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Kristof Depraetere

Kristof Depraetere
Dedalus HealthCare · clinalytix

Master of Science in Computer Science
Applying Machine Learning and Deep Learning in real commercially available medical software products.

About

19
Publications
3,560
Reads
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170
Citations
Citations since 2017
4 Research Items
101 Citations
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Introduction
As R&D lead of the clinalytix department at Dedalus Healthcare I take care of the people management, the software architecture, and lead the overal development of our software products and underlying machine learning and natural language processing frameworks. We develop state of the art production-grade predictive analytics services. The prediction models used by these prediction services are created using machine learning and deep learning.
Additional affiliations
February 2003 - November 2021
Dedalus HealthCare
Position
  • R&D Lead
Description
  • As R&D Lead of the clinalytix department at Dedalus Healthcare I take care of the people management, software architecture, and project management for the development of our software products. We develop state of the art production-grade predictive analytics services using artificial intelligence, machine learning, and deep learning. I lead and grow this focused and dedicated start-up spirited team of software engineers, data scientists and medical doctors.

Publications

Publications (19)
Article
Full-text available
There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds s...
Article
Full-text available
Objective Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We p...
Preprint
BACKGROUND Machine learning (ML) algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. We provide detailed eva...
Article
Full-text available
Background: Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. We provide detailed evalua...
Preprint
Full-text available
Objective: Machine learning algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provi...
Article
Full-text available
Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations...
Conference Paper
Full-text available
Antibiotics resistance poses a significant problem in today’s hospital care. Although large amounts of resistance data are gathered locally, they cannot be compared globally due to format and access diversity. We present an ontology-based integration approach serving an EU project in making antibiotics resistance data semantically and geographicall...
Technical Report
Full-text available
This report describes the second release of the semantic mediation framework prototype developed to support the semantic interoperability between the clinical research and clinical care systems of the SALUS project. The deliverable itself is the set of tools and services developed to create the semantic mediation framework prototype. For each of th...
Technical Report
Full-text available
This report describes the prototype of the semantic resource set developed to define the SALUS harmonized model to support the semantic interoperability between the clinical research and clinical care systems of the SALUS project for post market safety studies. The deliverable itself comprises of the SALUS Entity Models, the Source Content Entity M...
Conference Paper
Full-text available
The Simple Knowledge Organization System (SKOS) is popular for expressing controlled vocabularies for their use in Semantic Web applications. Using SKOS, concepts can be linked to other concepts and organized into hierarchies inside a single terminology system. Meanwhile, expressing mappings between concepts in different terminology systems is also...
Conference Paper
Full-text available
This work aims to demonstrate the interoperability framework developed in the SALUS project which enables effective integration and utilization of EHR data to reinforce post-market safety activities.
Article
Full-text available
The Simple Knowledge Organization System (SKOS) is popular for expressing controlled vocabularies, such as taxonomies, classifications, etc., for their use in Semantic Web applications. Using SKOS, concepts can be linked to other concepts and organized into hierarchies inside a single terminology system. Meanwhile, expressing mappings between conce...
Article
Full-text available
There is a growing need to semantically process and integrate clinical data from different sources for Clinical Data Management and Clinical Decision Support in the healthcare IT industry. In the clinical practice domain, the semantic gap between clinical information systems and domain ontologies is quite often difficult to bridge in one step. In t...
Conference Paper
Full-text available
Pre-approval clinical trials cannot guarantee that drugs will not have serious side effects after they are marketed. Post-approval drug safety data studies aim to address this problem, however, their effectiveness is started to be discussed especially after recent examples of drug withdrawals. This is due to the fact that, current post market safet...
Article
Although the health care sector has already been subjected to a major computerization effort, this effort is often limited to the implementation of standalone systems which do not communicate with each other. Interoperability problems limit health care applications from achieving their full potential. In this paper, we propose the use of Semantic W...
Article
Full-text available
Bacterial resistance to drugs has reached alarming levels but useful cross-site monitoring systems to track resistance evolution are lacking. In this paper we present the TrendMon surveillance system, a platform for querying, integrating and visualising antimicrobial resistance information.
Article
Full-text available
Proper surveillance of infectious diseases poses special challenges to information technology when it comes to data collection, including wide-area, multi-source and trans-border collection and aggregation of infectious disease and drug resistance information. In this project, we present a novel approach to efficiently monitor bacterial resistance...
Article
Full-text available
Antibiotics resistance development poses a significant problem in today's hospital care. Massive amounts of clinical data are being collected and stored in proprietary and unconnected systems in heterogeneous format. The DebugIT EU project promises to make this data geographically and semantically interoperable for case-based knowledge analysis app...

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Projects

Projects (2)
Project
Clinical prediction services for risk prediction of adverse events.