Automated Selection of Relevant Information for Notification of Incident Cancer Cases within a Multisource Cancer Registry

Jouhet, Vianney; Défossez, Gautier; Crisap,; CoRIM,; Ingrand, Pierre

doi:10.3414/me12-01-0101

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…In contrast, hospital‐based cancer databases provide more clinical information than population‐based cancer registries, such as patient information, clinicopathological information, genomic data, disease staging, treatment, follow‐up, lab test results, and medical records, which supports clinical research and improves the care of cancer patients [ 3 , 4 ]. Furthermore, a consistent system of coding needs to be ensured to integrate the collected data from different sources that could be encoded in various terminological standards [ 5 ]. In addition, ontology, as an integration of knowledge, annotation, and concepts, plays an important role in cancer treatment and research.…”

Section: Informatics‐supported Applications Of Cancer Research and Cl...mentioning

confidence: 99%

Application of informatics in cancer research and clinical practice: Opportunities and challenges

et al. 2022

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Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the

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Section: Informatics‐supported Applications Of Cancer Research and Cl...mentioning

confidence: 99%

Application of informatics in cancer research and clinical practice: Opportunities and challenges

et al. 2022

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“…A notification algorithm [24] determines, for each patient, the tumours that should be notified to cancer registry staff according to registration rules for multiple primary cancers [25] and already validated tumours. Then each case is manually checked by registry staff by visual inspection of information sources, assessing the need to refer to a patient’s medical record to register the tumour.…”

Section: Introductionmentioning

confidence: 99%

Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer

Défossez

Rollet

Dameron

et al. 2014

BMC Med Inform Decis Mak

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BackgroundEnsuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity.MethodsCare trajectories were modeled as ordered dated events aggregated into states, the granularity of which was defined from standard care guidelines. The algorithm generates each state from the aggregation over a period of tracer events characterised on the basis of diagnoses and medical procedures. The sequences are presented in simple form showing presence and order of the states, and in an extended form that integrates the duration of the states. The similarity of the sequences, which are represented in the form of chains of characters, was calculated using a generalised Levenshtein distance.ResultsThe evaluation was performed on a sample of 159 female patients whose itineraries were also calculated manually from medical records using the same aggregation rules and dating system as the algorithm. Ninety-eight per cent of the trajectories were correctly reconstructed with respect to the ordering of states. When the duration of states was taken into account, 94% of the trajectories matched reality within three days. Dissimilarities between sequences were mainly due to the absence of certain pathology reports and to coding anomalies in hospitalisation data.ConclusionsThese results show the ability of an integrated regional information system to formalise care trajectories and automatically produce indicators for time-lapse to care instatement, of interest in the planning of care in cancer. The next step will consist in evaluating this approach and extending it to more complex trajectories (metastasis, relapse) and to other cancer localisations.

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Socio-demographic inequalities in stage at diagnosis of lung cancer: A French population-based study

Quillet,

Le Stang,

Meriau

et al. 2024

Cancer Epidemiology

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Automated Selection of Relevant Information for Notification of Incident Cancer Cases within a Multisource Cancer Registry

Cited by 8 publications

References 8 publications

Application of informatics in cancer research and clinical practice: Opportunities and challenges

Application of informatics in cancer research and clinical practice: Opportunities and challenges

Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer

Socio-demographic inequalities in stage at diagnosis of lung cancer: A French population-based study

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