Primoz Kocbek

Orcid: 0000-0002-9064-5085

According to our database1, Primoz Kocbek authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare.
CoRR, 2024

2023
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI.
CoRR, 2023

2022
Relevance of automated generated short summaries of scientific abstract: use case scenario in healthcare.
Proceedings of the 10th IEEE International Conference on Healthcare Informatics, 2022

Generating Extremely Short Summaries from the Scientific Literature to Support Decisions in Primary Healthcare: A Human Evaluation Study.
Proceedings of the Artificial Intelligence in Medicine, 2022

2021
A Review of Mortality Risk Prediction Models in Smartphone Applications.
J. Medical Syst., 2021

2020
Interpretability of machine learning-based prediction models in healthcare.
WIREs Data Mining Knowl. Discov., 2020

Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test.
CoRR, 2020

Evaluation of Mobile Phone Mortality Risk Score Applications Using Data from the Electronic Medical Records.
Proceedings of the Digital Personalized Health and Medicine - Proceedings of MIE 2020, Medical Informatics Europe, Geneva, Switzerland, April 28, 2020

2019
Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care.
Health Informatics J., 2019

Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data.
Comput. Math. Methods Medicine, 2019

Using (Automated) Machine Learning and Drug Prescription Records to Predict Mortality and Polypharmacy in Older Type 2 Diabetes Mellitus Patients.
Proceedings of the Neural Information Processing - 26th International Conference, 2019

Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening.
Proceedings of the Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems, 2019


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