Search:
Scroll to top
Tag: machine learning
07/11/2022

Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): A retrospective deep learning study

Afshar M, Sharma B, Dligach D, Oguss M, Brown R, Chhabra N, Thompson HM, Markossian T, Joyce C, Churpek MM, & Karnik NS (2022). Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. The Lancet (British Edition), 4(6), e426–e435. https://doi.org/10.1016/S2589-7500(22)00041-3

SMART-AI is a substance misuse algorithm to support referral to treatment using artificial intelligence. The tool is a machine learning classifier tool for identifying alcohol misuse, opioid misuse, and non-opioid drug misuse using clinical notes collected in the electronic health records. Using of patients (N=16,917) during the first 24 hours of hospitalization, the prospective primary analysis consisted of temporal validation done to examine misuse classification and the association to outcomes and treatment referrals. Results from manual screening identified 3.5% of patients had any type of substance misuse and 11% of these patients had more than one type of misuse. SMART-AI showed good calibration and validity, with a false negative rate of 0.18-0.19 and a false positive rate of 0.03 between non-Hispanic Black and non-Hispanic White subgroups. The results also show prediction performance can change over time or in differing patient settings, where prevalence of substance misuse varies. There were also significant changes during the COVID-19 pandemic which required the algorithm to be recalibrated. Overall, this study demonstrated that clinical notes from the electronic health record during initial hospitalization can be used to identify substance misuse accurately with the help of artificial intelligence and may be used to potentially improve screening rates.

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ; ;
05/23/2022

Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach

Shiba K, Daoud A, Kino S, Nishi D, Kondo K, and Kawachi I. (2022), Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach. Psychiatry Clin. Neurosci., 76: 97-105. https://doi-org.dartmouth.idm.oclc.org/10.1111/pcn.13322

Researchers investigated the heterogeneous effects of disaster-related traumatic experiences on post-disaster mental health problems, using a new machine learning approach. Data was derived from a prospective cohort study of Japanese older adults (65 and older) in an area severely affected by the Great East Japan Earthquake of 2011. Baseline data were from 7 months before the earthquake (N=4,957 participants) and two follow-ups were conducted 2.5 and 5.5 years after the earthquake (n=3,567 and n=2,781 respectively). Disaster-related traumatic experiences were defined as home loss and loss of loved ones due to the disaster. Depressive symptoms and posttraumatic stress symptoms were assessed at the two follow-up time points. Researchers applied a novel machine learning approach called the generalized random forest algorithm to estimate the conditional average treatment effects of the disaster damages on mental health outcomes. Results showed significant heterogeneity in the impact of disaster damages across individuals, with unique patterns in characteristics of individuals who were more severely impacted. As an example, the most vulnerable group tended to be from lower socioeconomic status with preexisting depressive symptoms. The study demonstrates that this machine learning method can identify heterogeneity in mental health problems  among respondents following a disaster event. Analyzing such heterogeneity may be beneficial in designing future post-disaster mental health interventions.

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ;
05/10/2022

How Wearables and Health Apps Can Help Diagnose and Treat Diseases

Article Excerpt: Wearable devices, such as fitness trackers and smartwatches, can measure a growing array of health indicators. Machine learning can filter that torrent of data to reveal a continuous, quantified picture of you and your health. But wearables linked to health apps are not only able to help diagnose diseases—they are beginning to treat them too.

Full Article: https://tinyurl.com/2p86ar2w

Article Source: The Economist

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ;
05/05/2022

Wearable Technology Promises to Revolutionise Health Care

Article Excerpt: It is a stealthy killer. When the heart’s chambers beat out of sync, blood pools and clots may form. Atrial fibrillation causes a quarter of more than 100,000 strokes in Britain each year. Most of those would never happen if the heart arrhythmia were treated, but first it has to be found. Tests are costly and inaccurate, but Apple Watches, and soon Fitbits, can detect it, are far cheaper and can save those whose lives are in danger. This is just one example of the revolution about to transform medicine. Smartwatches and -rings, fitness trackers and a rapidly growing array of electronically enhanced straps, patches and other “wearables” can record over 7,500 physiological and behavioural variables. Some of them are more useful than others, obviously, but, as our Technology Quarterly in this issue explains, machine learning can filter a torrent of data to reveal a continuous, quantified picture of you and your health.

