI scans from 22 global epilepsy centres to develop the algorithm,

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AI Tools Predict DNA's Regulatory Role and 3D Structure

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Research
09 August 2022
Newly developed artificial intelligence (AI) programs accurately predicted the role of DNA's regulatory elements and three-dimensional (3D) structure based solely on its raw sequence, according to two recent studies in Nature Genetics. These tools could eventually shed new light on how genetic mutations lead to disease and could lead to new understanding of how genetic sequence influences the spatial organization and function of chromosomal DNA in the nucleus,
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New AI Technology Integrates Multiple Data Types to Predict Cancer Outcomes

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Research
08 August 2022
While it's long been understood that predicting outcomes in patients with cancer requires considering many factors, such as patient history, genes and disease pathology, clinicians struggle with integrating this information to make decisions about patient care. A new study from researchers from the Mahmood Lab at Brigham and Women's Hospital reveals a proof-of-concept model that uses artificial intelligence (AI) to combine multiple types of data from different sources to predict patient outcomes for 14 different types of cancer.
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Proteins and Natural Language: AI Enables the Design of Novel Proteins

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Research
04 August 2022
Artificial intelligence (AI) has created new possibilities for designing tailor-made proteins to solve everything from medical to ecological problems. A research team at the University of Bayreuth led by Prof. Dr. Birte Höcker has now successfully applied a computer-based natural language processing model to protein research.
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Using Smartphones could Help Improve Memory Skills

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Research
02 August 2022
Using digital devices, such as smartphones, could help improve memory skills rather than causing people to become lazy or forgetful, finds a new study led by UCL researchers.

The research, published in Journal of Experimental Psychology: General, showed that digital devices help people to store and remember very important information.

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More eHealth News ...

  1. Advancing Dynamic Brain Imaging with AI
  2. MIT Engineers Develop Stickers that can See Inside the Body
  3. Patient Deterioration Predictor could Surpass Limits of Traditional Vital Signs
  4. AI Analyses Neuron Changes to Detect whether Drugs are Effective for Neurodegenerative Disease Patients
  5. Teaching AI to Ask Clinical Questions
  6. Virtual Reality App Trial Shown to Reduce Common Phobias
  7. A Machine Learning Model to Predict Immunotherapy Response in Cancer Patients
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