Integrating case-based reasoning, knowledge-based approach and Dijkstra algorithm for route finding

Proceedings of the Conference on Artificial Intelligence Applications149-155PCAA

Finding the shortest route using cases, knowledge, and Dijkstra's algorithm

10.1109/64.331478IEEE expert957-11IEEX

Towards human–machine collaboration in creating an evaluation corpus for adverse drug events in discharge summaries of electronic medical records

10.1016/j.bdr.2016.04.001Big Data Research437-4

Chromosomal breaks at FRA18C: association with reduced DOK6 expression, altered oncogenic signaling and increased gastric cancer survival

10.1038/s41698-017-0012-3NPJ PRECISION ONCOLOGY1

Retrospective Study of Reported Adverse Events Due to Complementary Health Products in Singapore From 2010 to 2016

The objective of this study is to collate and analyse adverse event reports associated with the use of complementary health products (CHP) submitted to the Health Sciences Authority (HSA) of Singapore for the period 2010–2016 to identify various trends and signals for pharmacovigilance purposes. A total of 147,215 adverse event reports suspected to be associated with pharmaceutical products and CHP were received by HSA between 2010 and 2016. Of these, 143,191 (97.3%) were associated with chemical drugs, 1,807 (1.2%) with vaccines, 1,324 (0.9%) with biological drugs (biologics), and 893 (0.6%) with CHP. The number of adverse event reports associated with Chinese Proprietary Medicine, other complementary medicine and health supplements are presented. Eight hundred and ninety three adverse event reports associated with CHP in the 7-year period have been successfully collated and analyzed. In agreement with other studies, adverse events related to the “skin and appendages disorders” were the most commonly reported. Most of the cases involved dermal allergies (e.g., rashes) associated with the use of glucosamine products and most of the adulterated products were associated with the illegal addition of undeclared drugs for pain relief. Dexamethasone, chlorpheniramine, and piroxicam were the most common adulterants detected. Reporting suspected adverse events is strongly encouraged even if the causality is not confirmed because any signs of clustering will allow rapid regulatory actions to be taken. The findings from this study help to create greater awareness on the health risks, albeit low, when consuming CHP and dispelling the common misconception that “natural” means “safe.” In particular, healthcare professionals and the general public should be aware of potential adulteration of CHP. The analysis of spontaneously reported adverse events is an important surveillance system in monitoring the safety of CHP and helps in the understanding of the risk associated with the use of such products. Greater collaboration and communication between healthcare professionals, regulators, patients, manufacturers, researchers, and the general public are important to ensure the quality and safety of CHP

Pioneering Arterial Hypertension Phenotyping on Nationally Aggregated Electronic Health Records

Background: Hypertension is frequently studied in epidemiological studies that have been conducted using retrospective observational data, either as an outcome or a variable. However, there are few validation studies investigating the accuracy of hypertension phenotyping algorithms in aggregated electronic health record (EHR) data. Methods: Utilizing a centralized repository of inpatient EHR data from Singapore for the period of 2019–2020, a new algorithm that incorporates both diagnostic codes and medication details (Diag+Med) was devised. This algorithm was intended to supplement and improve the diagnostic code-only model (Diag-Only) for the classification of hypertension. We computed various metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) to assess the algorithm’s effectiveness in identifying hypertension on 2813 chart-reviewed records. This pool was composed of two patient cohorts: a random sampling of all inpatient admissions (Random Cohort) and a targeted group with atrial fibrillation diagnoses (AF Cohort). Results: The Diag+Med algorithm was more sensitive at detecting hypertension patients in both cohorts compared to the Diag-Only algorithm (83.8 and 87.6% vs. 68.2 and 66.5% in the Random and AF Cohorts, respectively). These improvements in sensitivity came at minimal costs in terms of PPV reductions (88.2 and 90.3% vs. 91.4 and 94.2%, respectively). Conclusion: The combined use of diagnosis codes and specific antihypertension medication exposure patterns facilitates a more accurate capture of patients with hypertension in a database of aggregated EHRs from diverse healthcare institutions in Singapore. The results presented here allow for the bias correction of risk estimates derived from observational studies involving hypertension

Phenotyping Diabetes Mellitus on Aggregated Electronic Health Records from Disparate Health Systems

Background: Identifying patients with diabetes mellitus (DM) is often performed in epidemiological studies using electronic health records (EHR), but currently available algorithms have features that limit their generalizability. Methods: We developed a rule-based algorithm to determine DM status using the nationally aggregated EHR database. The algorithm was validated on two chart-reviewed samples (n = 2813) of (a) patients with atrial fibrillation (AF, n = 1194) and (b) randomly sampled hospitalized patients (n = 1619). Results: DM diagnosis codes alone resulted in a sensitivity of 77.0% and 83.4% in the AF and random hospitalized samples, respectively. The proposed algorithm combines blood glucose values and DM medication usage with diagnostic codes and exhibits sensitivities between 96.9% and 98.0%, while positive predictive values (PPV) ranged between 61.1% and 75.6%. Performances were comparable across sexes, but a lower specificity was observed in younger patients (below 65 versus 65 and above) in both validation samples (75.8% vs. 90.8% and 60.6% vs. 88.8%). The algorithm was robust for missing laboratory data but not for missing medication data. Conclusions: In this nationwide EHR database analysis, an algorithm for identifying patients with DM has been developed and validated. The algorithm supports quantitative bias analyses in future studies involving EHR-based DM studies

Using the consolidated framework for implementation research to guide a pilot of implementing an institution level patient informed consent process for clinical research at an outpatient setting

Abstract Background In Singapore, research teams seek informed patient consent on an ad hoc basis for specific clinical studies and there is typically a role separation between operational and research staff. With the enactment of the Human Biomedical Research Act, there is increased emphasis on compliance with consent-taking processes and research documentation. To optimize resource use and facilitate long-term research sustainability at our institution, this study aimed to design and pilot an institution level informed consent workflow (the “intervention”) that is integrated with clinic operations. Methods We used the Consolidated Framework for Implementation Research (CFIR) as the underpinning theoretical framework and conducted the study in three stages: Stage 1, CFIR constructs were used to systematically identify barriers and facilitators of intervention implementation, and a simple time-and-motion study of the patient journey was used to inform the design of the intervention; Stage 2, implementation strategies were selected and mapped to the Expert Recommendations for Implementing Change (ERIC) taxonomy; Stage 3, we piloted and adapted the implementation process at two outpatient clinics and evaluated implementation effectiveness through patient participation rates. Results We identified 15 relevant CFIR constructs. Implementation strategies selected to address these constructs were targeted at three groups of stakeholders: institution leadership (develop relationships, involve executive boards, identify and prepare champions), clinic management team (develop relationships, identify and prepare champions, obtain support and commitment, educate stakeholders), and clinic operations staff (develop relationships, assess readiness, conduct training, cyclical tests of change, model and simulate change, capture and share local knowledge, obtain and use feedback). Time-and-motion study in clinics identified the pre-consultation timepoint as the most appropriate for the intervention. The implementation process was adapted according to clinic operations staff and service needs. At the conclusion of the pilot, 78.3% of eligible patients provided institution level informed consent via the integrated workflow implemented. Conclusions Our findings support the feasibility of implementing an institution level informed consent workflow that integrates with service operations at the outpatient setting to optimize healthcare resources for research. The CFIR provided a useful framework to identify barriers and facilitators in the design of the intervention and its implementation process