IMPORTANCE Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. OBJECTIVE To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. DESIGN, SETTING, AND PARTICIPANTS This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. EXPOSURES Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). MAIN OUTCOMES AND MEASURES The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. RESULTS The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (continued) Key Points Question Can machine learning models predict improvement of various depressive symptoms with antidepressant treatment based on pretreatment symptom scores and electroencephalographic measures? Findings In this prognostic study, using the machine learning approach of gradient-boosted decision trees, the ElecTreeScore algorithm could reliably distinguish the patients who r...
Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to flawed systems that perpetuate gender and racial biases, and limit how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by promoting diversity and inclusion in AI. The program targets high school students from underrepresented backgrounds in AI and gives them a chance to learn about AI with a focus on biomedicine. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at our final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There was also a nominally significant increase in the students' knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater inclusion in AI.
Ethical, legal, and social implications (ELSI) research was introduced in the 1990s as part of an effort to carry out responsible research in human genetics and genomics. Since then it has become a common practice associated with large-scale research in the life sciences. This article argues that ELSI research that is suitable for Korea society should (a) focus on high-priority and specific issues, (b) relate directly to ongoing scientific research, and (c) be informed by a broad range of academic disciplines, including not only law and ethics, but also economics, anthropology and other areas of the humanities and social sciences. It is an essential task of ELSI researchers in Korea to create an environment in which top-down and bottom-up research can be combined to reflect the perspectives of variety of groups and perspectives. A further objective of ELSI research is to establish a research infrastructure to facilitate communication between researchers, the public, the government, and other stakeholders.
The use of artificial intelligence (AI) in healthcare settings has become increasingly common. Many hope that AI will remove constraints on human and material resources and bring innovations in diagnosis and treatment. However, the deep learning techniques and resulting black box problem of AI raise important ethical concerns. To address these concerns, this article explores some of the relevant ethical domains, issues, and themes in this area and proposes principles to guide use of AI in healthcare. Three ethical themes are identified, including respect for person, accountability, and sustainability, which correspond to the three domains of data acquisition, clinical setting, and social environment. These themes and domains were schematized with detailed explanations of relevant ethical issues, concepts, and applications, such as explainability and accountability. Additionally, it is argued that conflicts between ethical principles should be resolved through deliberative democratic methods and a consensus building process.
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