A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse

Garcelon, Nicolas; Neuraz, Antoine; Salomon, Rémi; Faour, Hassan; Benoît, Vincent; Delapalme, Arthur; Münnich, Arnold; Burgun, Anita; Rance, Bastien

doi:10.1016/j.jbi.2018.02.019

Cited by 100 publications

(88 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Dr. Warehouse proposed by Garcelon et al [30] is a free-text search engine using Oracle Text to index all its documents. The system is based on relational databases and relies on ranking after retrieval which may limit its capability to deploy state-of-the-art IR methods such as BM25 or Markov Random Fields.…”

Section: Related Workmentioning

confidence: 99%

Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation (Preprint)

Liu¹,

Wang²,

Wen³

et al. 2019

Preprint

View full text Add to dashboard Cite

150-250 words)Background: Widespread adoption of electronic health records (EHRs) has enabled secondary use of EHR data for clinical research and healthcare delivery. Natural language processing (NLP) techniques have shown promise in their capability to extract the embedded information in unstructured clinical data, and information retrieval (IR) techniques provide flexible and scalable solutions that can augment the NLP systems for retrieving and ranking relevant records. Methods:In this paper, we present the implementation of Cohort Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval system that can execute textual cohort selection queries on both structured and unstructured EHR data. CREATE is a proof-of-concept system that leverages a combination of structured queries and IR techniques on NLP results to improve cohort retrieval performance while adopting the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The NLP component empowered by cTAKES is used to extract CDM concepts from textual queries. We design a hierarchical index in Elasticsearch to support CDM concept search utilizing IR techniques and frameworks.Results: Our case study on 5 cohort identification queries evaluated using the IR metric, P@5 (Precision at 5) at both the patient-level and document-level, demonstrates that CREATE achieves an average P@5 of 0.90, which outperforms systems using only structured data or only unstructured data with average P@5s of 0.54 and 0.74, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation (Preprint)

Liu¹,

Wang²,

Wen³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…It gives our treating teams the essential insights and knowledge on which to base their care. We aggregate data in warehouses, we have mentioned ConSoRe, but in France alone, we can find other models outside oncology applications such as Dr Warehouse and eHOP [30,31]. Ethical and legal issues are paramount when developing these infrastructures, as it is unclear how samples (and data) might be re-used and whether any future uses were compatible with the original consent.…”

Section: Resultsmentioning

confidence: 99%

Untangling Data in Precision Oncology – A Model for Chronic Diseases?

Fernández

2020

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: Any attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment paradigms are shifting to targeted molecular therapies. Methods: From nineteenth-century medical notes consisting entirely of narrative description to standardised forms recording physical examination and medical notes, we have nowadays moved to electronic health records (EHRs). All our analogue medical records are rendered as sequences of zeros and ones changing how we capture and share data. The challenge we face is to offload the analysis without entrusting a machine (or algorithms) to make major decisions about a diagnosis, a treatment, or a surgery, keeping the human oversight. Computers don’t have judgment, they lack context. Results: EHRs have become the latest addition to our toolset to look after patients. Moore’s law and general advances in computation have contributed to make EHRs a cornerstone of Molecular Tumour Boards, presenting a detailed and unique description of a tumour and treatment options. Conclusions: Precision oncology, as a systematic approach matching the most accurate and effective treatment to each individual cancer patient, based on a molecular profile, is already expanding to other disease areas.

show abstract

“…Dr. Warehouse is an open-source CDW based on a vector space model, which explores NLP using the Unified Medical Language System Metathesaurus ® (UMLS ® ) [40]. By leveraging phenotype similarities identified in the EHR, Dr.…”

Section: Phenotype-driven Diagnosismentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

View full text Add to dashboard Cite

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

show abstract

A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse

Cited by 100 publications

References 37 publications

Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation (Preprint)

Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation (Preprint)

Untangling Data in Precision Oncology – A Model for Chronic Diseases?

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Contact Info

Product

Resources

About