Article Excerpt: Mental healthcare is on the verge of a significant transformation with algorithms emerging as potential allies in the treatment process. However, the inherent bias in generative AI poses a critical question: what are the implications for patient outcomes?
Full Article: https://tinyurl.com/ykx8j6u5
Article Source: Omnia Health
Article Excerpt: Co-authors Andrew Campbell, professor of computer science, HealthX Lab graduate students Subigya Nepal and Weichen Wang, and Jeremy Huckins, Department of Psychological and Brain Sciences won a Distinguished Paper Award at the 2023 ACM UbiComp Conference for their paper titled “GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling.” Eight out of 210 papers received the Distinguished Paper Award, presented at UbiComp and published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Volume 6).
Full Article: https://tinyurl.com/4h4xcrsf
Article Source: Dartmouth Computer Science News
Article Excerpt: The Center for Technology and Behavioral Health will host its second digital health summit on Oct. 25 at the Hanover Inn. The Clinically-Validated Digital Therapeutics: Innovations in Scientific Discovery, Clinical Applications, and Global Deployment event will gather experts from diverse sectors of the health care industry—researchers, providers, regulators, payers, and investors, as well as representatives from global pharma—to help shape a vision for making digital therapeutics accessible to all.
“The goal of the summit is to bring together a really broad group of stakeholders in the space of digital health, and have a shared dialogue about where are we at this moment in time and how we can work together to accelerate the pace at which we can get the most effective and most engaging tools into the hands of people all over the world,” says CTBH Director Lisa Marsch.
Full Article: https://tinyurl.com/mr267ccw
Article Source: Dartmouth News
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
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
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
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.
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.
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.