Cary Oberije - Academia.edu (original) (raw)
Papers by Cary Oberije
Studies in health technology and informatics, 2020
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08, 2008
Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporati... more Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques. In this paper we propose a privacy-preserving (PP) Cox model for survival analysis, and consider a real clinical setting where the data is horizontally distributed among different institutions. The proposed model is based on linearly projecting the data to a lower dimensional space through an optimal mapping obtained by solving a linear programming problem. Our approach differs from the commonly used random projection approach since it instead finds a projection that is optimal at preserving the properties of the data that are important for the specific problem at hand. Since our proposed approach produces an sparse mapping, it also generates a PP mapping that not only projects the data to a lower dimensional space but it also depends on a smaller subset of the original features (it provides explicit feature selection). Real data from several European healthcare institutions are used to test our model for survival prediction of non-small-cell lung cancer patients. These results are also confirmed using publicly available benchmark datasets. Our experimental results show that we are able to achieve a near-optimal performance without directly sharing the data across different data sources. This model makes it possible to conduct large-scale multi-centric survival analysis without violating privacy-preserving requirements.
![Research paper thumbnail of Erratum: “Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers” [Med. Phys. 45 (7), 3449-3459 (2018)]](https://attachments.academia-assets.com/83461870/thumbnails/1.jpg)
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Radiotherapy and Oncology, 2010
Radiotherapy and Oncology, 2009
International Journal of Radiation Oncology*Biology*Physics, 2008
The current tumor, node, metastasis system needs refinement to improve its ability to predict sur... more The current tumor, node, metastasis system needs refinement to improve its ability to predict survival of patients with non-small-cell lung cancer (NSCLC) treated with (chemo)radiation. In this study, we investigated the prognostic value of tumor volume and N status, assessed by using fluorodeoxyglucose-positron emission tomography (PET). Clinical data from 270 consecutive patients with inoperable NSCLC Stages I-IIIB treated radically with (chemo)radiation were collected retrospectively. Diagnostic imaging was performed using either integrated PET-computed tomography or computed tomography and PET separately. The Kaplan-Meier method, as well as Cox regression, was used to analyze data. Univariate survival analysis showed that number of positive lymph node stations (PLNSs), as well as N stage on PET, was associated significantly with survival. The final multivariate Cox model consisted of number of PLNSs, gross tumor volume (i.e., volume of the primary tumor plus lymph nodes), sex, World Health Organization performance status, and equivalent radiation dose corrected for time; N stage was no longer significant. Number of PLNSs, assessed by means of fluorodeoxyglucose-PET, was a significant factor for survival of patients with inoperable NSCLC treated with (chemo)radiation. Risk stratification for this group of patients should be based on gross tumor volume, number of PLNSs, sex, World Health Organization performance status, and equivalent radiation dose corrected for time.
European Journal of Cancer, 2007
British Journal of Psychology, 2009
Understanding blood donation motivation among non-donors is prerequisite to effective recruitment... more Understanding blood donation motivation among non-donors is prerequisite to effective recruitment. Two studies explored the psychological antecedents of blood donation motivation and the generalisability of a model of donation motivation across groups differing in age and educational level. An older well-educated population and a younger less well-educated population were sampled. The studies assessed the role of altruism, fear of blood/needles and donation-specific cognitions including attitudes and normative beliefs derived from an extended theory of planned behaviour (TPB). Across both samples, results showed that affective attitude, subjective norm, descriptive norm, and moral norm were the most important correlates of blood donation intentions. Self-efficacy was more important among the younger less well-educated group. Altruism was related to donation motivation but only indirectly through moral norm. Similarly, fear of blood/needles only had an indirect effect on motivation through affective attitude and self-efficacy. Additional analyses with the combined data set found no age or education moderation effects, suggesting that this core model of donation-specific cognitions can be used to inform future practical interventions recruiting new blood donors in the general population.
ObjectiveThe current pandemic has led to a proliferation of predictive models being developed to ... more ObjectiveThe current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice.MethodsIn this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive m...
Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as... more Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials. Abstract The aim of this study is to build a decision support system (DSS) to select radical prostatecto...
Seminars in Radiation Oncology
Journal of Personalized Medicine
Background: Searching through the COVID-19 research literature to gain actionable clinical insigh... more Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as...
International Journal of Technology Assessment in Health Care
Introduction:Optimizing radiotherapy with or without chemotherapy through advanced imaging and ac... more Introduction:Optimizing radiotherapy with or without chemotherapy through advanced imaging and accelerated radiation schemes shows promising results in locally advanced non–small-cell lung cancer (NSCLC). This study compared the cost-effectiveness of positron emission tomography-computed tomography based isotoxic accelerated sequential chemo-radiation (SRT2) and concurrent chemo-radiation with daily low-dose cisplatin (CRT2) with standard sequential (SRT1) and concurrent chemo-radiation (CRT1).Methods:We used an externally validated mathematical model to simulate the four treatment strategies. The model was built using data from 200 NSCLC patients treated with curative sequential chemo-radiation. For concurrent strategies, data from a meta-analysis and a single study were included in the model. Costs, utilities, and resource use estimates were obtained from literature. Primary outcomes were the incremental cost-effectiveness and cost-utility ratio (ICUR) of each strategy. Scenario a...
