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Tag: diagnosis
10/27/2022

Personalising Mental Health Care

Article Excerpt: Although researchers have made unprecedented progress in identifying ‘averaged’ or ‘population-level’ mechanisms of mental health disorders, these approaches have led to a drowning effect at an individual level where person-specific information is often lost if it doesn’t align with an averaged expectation. To bridge this gap between research and clinical practice, we have developed a novel individualised machine learning framework called Affinity Scores. By identifying personalised signatures that can be integrated into a clinician’s decision-making for each of their patients, Affinity Scores represent a fundamental shift in our approach to personalised psychiatry.

Full Article: https://tinyurl.com/mrcn2ayh

Article Source: Pursuit

Posted in: Emerging Technologies // Tagged: ; ; ; ; ;
10/17/2022

Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges

El-Nahal W, Grader-Beck T, Gebo K, Holmes E, Herne K, Moore R, Thompson D, Berry S. Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges. BMJ Health Care Inform 2022;29:e100521. doi:10.1136/bmjhci-2021-100521

An electronic medical record (EMR) alert system was developed to use readily available data elements to accurately identify hospitalized people with HIV. Authors described the design and implementation of the EMR alert and methods to evaluate its accuracy for identifying people with HIV. Over 24 months, the EMR alert was used to notify an intervention team and data abstraction team in real time about admissions of people with HIV. Sensitivity was assessed by comparing the machine-learning alert system to manual chart reviews. Positive predictive value (probability that a patient with a positive test result actually has the disease), was assessed by false positives identified in chart review (not having HIV despite alert triggering). Results demonstrated high sensitivity (sensitivity=100%, 95% CI 82-100%) and good predictive value (84%, 95% CI 82-86%). A combination of data (diagnosis, prescriptions, and lab orders) in the EMR alert system achieved high sensitivity and positive predictive value in identifying people with HIV. ICD Code diagnoses were the strongest contributors to predictive value, compared to the other criteria. Use of data-driven alerts in electronic health record systems can facilitate the deployment of multidisciplinary teams for medication review, education, case management, and outpatient linkage to follow-up.

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: ; ; ; ; ; ; ; ;
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: ; ; ; ; ; ; ; ;
03/22/2022

Artificial Intelligence May Help to Classify Colorectal Polyps

Article Excerpt: Researchers at the Geisel School of Medicine at Dartmouth College in Hanover, New Hampshire, recruited 15 pathologists to analyze and classify specimens of the four most common types of colorectal polyps from 100 slides. In one session, about half the doctors used the AI-augmented digital system while the others used just a microscope. After a 12-week break, each group switched techniques and read the same slides again. The pathologists accurately classified the samples 73.9% of the time when using just a microscope and 80.8% when they used the AI-augmented digital system… “This is about helping pathologists make this subtype classification more accurately and more efficiently,” says Saeed Hassanpour, an associate professor at the Geisel School of Medicine who was the senior author of the study.

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

Article Source: Cancer Today Magazine

Posted in: Methodologies & Analytics // Tagged: ; ; ; ; ; ;
03/02/2022

Doctors Using AI, Supercomputer To Predict And Prevent 50% Of Mental Illness

Article Excerpt: Almost a billion people globally suffer from some form of mental illness. But doctors from Cincinnati Children’s Hospital are using artificial intelligence and the world’s second most powerful supercomputer for early diagnosis. And that, they say, can make all the difference. “If we can identify this early … we can treat for and alleviate almost 50% of the mental illness that goes into adulthood,” Dr. John Pestian told me recently on the TechFirst podcast. “So catching it young, catching it early, and giving care is a very important part.”

Full Article: https://tinyurl.com/bddp425e

Article Source: Forbes

Posted in: Emerging Technologies // 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: ; ; ; ; ; ; ; ; ; ;
01/11/2022

Patient-Focused Technology Improving Women’s Healthcare

Article Excerpt: One of the most significant advances in patient-focused technology has been the advent of telehealth technologies. From the proliferation of wearable devices that allow round-the-clock patient monitoring to the rise in telemedicine, offering on-demand virtual consultations with healthcare providers, patients have greater access than ever before to the care, and the caregivers, they need. For women, this increased access to care through remote health technologies has profound implications for their overall quality of care. First, studies have shown that women are at significantly higher risk of being misdiagnosed or of failing to receive a timely diagnosis due to unconscious gender biases and the general lack of training in women’s health. With the dawn of wearable health tech and the integration of artificial intelligence (AI) systems into the care regimen, women are receiving faster and more accurate diagnoses than they would likely have been given in a brief clinical encounter.

Full Article: https://tinyurl.com/4x4m3t8d

Article Source: Innovation & Tech Today

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ; ; ; ; ;
11/09/2021

New AI Model Accurately Classifies Colorectal Polyps Using Slides from 24 Institutions

Article Excerpt: An artificial intelligence (AI) model for automated classification of colorectal polyps could benefit cancer screening programs by improving efficiency, reproducibility, and accuracy, as well as reducing access barriers to pathological services. In a new study out of Dartmouth’s and Dartmouth-Hitchcock’s Norris Cotton Cancer Center, a computer science and clinical research team led by Saeed Hassanpour, PhD, trained a deep neural network to do just that. Not only can their model distinguish the four major types of colorectal polyps at the level of practicing pathologists, as evaluated on a dataset across multiple external institutions, but also proves that a model designed using data from a single institution can achieve high accuracy on outside data.

Full Article: https://tinyurl.com/tduukm9b

Article Source: Dartmouth Cancer Center News

Posted in: Methodologies & Analytics // Tagged: ; ; ; ; ; ; ;