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01/31/2023

PTSD Study Uses Precision Medicine Tech from AiCure

Article Excerpt: AiCure’s AI-powered digital biomarker solution enables remote detection of subtle changes in a patient’s health status and response to treatment by capturing audio and visual data between clinic visits. Accessed through AiCure’s Patient Connect application, patients use their smartphone’s front-facing camera to complete brief assessments. AiCure’s algorithm then analyzes behavior, such as emotional expressivity, physical movement and speech patterns. By frequently aggregating these sensitive, objective insights, AiCure empowers pharmaceutical companies to improve their understanding of the disease and treatment side effects, elevating the integrity of their trial data, and optimizing patient outcomes.

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

Article Source: Applied Clinical Trials

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ;
10/31/2022

Leveraging a Technology Accelerator to Drive Addiction Treatment Success

Article Excerpt: Effective treatments that lead to improved patient outcomes are what clinicians strive to provide for their patients across healthcare settings and specialties. But there may be many hurdles to treatment success depending on the condition, type of care required, or the way that patient data is used. This is especially true in behavioral health and addiction treatment, where finding and leveraging the drivers of treatment success can be hampered by limited technology and data analytics capabilities… The pervasiveness of SUD, along with the need for individualized treatments, can make it difficult for treatment centers and providers to gain insights into treatment success and improve practices. To meet this challenge, Cumberland Heights, a Tennessee-based drug and alcohol addiction treatment facility with 350 employees, 2,500 patients annually, and 20 locations, turned to a ‘technology accelerator’ platform. Nick Hayes, PhD, chief science officer at Cumberland Heights, sat down with HealthITAnalytics to discuss how the organization uses the cloud-based EMR software to better understand the ways in which unique patient predictors lead to better patient outcomes.

Full Article: https://tinyurl.com/2p9dn3zh

Article Source: Health IT Analytics

Posted in: Methodologies & Analytics // Tagged: ; ; ; ; ; ;
09/30/2022

Can Smartphones Help Predict Suicide?

Article Excerpt: A unique research project is tracking hundreds of people at risk for suicide, using data from smartphones and wearable biosensors to identify periods of high danger — and intervene… In the field of mental health, few new areas generate as much excitement as machine learning, which uses computer algorithms to better predict human behavior. There is, at the same time, exploding interest in biosensors that can track a person’s mood in real time, factoring in music choices, social media posts, facial expression and vocal expression.

Full Article: https://tinyurl.com/4ay346zn

Article Source: The New York Times

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ;
08/15/2022

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR Mhealth Uhealth 2022;10(8):e33850. DOI: 10.2196/33850

This study aimed to develop and evaluate a machine learning model to predict next-day physiological and prenatal stress by collecting sensor heart rate data and ecological momentary assessment (EMA) questionnaires. This study applied an explainability model for the prediction results. A total of 16 adult pregnant women from an obstetrics and gynecology clinic were enrolled in the study. Participants received a 12-week cognitive behavioral therapy intervention and wore a mobile electrocardiography (heart rate) sensor for 12 weeks. Participants completed EMAs for perceived stress on their mobile phones 5 times a day for 12 weeks. In total, about 4000 hours of data were collected and participants completed 2800 EMAs. Researchers used these data to train and evaluate 6 different machine learning models to select the best performing model for predicting next-day physiological and perceived stress. The random forest classifier performed the best for both physiological and perceived stress, with an average F1 score (a commonly used evaluation metric) of 81.9% and 72.5%, respectively. Two features significantly predicted both physiological and perceived stress: feeling unable to overcome difficulties and participants’ number of children. Results demonstrated that a machine learning model can predict next-day physiological and perceived stress among pregnant women. Future studies should validate the model with a larger sample size.

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ; ;
07/25/2022

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

Megerian JT, Dey S, Melmed RD, Coury DL, Lerner M, Nicholls CJ, Sohl K, Rouhbakhsh R, Narasimhan A, Romain J, Golla S, Shareef S, Ostrovsky A, Shannon J, Kraft C, Liu-Mayo S, Abbas H, Gal-Szabo DE, Wall DP, & Taraman S (2022). Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder. NPJ Digital Medicine, 5(1), 57–57. https://doi.org/10.1038/s41746-022-00598-6

Researchers conducted a double-blinded, multi-site, active comparator cohort study to test the accuracy of artificial intelligence software for diagnosing autism spectrum disorder (ASD). The software device collects data about child behavioral features from 3 sources (caregiver questionnaire, analysis of two short 1 minute home videos recorded and uploaded by the child’s caregiver, provider questionnaire). Data are processed using a machine learning algorithm to indicate whether a person is ASD positive, ASD negative, or inconclusive (i.e., inputted data are not sufficient for a predictive output). Researchers evaluated the software in a study with 425 children aged 18-72 months for whom a caregiver or provider had a concern about developmental delay. Researchers compared the software outputs to the clinical standard (diagnosis made by a provider based on DSM-5 criteria). Results demonstrated that data collection with the software device took less time to administer and require less specialty training relative to clinical standard process. For about 33% of the sample, the algorithm output supported accurate diagnoses compared with clinical evaluation. Of the children for whom the software algorithm made a definite evaluation, 98.4% with clinically diagnosed ASD received an ASD positive result and 78.9% without a clinical diagnosis of ASD received an ASD negative result. All children who received a false-positive result (n=15) had a non-ASD developmental condition. Only one child received a false negative result in this study. Overall, this machine learning tool demonstrated high sensitivity and good specificity for diagnosing ASD. The tool can potentially expand the ability to effectively diagnose children with ASD in primary care to facilitate early intervention and more efficient use of specialist resources.

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ; ; ; ;
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: ; ; ; ; ;
05/04/2022

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: ; ; ; ;