scholarly journals Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research Register (SHARE).

2019 ◽  
Author(s):  
Wen Shi ◽  
Tom Kelsey ◽  
Frank Sullivan
Keyword(s):  
Health Research ◽  
Clinical Studies ◽  
Search Strategy ◽  
Database Search ◽  
Prediction Score ◽  
Free Text ◽  
Case Based Reasoning ◽  
Study Participation ◽  
Research Register ◽  
Case Based

Abstract Background: Trials often struggle to achieve their target sample size with only half doing so. Some researchers have turned to Electronic Health Records (EHRs), seeking a more efficient way of recruitment. The Scottish Health Research Register (SHARE) obtained patients’ consent for their EHRs to be used as a searching base from which researchers can find potential participants. However, due to the fact that EHR data is not complete, sufficient or accurate, a database search strategy may not generate the best case-finding result. The current study aims to evaluate the performance of a case-based reasoning method in identifying participants for population-based clinical studies recruiting through SHARE, and assess the difference between its resultant cohort and the original one deriving from searching EHRs.Methods: A case-based reasoning framework was applied to 119 participants in nine projects using two-fold cross-validation, with records from a further 86,292 individuals used for testing. A prediction score for study participation was derived from the diagnosis, procedure, pharmaceutical prescription, and laboratory test results attributes of each participant. Evaluation was conducted by calculating Area Under the ROC Curve and information retrieval metrics for the ranking list of the test set by prediction score. We compared the most likely participants as identified by searching a database to those ranked highest by our model. Results: The average ROCAUC for nine projects was 81% indicating strong predictive ability for these data. However, the derived ranking lists showed lower predictive performance, with only 21% of the persons ranked within top 50 positions being the same as identified by searching databases.Conclusions: Case-based reasoning is may be more effective than a database search strategy for participant identification for clinical studies using population EHRs. The lower performance of ranking lists derived from case-based reasoning means that patients identified as highly suitable for study participation may still not be recruited. This suggests that further study is needed into improvements in the collection and curation of population EHRs such as use of free text data to better define the characteristics of people more likely to be recruited.

scholarly journals Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research Register (SHARE).

2020 ◽  
Author(s):  
Wen Shi ◽  
Tom Kelsey ◽  
Frank Sullivan
Keyword(s):  
Health Research ◽  
Clinical Studies ◽  
Search Strategy ◽  
Database Search ◽  
Prediction Score ◽  
Free Text ◽  
Case Based Reasoning ◽  
Study Participation ◽  
Research Register ◽  
Case Based

Abstract Background: Trials often struggle to achieve their target sample size with only half doing so. Some researchers have turned to Electronic Health Records (EHRs), seeking a more efficient way of recruitment. The Scottish Health Research Register (SHARE) obtained patients’ consent for their EHRs to be used as a searching base from which researchers can find potential participants. However, due to the fact that EHR data is not complete, sufficient or accurate, a database search strategy may not generate the best case-finding result. The current study aims to evaluate the performance of a case-based reasoning method in identifying participants for population-based clinical studies recruiting through SHARE, and assess the difference between its resultant cohort and the original one deriving from searching EHRs. Methods: A case-based reasoning framework was applied to 119 participants in nine projects using two-fold cross-validation, with records from a further 86,292 individuals used for testing. A prediction score for study participation was derived from the diagnosis, procedure, pharmaceutical prescription, and laboratory test results attributes of each participant. Evaluation was conducted by calculating Area Under the ROC Curve and information retrieval metrics for the ranking list of the test set by prediction score. We compared the most likely participants as identified by searching a database to those ranked highest by our model. Results: The average ROCAUC for nine projects was 81% indicating strong predictive ability for these data. However, the derived ranking lists showed lower predictive performance, with only 21% of the persons ranked within top 50 positions being the same as identified by searching databases. Conclusions: Case-based reasoning is may be more effective than a database search strategy for participant identification for clinical studies using population EHRs. The lower performance of ranking lists derived from case-based reasoning means that patients identified as highly suitable for study participation may still not be recruited. This suggests that further study is needed into improvements in the collection and curation of population EHRs, such as use of free text data to aid reliable identification of people more likely to be recruited to clinical trials.

scholarly journals Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research Register (SHARE).

2020 ◽  
Author(s):  
Wen Shi ◽  
Tom Kelsey ◽  
Frank Sullivan
Keyword(s):  
Health Research ◽  
Clinical Studies ◽  
Search Strategy ◽  
Database Search ◽  
Prediction Score ◽  
Free Text ◽  
Case Based Reasoning ◽  
Study Participation ◽  
Research Register ◽  
Case Based

Abstract Background: Trials often struggle to achieve their target sample size with only half doing so. Some researchers have turned to Electronic Health Records (EHRs), seeking a more efficient way of recruitment. The Scottish Health Research Register (SHARE) obtained patients’ consent for their EHRs to be used as a searching base from which researchers can find potential participants. However, due to the fact that EHR data is not complete, sufficient or accurate, a database search strategy may not generate the best case-finding result. The current study aims to evaluate the performance of a case-based reasoning method in identifying participants for population-based clinical studies recruiting through SHARE, and assess the difference between its resultant cohort and the original one deriving from searching EHRs. Methods: A case-based reasoning framework was applied to 119 participants in nine projects using two-fold cross-validation, with records from a further 86,292 individuals used for testing. A prediction score for study participation was derived from the diagnosis, procedure, pharmaceutical prescription, and laboratory test results attributes of each participant. Evaluation was conducted by calculating Area Under the ROC Curve and information retrieval metrics for the ranking list of the test set by prediction score. We compared the most likely participants as identified by searching a database to those ranked highest by our model. Results: The average ROCAUC for nine projects was 81% indicating strong predictive ability for these data. However, the derived ranking lists showed lower predictive performance, with only 21% of the persons ranked within top 50 positions being the same as identified by searching databases. Conclusions: Case-based reasoning is may be more effective than a database search strategy for participant identification for clinical studies using population EHRs. The lower performance of ranking lists derived from case-based reasoning means that patients identified as highly suitable for study participation may still not be recruited. This suggests that further study is needed into improvements in the collection and curation of population EHRs, such as use of free text data to aid reliable identification of people more likely to be recruited to clinical trials.

