Podcast: Conversations in Science - October 2019
31 October 2019 | Author: Melissa Lewis-Brown, Chief Science Strategy Officer (Interim)
Our new podcasts, hosted by our Science Manager, Melissa Lewis-Brown, will be shedding light on different aspects of Science.
This month’s podcast draws out some intriguing themes from HDR UK’s first 100+ open access scientific publications. Publications specifically cited in this podcast include:
- Fusar-Poli et al 2019, Real World Implementation of a Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk of Psychosis in Clinical Routine: Study Protocol. Frontiers in Psychiatry, 10: 109
- Ramu et al 2019, Recorded poor insight as a predictor of service use outcomes: cohort study of patients with first-episode psychosis in a large mental healthcare database. BMJ Open, 9:1-7
- Velupillai et al 2019, Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior. Psychiatry, 10:36
- Hastie et al 2019, Antenatal exposure to solar radiation and learning disabilities: Population cohort study of 422,512 children. Scientific Reports, 9(9356):1-9 (please also see this case study)
- Fleming et al 2019, Educational and Health Outcomes of Children Treated for Type 1 Diabetes: Scotland-Wide Record Linkage Study of 766,047 Children. Diabetes Care, 42(9): 1700-1707 (please also see this news story)
- Aldridge et al 2019, Causes of death among homeless people: a population-based cross-sectional study of linked hospitalisation and mortality data in England. Wellcome open research, 4(49): 1-17 (please also see this case study)
- Kuan et al 2019, A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. The Lancet Digital Health, 1(2): e63-e77 (please also see this blog)
- Wheater et al 2019, A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records. BMC Medical Informatics and Decision Making, 19:184
- Liu et al 2019, A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health, 1(6): e271-e297
Health Data Research UK researchers develop innovative tools and technologies needed to unlock knowledge from complex and diverse health data, to address some of the biggest health challenges that...
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