Wei Ying Tan

Email Address
idstwy@nus.edu.sg


Organizational Units
OrgUnit Logo
Organizational Unit
OrgUnit Logo
Organizational Unit
OrgUnit Logo
Organizational Unit
UNIV ADMIN
faculty
OrgUnit Logo
Organizational Unit

Filters

2021 - 20232
Reset filters

Settings

Citations

Publication Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Publication
    Patient similarity analytics for explainable clinical risk prediction
    (BioMed Central Ltd, 2021-07-01) Fang, Hao Sen Andrew; Tan, Ngiap Chuan; Tan, Wei Ying; Oei, Ronald Wihal; Lee, Mong Li; Hsu, Wynne; SPECIALITY RESEARCH INSTITUTES/CENTRES; INSTITUTE OF DATA SCIENCE
    Background: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods: The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results: The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions: Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice. © 2021, The Author(s).
  • No Thumbnail Available
    Publication
    A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data
    (IOS PRESS, 2023-01-01) Tan, Wei Ying; Hargreaves, Carol; Chen, Christopher; Hilal, Saima; Assoc Prof Carol Anne Hargreaves; PHARMACOLOGY; INSTITUTE OF DATA SCIENCE; STATISTICS & APPLIED PROBABILITY; SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
    Background: The major mechanisms of dementia and cognitive impairment are vascular and neurodegenerative processes. Early diagnosis of cognitive impairment can facilitate timely interventions to mitigate progression. Objective: This study aims to develop a reliable machine learning (ML) model using socio-demographics, vascular risk factors, and structural neuroimaging markers for early diagnosis of cognitive impairment in a multi-ethnic Asian population. Methods: The study consisted of 911 participants from the Epidemiology of Dementia in Singapore study (aged 60-88 years, 49.6% male). Three ML classifiers, logistic regression, support vector machine, and gradient boosting machine, were developed. Prediction results of independent classifiers were combined in a final ensemble model. Model performances were evaluated on test data using F1 score and area under the receiver operating curve (AUC) methods. Post modelling, SHapely Additive exPlanation (SHAP) was applied on the prediction results to identify the predictors that contribute most to the cognitive impairment prediction. Findings: The final ensemble model achieved a F1 score and AUC of 0.87 and 0.80 respectively. Accuracy (0.83), sensitivity (0.86), specificity (0.74) and predictive values (positive 0.88 negative 0.72) of the ensemble model were higher compared to the independent classifiers. Age, ethnicity, highest education attainment and neuroimaging markers were identified as important predictors of cognitive impairment. Conclusion: This study demonstrates the feasibility of using ML tools to integrate multiple domains of data for reliable diagnosis of early cognitive impairment. The ML model uses easy-To-obtain variables and is scalable for screening individuals with a high risk of developing dementia in a population-based setting.