Additional file 2: of A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population

Evaluation of the number of clusters. (DOCX 612 kb

Additional file 4 of From free text to clusters of content in health records: an unsupervised graph partitioning approach

Effect of the sparsification. (PDF 123 kb

Additional file 2 of From free text to clusters of content in health records: an unsupervised graph partitioning approach

Word cloud and Sankey diagram for the 17 community partition. (PDF 169 kb

Additional file 3 of From free text to clusters of content in health records: an unsupervised graph partitioning approach

Effect of the corpus size. (PDF 107 kb

Additional file 1 of From free text to clusters of content in health records: an unsupervised graph partitioning approach

Word clouds for the 44 community partition. (PDF 209 kb

Additional file 5 of From free text to clusters of content in health records: an unsupervised graph partitioning approach

Comparison with other clustering methods. (PDF 103 kb

sj-docx-1-jtt-10.1177_1357633X211066235 - Supplemental material for Use and impact of virtual primary care on quality and safety: The public's perspectives during the COVID-19 pandemic

Supplemental material, sj-docx-1-jtt-10.1177_1357633X211066235 for Use and impact of virtual primary care on quality and safety: The public's perspectives during the COVID-19 pandemic by Ana Luisa Neves, Jackie van Dael, Niki O’Brien, Kelsey Flott, Saira Ghafur, Ara Darzi and Erik Mayer in Journal of Telemedicine and Telecare</p

Data_Sheet_1_Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.PDF

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.</p

Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy

No description supplie

Appraising the relevance of DNA copy number loss and gain in prostate cancer using whole genome DNA sequence data

A variety of models have been proposed to explain regions of recurrent somatic copy number alteration (SCNA) in human cancer. Our study employs Whole Genome DNA Sequence (WGS) data from tumor samples (n = 103) to comprehensively assess the role of the Knudson two hit genetic model in SCNA generation in prostate cancer. 64 recurrent regions of loss and gain were detected, of which 28 were novel, including regions of loss with more than 15% frequency at Chr4p15.2-p15.1 (15.53%), Chr6q27 (16.50%) and Chr18q12.3 (17.48%). Comprehensive mutation screens of genes, lincRNA encoding sequences, control regions and conserved domains within SCNAs demonstrated that a two-hit genetic model was supported in only a minor proportion of recurrent SCNA losses examined (15/40). We found that recurrent breakpoints and regions of inversion often occur within Knudson model SCNAs, leading to the identification of ZNF292 as a target gene for the deletion at 6q14.3-q15 and NKX3.1 as a two-hit target at 8p21.3-p21.2. The importance of alterations of lincRNA sequences was illustrated by the identification of a novel mutational hotspot at the KCCAT42, FENDRR, CAT1886 and STCAT2 loci at the 16q23.1-q24.3 loss. Our data confirm that the burden of SCNAs is predictive of biochemical recurrence, define nine individual regions that are associated with relapse, and highlight the possible importance of ion channel and G-protein coupled-receptor (GPCR) pathways in cancer development. We concluded that a two-hit genetic model accounts for about one third of SCNA indicating that mechanisms, such haploinsufficiency and epigenetic inactivation, account for the remaining SCNA losses