Publications by authors named "D I Bates"
Bacteria can be engineered to manufacture chemicals, but it is unclear how to optimally engineer a single cell to maximise production performance from batch cultures. Moreover, the performance of engineered production pathways is affected by competition for the host's native resources. Here, using a 'host-aware' computational framework which captures competition for both metabolic and gene expression resources, we uncover design principles for engineering the expression of host and production enzymes at the cell level which maximise volumetric productivity and yield from batch cultures.
View Article and Find Full Text PDFBackground: Adverse drug events (ADEs) are understudied in the ambulatory care setting. We aim to estimate the prevalence and characteristics of ADEs in outpatient care using electronic health records (EHRs).
Methods: This cross-sectional study included EHR data for patients who had an outpatient encounter at an academic medical center from 1 October 2018 through 31 December 2019.
Article Synopsis
- The systematic review investigates users' perceptions of AI-enabled decision aids that help facilitate shared decision-making between patients and clinicians with personalized recommendations.* -
- The study analyzed 26 articles which found that patients generally found these decision aids easy to use and helpful, enhancing their commitment to treatment.* -
- However, clinicians raised concerns regarding the accuracy and recency of the information provided, as well as the potential risks of over- or under-treatment, alongside recognizing challenges and biases that need to be managed.*
Objectives: To estimate the frequency, severity, and preventability of adverse events associated with perioperative care, and to describe the setting and professions concerned.
Design: Multicenter retrospective cohort study.
Setting: 11 US hospitals.
Background: Distinguishing between mild cognitive impairment (MCI) and early dementia requires both neuropsychological and functional assessment that often relies on caregivers' insights. Contacting a patient's caregiver can be time-consuming in a physician's already-filled workday.
Objective: To assess the utility of a brief, machine learning (ML)-enabled digital cognitive assessment, the Digital Clock and Recall (DCR), for detecting functional dependence.