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Tag: machine learning

The Therapists Using AI to Make Therapy Better

Article Excerpt: Researchers have tried to study talking therapy for years to unlock the secrets of why some therapists get better results than others. It can be as much art as science, based on the experience and gut instinct of qualified therapists. It’s been virtually impossible to fully quantify what works and why—until now… AI is changing that equation. The type of machine learning that carries out automatic translation can quickly analyze vast amounts of language. That gives researchers access to an endless, untapped source of data: the language therapists use. Researchers believe they can use insights from that data to give therapy a long-overdue boost. The result could be that more people get better, and stay better.

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Article Source: MIT Technology Review


Wearable Tech Confirms Wear-and-Tear of Work Commute

Article Excerpt: Office commuting may not return to its pre-pandemic place in the work life, but snarled traffic and transit delays probably won’t go away for good. As hybrid and remote options remake the office, a new study demonstrates the link between commuting and job performance. The research also shows how consumer technology can predict individual work quality based on the daily grind of commuting. “Your commute predicts your day,” says Andrew Campbell, the Albert Bradley 1915 Third Century Professor of computer science, the lead researcher, and a co-author of the study. “COVID-19 may have upended the work world but traveling to and from the office remains an important part of life that affects the quality of work that people produce.”

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Article Source: Dartmouth News


How Machine Learning Systems Help EHR Usabilty, Mitigate Burden

Article Excerpt: Machine learning systems can aid EHR (electronic health record) usability and cut burden for disease phenotyping to support clinical research, according to a recent Mount Sinai study published in the journal Patterns.The machine learning-based algorithm diagnosed patients as accurately as the standard set of disease phenotyping algorithms for conditions like dementia, sickle cell anemia, and multiple sclerosis.

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Article Source: EHR Intelligence


A Yale Doctor Is Using a Video Game to Fight the Opioid Crisis

Article Excerpt: As drug-related deaths have spiked across the United States in recent years, doctors seeking to curb that surge are getting an unlikely new tool: a video game. The game, titled “PlaySmart,” was developed by Lynn Fiellin and funded in part by the National Institutes of Health. A professor at the Yale School of Medicine and Yale Child Study Center, as well as the founder and director of the play2PREVENT video game development lab, Fiellin hopes that by using “PlaySmart,” she and her team will be able to collect more data related to adolescent opioid misuse and provide aid to both kids who play the game and the adults who work those youths.

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Article Source: The Washington Post


Machine Learning 101: Promise, Pitfalls and Medicine’s Future

Article Excerpt: You’ve heard the term “machine learning” as it’s becoming recognized as a valuable tool to help physicians in diagnosing and managing patients, as well as other aspects of medicine. But do you understand what that buzzword really means? Two experts recently explained the fundamentals of machine learning, what it means in the clinical setting and the possible risks of using the technology during an education session—“Machine Learning: An Introduction and Discussion of Medical Applications”—that took place during the June 2021 AMA Sections Meetings and was hosted by AMA Medical Student Section.

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Article Source: AMA


How Your Phone Can Predict Depression and Lead to Personalized Treatment

Article Excerpt: In a recent study, publishing in the June 9, 2021 online edition of Nature Translational Psychiatry , researchers at University of California San Diego School of Medicine used a combination of modalities, such as measuring brain function, cognition and lifestyle factors, to generate individualized predictions of depression. The machine learning and personalized approach took into account several factors related to an individual’s subjective symptoms, such as sleep, exercise, diet, stress, cognitive performance and brain activity.

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Article Source: UC San Diego Health


The Potential of Artificial Intelligence to Bring Equity in Health Care

Article Excerpt: The Jameel Clinic recently hosted the AI for Health Care Equity Conference to assess current state-of-the-art work in this space, including new machine learning techniques that support fairness, personalization, and inclusiveness; identify key areas of impact in health care delivery; and discuss regulatory and policy implications. Nearly 1,400 people virtually attended the conference to hear from thought leaders in academia, industry, and government who are working to improve health care equity and further understand the technical challenges in this space and paths forward.

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Article Source: MIT News


Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma

Lekkas D, Jacobson N. (2021). Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Scientific Reports. 11(1): 10303. doi: 10.1038/s41598-021-89768-2

Researchers examined the efficacy of using time-anchored Global Positioning System (GPS) location data to detect post-traumatic stress disorder (PTSD) diagnostic status among women (ages 18–65 years) who had experienced child abuse (n = 185). Read More


Associations between substance use and Instagram participation to inform social network-based screening models: Multimodal cross-sectional study

Bergman B, Wu W, Marsch L. (2020). Associations between substance use and Instagram participation to inform social network-based screening models: Multimodal cross-sectional study. JMIR. 22(9): e21916. doi: 10.2196/21916

Researchers recruited Instagram users ages 18-73 years (n = 3117) to examine associations between substance use and Instagram participation and explore whether age, gender, and race/ethnicity moderate these relationships. Read More