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SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator.
Comput Biol Med
December 2024
Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Medicine, College of Medicine, Korea University, Seoul, 02841, Republic of Korea. Electronic address:
Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents, potentially compromising data quality. To address this, machine learning-based shortened questionnaires have been developed. While these questionnaires possess high levels of accuracy, their practical use in clinical settings is hindered by a lack of transparency and the need for specialized machine learning expertise.
View Article and Find Full Text PDFThe Awards for Distinguished Contributions to Psychology in the Public Interest recognize persons who have advanced psychology as a science and/or profession by a single extraordinary achievement or a lifetime of outstanding contributions in the public interest. Nancy M. Sidun, PsyD, ABPP, ATR, is a clinical psychologist, art therapist, educator, administrator, and scholar.
View Article and Find Full Text PDFOverview of Alzheimer's Disease Neuroimaging Initiative and future clinical trials.
Alzheimers Dement
December 2024
Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA.
The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to optimize and validate biomarkers for clinical trials while sharing all data and biofluid samples with the global scientific community. ADNI has been instrumental in standardizing and validating amyloid beta (Aβ) and tau positron emission tomography (PET) imaging. ADNI data were used for the US Food and Drug Administration (FDA) approval of the Fujirebio and Roche Elecsys cerebrospinal fluid diagnostic tests.
View Article and Find Full Text PDFTackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.
Lancet Digit Health
January 2025
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK; Centre for Patient Reported Outcomes Research, School of Health Sciences, College of Medical and Dental Sciences, Birmingham, UK; University of Birmingham, Birmingham, UK. Electronic address:
Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups.
View Article and Find Full Text PDFSex difference in alexithymia and cognitive impairment in chronic schizophrenia: a large sample study based on Chinese Han population.
Eur Arch Psychiatry Clin Neurosci
December 2024
CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Hai-Dian District, Beijing, 100101, China.
Sex differences in schizophrenia have been noted across domains such as alexithymia and cognitive function; however, how they interact remains unclear. This study aimed to explore sex differences in the relationship between alexithymia and cognitive function in patients with chronic schizophrenia. A total of 695 patients (464 males and 231 females) who met the DSM-IV diagnostic criteria for schizophrenia were recruited in this cross-sectional study.
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