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Machine Learning

Can machine learning predict and prevent mental health problems? | News Medical Net

Developing a Crisis Surveillance System for Mental Health Using Machine Learning Models 

It had observed that the prompt identification of persons at risk of mental health decline improves illness outcomes. In the current study, researchers constructed a machine learning model that used people’s electronic health information to monitor them for 28 days for any danger of a mental health crisis. A crisis had defined as the emergence of symptoms requiring healthcare services. In the hospital’s database, time series of events have represented with timestamps and event attributes in separate SQL tables. Based on data from 2012 to 2019, models are trained, tweaked, and chosen. 

The system forecasts the probability of a crisis occurring during the following 28 days (whereby the algorithm is queried every week for every patient). The dashboard displays the patients with the most significant estimated risk with essential indicators, patient notes, and a questionnaire form that the doctor completes for each patient. Classifiers based on machine learning approaches such as decision trees, ensembles, probabilistic, and deep learning were evaluated. The researchers compared the XGBoost (extreme gradient boosting) model to two baseline variables, namely the clinical practice diagnosis-based baseline model and prospective study data. In the case of organic illnesses, the general model performed much better with an area under the receiver operating characteristic (AUROC) of 0.89 compared to its overall performance of 0.797%. 

The lowest performance was reported for mood disorders, schizophrenia, schizotypal disorders, and delusional disorders. Researchers think this research might encourage mental health professionals to change from reactive to preventive treatment. 

Source: https://www.news-medical.net/news/20220519/Can-machine-learning-predict-and-prevent-mental-health-problems.aspx  

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Machine Learning

Machine learning radically reduces workload of cell counting for disease diagnosis | Techxplore

Utilizing machine learning to accomplish medical picture segmentation is a novel approach. 

China’s Beihang University has created a new training approach that automates a significant portion of the manual annotation labor performed by people. Machine learning can do blood cell counts for illness diagnosis instead of costly and often less precise cell analyzer devices. On April 9, an article describing their new training program has published in the journal Cyborg and Bionic Systems. 

Since 2015, researchers from China’s Beihang University have created a new training strategy for the CNN, or convolutional network segmentation model, frequently used in medical picture segmentation. They discovered that their training approach for segmenting multiple-cell-type photos reached the same level as training with manually annotated multiple-tone white images: 94.85 percent.  Source: https://techxplore.com/news/2022-05-machine-radically-workload-cell-disease.html

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Machine Learning

Estimating the informativeness of data | MIT News

Researchers at MIT have developed a technique for estimating how much information data are likely to contain that is more accurate and scalable than earlier approaches. 

All data is not created equal. But how much information is likely to be included in any one piece of data? This issue lies at the heart of medical research, scientific experiment development, and even ordinary human knowledge and thought. Researchers at MIT have devised a novel method for addressing this issue, which has implications for health, scientific discovery, cognitive science, and artificial intelligence. The essential concept is to utilize probabilistic inference techniques to first infer which explanations are likely, and then use these probable explanations to generate high-quality entropy estimates rather than enumerating all possible explanations. The research demonstrates that this inference-based method is substantially quicker and more accurate than earlier methods. 

Source: https://news.mit.edu/2022/estimating-informativeness-data-0425 

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Machine Learning

Meta AI Researchers Built An End-To-End Machine Learning Platform Called Looper, With Easy-To-Use APIs For Decision-Making And Feedback Collection | Mark Tech Post

Looper is a comprehensive artificial intelligence platform for optimization, personalization, and growth. 

Looper is a fully integrated artificial intelligence platform for optimization, customization, and feedback collecting developed by Meta Researchers. Looper can support the whole machine learning lifecycle, from model training through deployment and inference and product assessment and optimization. The Looper platform now hosts 700 AI models and generates 4 million AI outputs every second. It is intended for use cases with limited data volumes and model complexity that demand simplicity and speedy implementation. Looper runs in real-time, in contrast to many other AI systems, which make inferences in batch mode. 

Unlike large-scale AI models for vision, speech, and natural language processing, Looper uses models that can be rapidly retrained and deployed in huge numbers on shared infrastructure. A/B testing may investigate several models and decision rules, such as those employed by contextual bandits to mimic prediction uncertainty across one or more targets. A simple-to-use AI platform is often the deciding factor in adoption for teams with no previous expertise with production AI. Meta’s platform takes care of software updates, log management, and monitoring, resulting in considerable productivity improvements: It enables product makers to deploy more AI use cases than solutions with a restricted emphasis. Product teams have a spectrum of AI capabilities between beginners and expert AI engineers.

Source: https://www.marktechpost.com/2022/04/22/meta-ai-researchers-built-an-end-to-end-machine-learning-platform-called-looper-with-easy-to-use-apis-for-decision-making-and-feedback-collection/

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Machine Learning

Machine Learning turns monochromatic night vision into a rainbow of colors | Daily Beast 

Thanks to machine learning, scientists are altering what we see when we gaze through a night vision scope. 

Researchers at the University of California, Irvine, employed machine learning to create a natural rainbow of hues from what you see via a night vision scope or camera. Not only may the research aid the military, but also medical technology, healthcare, and even specialized duties such as art restoration. To comprehend how the new night vision technology works, it’s necessary first to grasp human eyesight. Neural networks are computer programs that operate like that of an artificial brain. The researchers at UC Irvine predicted the visible spectrum picture using infrared photographs of three distinct wavelengths and deep learning. 

After that, the neural networks were tasked with reconstructing the pictures’ hue, which was now captured using a night vision camera. Artificial neural networks will enable a slew of various scientific application undertakings. While the military is undoubtedly interested in this technology, it might also be beneficial in eye surgery and art restoration. “They have the potential to improve a clinician’s capacity to operate,” Browne said. “When applied to new technologies, they can improve the technology’s performance.” 

Source: https://www.thedailybeast.com/machine-learning-turns-monochromatic-night-vision-into-a-rainbow-of-colors