Full Article: https://tinyurl.com/mrxbet73

Article Source: The Economist

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ;
05/04/2022

AI Must Be Developed Responsibly to Improve Mental Health Outcomes

Article Excerpt: For years, artificial intelligence has been touted as a potential game-changer for healthcare in the United States. Over a decade since the HITECH Act incentivized hospital systems to use electronic health records (EHR) for patient data management, there has been an explosion in the amount of healthcare data generated, stored, and available to drive insights and clinical decision-making. The motivation to integrate AI into mental health services has grown during the pandemic. The Kaiser Family Foundation reported an increase in adults experiencing symptoms of anxiety and depression, from 1 in 10 adults pre-pandemic to 4 in 10 adults in early 2021. Coupled with a national shortage of mental health professionals as well as limited opportunities for in-person mental health support, AI-powered tools could be used as an entry point to care by automatically and remotely measuring and intervening to reduce mental health symptoms.

Full Article: https://tinyurl.com/22e7rc4s

Article Source: Fast Company

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ; ;
04/11/2022

Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers

Haroz E, Grubin F, Goklish N, Pioche S, Cwik M, Barlow A, Waugh E, Usher J, Lenert MC, Walsh CG. Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers. JMIR Public Health Surveill 2021;7(9):e24377. DOI: 10.2196/24377

Use of algorithms can be helpful as a method of risk detection for suicide. Researchers developed a machine learning algorithm to help identify people who are most at risk for suicide deaths or attempts in Native American reservation populations. Researchers explored how to implement the algorithm tool to inform care pathways in community-based suicide surveillance and case management systems. Researchers conducted qualitative in-depth interviews with 9 case managers from 3 communities (White Mountain Apache Tribe and two sites in Navajo Nation). Interviews included questions about staff perceptions and evaluation and response to risk as well as suggestions for implementation of risk algorithms into their care process. Participants highlighted the importance of current behavior, past history, and location to prioritize individuals. Participants agreed that algorithm-generated risk flags would be useful along with as much information as possible to respond to the flag. Researchers are now conducting an implementation pilot of the algorithm tool in the White Mountain Apache Tribe that flags people as high-risk or low-risk after an in-person follow-up.

Posted in: Cutting Edge Literature // Tagged: ; ; ; ;
03/03/2022

New MIT Technique Aims to Boost Fairness Within Machine-Learning Models

Article Excerpt:  Researchers from MIT acknowledged that many machine-learning models were created using skewed data, causing them to produce uneven results… Using the deep metric learning technique, researchers trained the neural network to recognize photos that are similar and different with regard to facial recognition and skin tone. During this process, researchers uncovered more information about why the previous models produced unfair results. They found that two people with a lighter skin tone were more likely to be differentiated than two people with a darker skin tone. Also, if models are trained taking into consideration the majority group instead of the minority group, it would cause bias.

Full Article: https://tinyurl.com/2p8eddan

Article Source: Health IT Analytics

Posted in: Uncategorized // Tagged: ; ; ; ; ;
01/23/2022

New Predictive Computer Program Could Help Detect Individuals at High Risk of Depression

Article Excerpt: A team of scientists from Nanyang Technological University, Singapore (NTU Singapore) has developed a predictive computer program that could be used to detect individuals who are at increased risk of depression. In trials using data from groups of depressed and healthy participants, the program achieved an accuracy of 80 per cent in detecting those individuals with a high risk of depression and those with no risk.

Full Article: https://tinyurl.com/mvtmbc55

Article Source: News Medical

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ; ; ;
12/13/2021

Smartwatch to Detect Opioid Cravings, Offer Interventions

Article Excerpt: In what could prevent opioid users from misusing drugs, experts are working on a project to develop smartwatches with the ability to detect emotional and psychological patterns opioid users show hours before indulging in substance abuse. The project is funded by the National Science Foundation’s Smart and Connected Health program, an independent agency funded by the United States government. The University of Massachusetts recently announced that “a research team … has received a $1.1 million grant to further develop a smartwatch sensor designed to support the long-term recovery of people with opioid use disorder (OUD).”

Full Article: https://tinyurl.com/yckn3w2y

Article Source: TRT World

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ; ;