Tracking global health funding is a crucial but time consuming and labor-intensive process. This study aimed to develop a framework to automate the tracking of global health spending using natural lan...
We used data curated by Schäferhoff et al., which tracked the official development assistance (ODA) disbursements to global CGH for 2013, 2015, and 2017, for training and validating the ML models. To ...
After we trained the machine on the training dataset (...
We have demonstrated that NLP and ML can be a feasible and efficient way to classify health projects into different global CGH categories, and thus track health funding for CGH routinely using data fr...
We used social media data from "covid19positive" subreddit, from 03/2020 to 03/2022 to identify COVID-19 cases and extract their reported symptoms automatically using natural language processing (NLP)...
Clinical registries are critical for modern surgery and underpin outcomes research, device monitoring, and trial development. However, existing approaches to registry construction are labor-intensive,...
We used a Hadoop data lake consisting of all the information generated by an academic medical center. Using this database and structured query language queries, we retrieved every operative note writt...
A total of 31 502 operative cases were downloaded and processed using regex classifiers. The codebase required 5 days of development, 3 weeks of validation, and less than 1 hour for the software to ge...
This study demonstrates the feasibility of automatically generating a spine registry using the EHR and an interpretable, customizable natural language processing algorithm which may reduce pitfalls as...
Many clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promisin...
To evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a c...
This diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened hu...
Main outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-...
A total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learnin...
In this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified po...
Risk-stratification tools are routinely used in obstetrics to assist care teams in assessing and communicating risk associated with delivery. Electronic health record data and machine learning methods...
To compare the predictive performance of natural language processing (NLP) of clinician documentation with that of a previously validated tool to identify individuals at high risk for maternal morbidi...
This retrospective diagnostic study was conducted at Brigham and Women's Hospital and Massachusetts General Hospital, Boston, Massachusetts, and included individuals admitted for delivery at the forme...
Natural language processing of clinician documentation and OB-CMI scores....
Natural language processing of clinician-authored admission notes was used to predict SMM in individuals delivering at the same institution but not included in the prospective OB-CMI study. The NLP mo...
This study included 19 794 individuals; 4034 (20.4%) were included in the original prospective validation study of the OB-CMI (testing set), and the remaining 15 760 (79.6%) composed the training set....
In this study, the NLP method and a validated risk-stratification tool had a similar ability to identify patients at high risk of SMM. Future prospective research is needed to validate the NLP approac...
Barriers to healthcare access are widespread in elderly populations, with a major consequence that older people are not benefiting from the latest technologies to diagnose disease. Recent advances in ...
Medico-administrative data are promising to automate the calculation of Healthcare Quality and Safety Indicators. Nevertheless, not all relevant indicators can be calculated with this data alone. Our ...
We performed a multicenter cross-sectional observational feasibility study on the clinical data warehouse of Assistance Publique - Hôpitaux de Paris (AP-HP). We studied the management of breast cancer...
Out of 5785 female patients diagnosed with a breast cancer (60.9 years, IQR [50.0-71.9]), 5,147 (89.0%) had procedures related to breast cancer recorded in the PMSI, and 3732 (72.5%) had at least one ...
The availability of medical reports in the electronic health records, of the elementary variables within the reports, and the performance of the extraction algorithms limit the population for which th...
The automated calculation of quality indicators from electronic health records is a prospect that comes up against many practical obstacles....
Social determinants of health (SDOHs) are known to be associated with increased risk of suicidal behaviors, but few studies use SDOHs from unstructured electronic health record notes....
To investigate associations between veterans' death by suicide and recent SDOHs, identified using structured and unstructured data....
This nested case-control study included veterans who received care under the US Veterans Health Administration from October 1, 2010, to September 30, 2015. A natural language processing (NLP) system w...
Occurrence of SDOHs over a maximum span of 2 years compared with no occurrence of SDOH....
Cases of suicide death were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. Suicide was ascertained by National Death Index, and patients were followed up for...
Of 6 122 785 veterans, 8821 committed suicide during 23 725 382 person-years of follow-up (incidence rate 37.18 per 100 000 person-years). These 8821 veterans were matched with 35 284 control particip...
In this study, NLP-extracted SDOHs, with and without structured SDOHs, were associated with increased risk of suicide among veterans, suggesting the potential utility of NLP in public health studies....
Participant recruitment is a barrier to successful clinical research. One strategy to improve recruitment is to conduct eligibility prescreening, a resource-intensive process where clinical research s...
We examined the clinical research staff's perceived usability of C2Q for clinical research eligibility prescreening....
Twenty clinical research staff evaluated the usability of C2Q using a cognitive walkthrough with a think-aloud protocol and a Post-Study System Usability Questionnaire. On-screen activity and audio we...
Evaluators aged from 24 to 46 years old (33.8; SD: 7.32) demonstrated high computer literacy (6.36; SD:0.17); female (75 %), White (35 %), and clinical research coordinators (45 %). C2Q demonstrated h...
The cognitive walkthrough with a think-aloud protocol informed iterative system refinement and demonstrated the usability of C2Q by clinical research staff. Key recommendations for system development ...
The number of published scientific articles is increasing dramatically and makes it difficult to keep track of research topics. This is particularly difficult in interdisciplinary research areas where...
