Doctor XAI: an ontology-based approach to black-box sequential data classification explanations
Several recent advancements in Machine Learning involve blackbox models: algorithms that
do not provide human-understandable explanations in support of their decisions. This …
do not provide human-understandable explanations in support of their decisions. This …
Co-design of human-centered, explainable AI for clinical decision support
eXplainable AI (XAI) involves two intertwined but separate challenges: the development of
techniques to extract explanations from black-box AI models and the way such explanations …
techniques to extract explanations from black-box AI models and the way such explanations …
A novel hybrid recommender system for the tourism domain
In this paper, we develop a novel hybrid recommender system for the tourism domain, which
combines (a) a Bayesian preferences elicitation component which operates by asking the …
combines (a) a Bayesian preferences elicitation component which operates by asking the …
[HTML][HTML] A patient-similarity-based model for diagnostic prediction
Objective To simulate the clinical reasoning of doctors, retrieve analogous patients of an
index patient automatically and predict diagnoses by the similar/dissimilar patients. Methods …
index patient automatically and predict diagnoses by the similar/dissimilar patients. Methods …
Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity
Background Many clinical concepts are standardized under a categorical and hierarchical
taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into …
taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into …
Graph-guided deep hashing networks for similar patient retrieval
Y Gu, X Yang, M Sun, C Wang, H Yang, C Yang… - Computers in Biology …, 2024 - Elsevier
With the rapid growth and widespread application of electronic health records (EHRs),
similar patient retrieval has become an important task for downstream clinical decision …
similar patient retrieval has become an important task for downstream clinical decision …
Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records
N Wang, Y Huang, H Liu, Z Zhang, L Wei, X Fei… - BMC medical informatics …, 2021 - Springer
Background A new learning-based patient similarity measurement was proposed to
measure patients' similarity for heterogeneous electronic medical records (EMRs) data …
measure patients' similarity for heterogeneous electronic medical records (EMRs) data …
[HTML][HTML] Structure-aware siamese graph neural networks for encounter-level patient similarity learning
Y Gu, X Yang, L Tian, H Yang, J Lv, C Yang… - Journal of Biomedical …, 2022 - Elsevier
Patient similarity learning has attracted great research interest in biomedical informatics.
Correctly identifying the similarity between a given patient and patient records in the …
Correctly identifying the similarity between a given patient and patient records in the …
The k-means clustering algorithm with semantic similarity to estimate the cost of hospitalization
The cost of hospitalization from a patient can be estimated by performing a cluster of patient.
One of the algorithms that is widely used for clustering is K-means. K-means algorithm …
One of the algorithms that is widely used for clustering is K-means. K-means algorithm …
Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records
N Wang, Y Huang, H Liu, X Fei, L Wei, X Zhao… - Biomedical engineering …, 2019 - Springer
Background Conventional risk prediction techniques may not be the most suitable approach
for personalized prediction for individual patients. Therefore, individualized predictive …
for personalized prediction for individual patients. Therefore, individualized predictive …