Advances in Particle Therapy
PLOS ONE
Background Prognostic models based on individual patient characteristics can improve treatment de... more Background Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/ CT) scan acquired during treatment. Methods The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called 'cohort
Studies in health technology and informatics, 2020
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08, 2008
Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporati... more Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques. In this paper we propose a privacy-preserving (PP) Cox model for survival analysis, and consider a real clinical setting where the data is horizontally distributed among different institutions. The proposed model is based on linearly projecting the data to a lower dimensional space through an optimal mapping obtained by solving a linear programming problem. Our approach differs from the commonly used random projection approach since it instead finds a projection that is optimal at preserving the properties of the data that are important for the specific problem at hand. Since our proposed approach produces an sparse mapping, it also generates a PP mapping that not only projects the data to a lower dimensional space but it also depends on a smaller subset of the original features (it provides explicit feature selection). Real data from several European healthcare institutions are used to test our model for survival prediction of non-small-cell lung cancer patients. These results are also confirmed using publicly available benchmark datasets. Our experimental results show that we are able to achieve a near-optimal performance without directly sharing the data across different data sources. This model makes it possible to conduct large-scale multi-centric survival analysis without violating privacy-preserving requirements.
Radiotherapy and Oncology, 2010
Radiotherapy and Oncology, 2009
International Journal of Radiation Oncology*Biology*Physics, 2008
The current tumor, node, metastasis system needs refinement to improve its ability to predict sur... more The current tumor, node, metastasis system needs refinement to improve its ability to predict survival of patients with non-small-cell lung cancer (NSCLC) treated with (chemo)radiation. In this study, we investigated the prognostic value of tumor volume and N status, assessed by using fluorodeoxyglucose-positron emission tomography (PET). Clinical data from 270 consecutive patients with inoperable NSCLC Stages I-IIIB treated radically with (chemo)radiation were collected retrospectively. Diagnostic imaging was performed using either integrated PET-computed tomography or computed tomography and PET separately. The Kaplan-Meier method, as well as Cox regression, was used to analyze data. Univariate survival analysis showed that number of positive lymph node stations (PLNSs), as well as N stage on PET, was associated significantly with survival. The final multivariate Cox model consisted of number of PLNSs, gross tumor volume (i.e., volume of the primary tumor plus lymph nodes), sex, World Health Organization performance status, and equivalent radiation dose corrected for time; N stage was no longer significant. Number of PLNSs, assessed by means of fluorodeoxyglucose-PET, was a significant factor for survival of patients with inoperable NSCLC treated with (chemo)radiation. Risk stratification for this group of patients should be based on gross tumor volume, number of PLNSs, sex, World Health Organization performance status, and equivalent radiation dose corrected for time.
European Journal of Cancer, 2007
British Journal of Psychology, 2009
Understanding blood donation motivation among non-donors is prerequisite to effective recruitment... more Understanding blood donation motivation among non-donors is prerequisite to effective recruitment. Two studies explored the psychological antecedents of blood donation motivation and the generalisability of a model of donation motivation across groups differing in age and educational level. An older well-educated population and a younger less well-educated population were sampled. The studies assessed the role of altruism, fear of blood/needles and donation-specific cognitions including attitudes and normative beliefs derived from an extended theory of planned behaviour (TPB). Across both samples, results showed that affective attitude, subjective norm, descriptive norm, and moral norm were the most important correlates of blood donation intentions. Self-efficacy was more important among the younger less well-educated group. Altruism was related to donation motivation but only indirectly through moral norm. Similarly, fear of blood/needles only had an indirect effect on motivation through affective attitude and self-efficacy. Additional analyses with the combined data set found no age or education moderation effects, suggesting that this core model of donation-specific cognitions can be used to inform future practical interventions recruiting new blood donors in the general population.
ObjectiveThe current pandemic has led to a proliferation of predictive models being developed to ... more ObjectiveThe current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice.MethodsIn this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive m...
Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as... more Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials. Abstract The aim of this study is to build a decision support system (DSS) to select radical prostatecto...
Seminars in Radiation Oncology
Journal of Personalized Medicine
Background: Searching through the COVID-19 research literature to gain actionable clinical insigh... more Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as...
International Journal of Technology Assessment in Health Care
Introduction:Optimizing radiotherapy with or without chemotherapy through advanced imaging and ac... more Introduction:Optimizing radiotherapy with or without chemotherapy through advanced imaging and accelerated radiation schemes shows promising results in locally advanced non–small-cell lung cancer (NSCLC). This study compared the cost-effectiveness of positron emission tomography-computed tomography based isotoxic accelerated sequential chemo-radiation (SRT2) and concurrent chemo-radiation with daily low-dose cisplatin (CRT2) with standard sequential (SRT1) and concurrent chemo-radiation (CRT1).Methods:We used an externally validated mathematical model to simulate the four treatment strategies. The model was built using data from 200 NSCLC patients treated with curative sequential chemo-radiation. For concurrent strategies, data from a meta-analysis and a single study were included in the model. Costs, utilities, and resource use estimates were obtained from literature. Primary outcomes were the incremental cost-effectiveness and cost-utility ratio (ICUR) of each strategy. Scenario a...
Advances in Particle Therapy
PLOS ONE
Background Prognostic models based on individual patient characteristics can improve treatment de... more Background Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/ CT) scan acquired during treatment. Methods The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called 'cohort