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8 ML/AI Projects To Make Your Portfolio Stand Out


This topic is so sensitive to be considered nowadays and in urgent need to do something about it. There are more than 264 million individuals worldwide who are suffering from depression. Depression is the main cause of disability worldwide and is a significant supporter of the overall global burden of disease and nearly 800,000 individuals consistently bite the dust because of suicide every year. Suicide is the second driving reason for death in 15–29-year-olds. Treatment for depression is often delayed, imprecise, and/or missed entirely.

How Does Technology Help to Improve Mental Health & Illness?


In any given year, 1 in 5 employed US adults experience a mental health issue like depression, anxiety, and insomnia. COVID19 has pushed the world into an uncharted territory as it has proved to be a perfect storm of stressors -- Right from job loss, economic instability, home schooling, food & health insecurity to the uncertainty of when (or even if) life will return to normal. A simple example -- 33 million jobs lost as of May 7, 2020 – huge financial stress, lock down multiplied domestic violence etc. In just a few months, Covid19 has just doubled the stat of the population affected mentally. Hence, mental health needs urgent addressing and some cool innovative technology solutions are coming to the rescue.

Study devises Machine Learning Model to detect Early Signs of Depression in Written Text


Now, there is one. A new machine learning algorithm devised by a team of computing scientists at the University of Alberta can identify early signal of …

Machine Learning Prediction of Treatment Response to Antidepressants


Question Can machine learning models predict improvement of various depressive symptoms with antidepressant treatment based on pretreatment symptom scores and electroencephalographic measures? Findings In this prognostic study, using the machine learning approach of gradient-boosted decision trees, the ElecTreeScore algorithm could reliably distinguish the patients who responded to treatment from those who did not based on various depressive symptoms using pretreatment symptom scores and electroencephalographic features (using the cross-validation approach on 518 patients). Meaning Machine learning approaches that include pretreatment symptom scores and electroencephalographic features may help predict which depressive symptoms will improve with antidepressants. Importance Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. Objective To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. Design, Setting, and Participants This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019.

GPT-3 Creative Fiction


What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.

With $9M in seed funding, StuffThatWorks launches AI-enabled crowdsourced healthcare platform - SiliconANGLE


StuffThatWorks, which harnesses the power of artificial intelligence to crowdsource data on health-related issues, today announced it's launching its platform after raising $9 million in seed funding. When it comes to chronic conditions, many people find themselves dissatisfied with the slow process of medical science because it often requires lengthy test series and long visits with specialists. The properly cautious nature of medical treatment can lead to slow improvement, which leads many patients to social media to seek out anecdotes from other sufferers to identify other options. For many patients and researchers, this second process is often haphazard and fraught with difficulty. It also often leads to questionable conclusions by patients who cannot easily sift through the experiences of other people in forums, Facebook and other places to get a better grasp on their own treatment.

UCLA Children's Hospital implements new AI robot to improve mental health during treatment


Modern medicine can often create magic, but often an extended stay in hospital can significantly impact mental health, especially in children separated from their parents for the first time in their lives. Now a new AI robot is being introduced into US hospitals to help children be happier during their hospital stay. Robin is an AI robot built by Armenia-based startup Expper Technologies, which has been designed to help kids in hospitals beat loneliness and isolation. Children can be very vulnerable to the effects and stress of intensive treatments with unfamiliar people, complex equipment, and painful procedures. The robot, sized at just under four feet tall and weighing 55 pounds, is intended to be a friendly companion robot with a child-friendly design, which interacts with children to distract them from the process. It is used to establish a peer-to-peer connection with children to ease their stress.

The Ethics of Dangerous Code


Recently I read a paper about automatic detection of suicide-related tweets: A machine learning approach predicts future risk to suicidal ideation from social media data. The authors of this study, Arunima Roy and colleagues, trained neural network models to detect suicidal thoughts and reported suicide attempts in tweets. Code availability: Due to the sensitive and potentially stigmatizing nature of this tool, code used for algorithm generation or implementation on individual Twitter profiles will not be made publicly available. Given that the paper describes an algorithm that could scan Twitter and identify suicidal people, it's not hard to imagine ways in which it could be misused. In this post I want to examine this kind of "ethical non-sharing" of code.

Explainable-Machine-Learning to discover drivers and to predict mental illness during COVID-19


The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

How AI Is Advancing NeuroTech


Neurotechnologies are based on the principles of the human nervous system and modeled on the human brain. NeuroTech can help researchers understand brain function and disfunction, and can help doctors treat neurological disorders. Some NeuroTech applications are focused on enhancing cognitive performance, improving sleep, and improving brain health for Longevity. Advances in AI could revolutionize NeuroTech over the next decade. NeuroTech has unprecedented prospects for growth both in terms of technology and as an industry.