Extracting information from free-text aircraft repair notes

2001 ◽  
Vol 15 (4) ◽  
pp. 295-305 ◽  
Author(s):  
BENOIT FARLEY
Keyword(s):  
Decision Support ◽  
Domain Knowledge ◽  
Electronic Version ◽  
Free Text ◽  
Case Based Reasoning ◽  
Lexical Information ◽  
Text Format ◽  
Support Methods ◽  
Case Based ◽  
Information Extraction System

For every problem mentioned by crew members in an aircraft log book, an associated repair action note is entered in the same log book by a maintenance technician after the problem has been handled. These hand-written repair notes, subsequently transcribed into a database, give an account of the actions undertaken by the technicians to fix the problems. Written in a free-text format with peculiar linguistic characteristics, including many arbitrary abbreviations and missing auxiliaries, they contain valuable information that can be used for decision support methods such as case-based reasoning. We use natural language techniques in our information extraction system to analyze the structure and contents of these notes in order to determine the pieces of equipment involved in a repair and what was done to them. Lexical information and domain knowledge are extracted from an electronic version of the illustrated parts catalog for the particular airplane, and are used at different stages of the process, from the morpholexical analysis to the evaluation of the semantic expression generated by the syntactical analyzer.

scholarly journals Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials

2015 ◽  
Vol 22 (e1) ◽  
pp. e141-e150 ◽  
Author(s):  
Riccardo Miotto ◽  
Chunhua Weng
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Eligible Patient ◽  
Free Text ◽  
Case Based Reasoning ◽  
Good Precision ◽  
Health Records ◽  
Electronic Health ◽  
Trial Participants ◽  
Case Based

Abstract Objective To develop a cost-effective, case-based reasoning framework for clinical research eligibility screening by only reusing the electronic health records (EHRs) of minimal enrolled participants to represent the target patient for each trial under consideration. Materials and Methods The EHR data—specifically diagnosis, medications, laboratory results, and clinical notes—of known clinical trial participants were aggregated to profile the “target patient” for a trial, which was used to discover new eligible patients for that trial. The EHR data of unseen patients were matched to this “target patient” to determine their relevance to the trial; the higher the relevance, the more likely the patient was eligible. Relevance scores were a weighted linear combination of cosine similarities computed over individual EHR data types. For evaluation, we identified 262 participants of 13 diversified clinical trials conducted at Columbia University as our gold standard. We ran a 2-fold cross validation with half of the participants used for training and the other half used for testing along with other 30 000 patients selected at random from our clinical database. We performed binary classification and ranking experiments. Results The overall area under the ROC curve for classification was 0.95, enabling the highlight of eligible patients with good precision. Ranking showed satisfactory results especially at the top of the recommended list, with each trial having at least one eligible patient in the top five positions. Conclusions This relevance-based method can potentially be used to identify eligible patients for clinical trials by processing patient EHR data alone without parsing free-text eligibility criteria, and shows promise of efficient “case-based reasoning” modeled only on minimal trial participants.

scholarly journals Cross-sectional analysis of UK research studies in 2015: results from a scoping project with the UK Health Research Authority

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e022340 ◽  
Author(s):  
Tim Clark ◽  
Richard H Wicentowski ◽  
Matthew R Sydes
Keyword(s):  
Health Research ◽  
Clinical Studies ◽  
Public Involvement ◽  
Secondary Outcome ◽  
Ethical Review ◽  
Free Text ◽  
Research Activities ◽  
The Uk ◽  
The Impact ◽  
Research Studies

ObjectivesTo determine whether data on research studies held by the UK Health Research Authority (HRA) could be summarised automatically with minimal manual intervention. There are numerous initiatives to reduce research waste by improving the design, conduct, analysis and reporting of clinical studies. However, quantitative data on the characteristics of clinical studies and the impact of the various initiatives are limited.DesignFeasibility study, using 1 year of data.SettingWe worked with the HRA on a pilot study using research applications submitted for UK-wide ethical review. We extracted into a single dataset, information held in anonymised XML files by the Integrated Research Application System (IRAS) and the HRA Assessment Review Portal (HARP). Research applications from 2014 to 2016 were provided. We used standard text extraction methods to assess information held in free-text fields. We use simple, descriptive methods to summarise the research activities that we extracted.ParticipantsNot applicable—records-based studyInterventionsNot applicable.Primary and secondary outcome measuresFeasibility of extraction and processing.ResultsWe successfully imported 1775 non-duplicate research applications from the XML files into a single database. Of these, 963 were randomised controlled trials and 812 were other studies. Most studies received a favourable opinion. There was limited patient and public involvement in the studies. Most, but not all, studies were planned for publication of results. Novel study designs (eg, adaptive and Bayesian designs) were infrequently reported.ConclusionsWe have demonstrated that the data submitted from IRAS to the HRA and its HARP system are accessible and can be queried for information. We strongly encourage the development of fully resourced collaborative projects to further this work. This would aid understanding of how study characteristics change over time and across therapeutic areas, as well as the progress of initiatives to improve the quality and relevance of research studies.

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