This study evaluates the utility of machine learning (ML) and natural language processing (NLP) in the processing and initial analysis of data within the electronic health record (EHR). We present and...
A total of 4216 distinct medication entries were obtained from the EHR and were initially labeled by human reviewers as opioid or non-opioid medications. An approach incorporating bag-of-words NLP and...
A total of 3991 medication strings were classified as non-opioid medications (94.7%), and 225 were classified as opioid medications by the human reviewers (5.3%). The algorithm achieved a 99.6% accura...
The automated approach achieved excellent performance in classifying opioids or non-opioids, even with a practical number of human reviewed training examples. This will allow a significant reduction i...
As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and complete epidemiologic surveillance of OUD, to inform prevention...
Data were drawn from EHR records for hospital and emergency department patient visits to a large regional academic medical center from 2017 to 2019. International Classification of Disease, 10th Editi...
While there was substantial overlap in the identified records (n = 1,381 [59.2 %]), overall n = 2,332 unique visits were identified. Of the total unique visits, 430 (18.4 %) were identified only by IC...
NLP-based algorithms can automate data extraction and identify evidence of opioid use disorder from unstructured electronic healthcare records. The most complete ascertainment of OUD in EHR was combin...
This study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records....
This was a random-sample retrospective observational cohort study leveraging unstructured data from injured patients taken to one of 9 acute care hospitals in an integrated health system between 2015 ...
A random sample of 7,000 patient encounters of 177,127 was annotated. Only 2,951 had sufficient information to determine whether an intra-abdominal injury was present. Among those, 84 (2.9%) had an in...
Natural language processing could be a screening decision support tool, which, if paired with human clinical assessment, can maximize precision of intra-abdominal injury identification....
Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patien...
CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 an...
Of the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical d...
The NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied...
Differential phase contrast, in its high resolution modification also known as first moment microscopy or momentum resolved STEM [1-7] , basically measures the lateral momentum transfer to the electro...
Measuring long-term housing outcomes is important for evaluating the impacts of services for individuals with homeless experience. However, assessing long-term housing status using traditional methods...
We compared VA EHR indicators of housing instability, including information extracted from clinical notes using natural language processing (NLP), with patient-reported housing outcomes in a cohort of...
NLP achieved higher sensitivity and specificity than standard diagnosis codes for detecting episodes of unstable housing. Other structured data elements in the VA EHR showed promising performance, par...
Evaluation efforts and research studies assessing longitudinal housing outcomes should incorporate multiple data sources of documentation to achieve optimal performance....
Propionic acidemia (PA) is a rare autosomal recessive organic acidemia that classically presents within the first days of life with a metabolic crisis or via newborn screening and is confirmed with la...
To retrospectively describe the natural history of patients with PA in a clinical setting from a real-world database using both structured and unstructured electronic health record (EHR) data using no...
This retrospective study used EHR data to identify patients with PA seen at the Mayo Clinic. Unstructured clinical text (medical notes, pathology reports) were analyzed using augmented curation natura...
In total, 13 patients with PA were identified, with visits occurring from 1998 to 2022. Age at diagnosis ranged from birth to 3 years; age at initial evaluation at the Mayo Clinic ranged from 3 days t...
This study highlights the range and frequency of clinical outcomes experienced by patients with PA and demonstrates the clinical burden of MDEs....
The Electronic Health Record (EHR) contains information about social determinants of health (SDoH) such as homelessness. Much of this information is contained in clinical notes and can be extracted us...
Rising patient numbers, with increasing complexity, challenge the sustainability of the current preoperative process. We evaluated whether an electronic screening application can distinguish patients ...
Prospective cohort study....
Preoperative clinic of a tertiary academic hospital....
1395 adult patients scheduled for surgery or procedural sedation....
We assessed a novel electronic preoperative screening application which consists of a questionnaire with a maximum of 185 questions regarding the patient's medical history and current state of health....
The recommendation of the electronic screening system was compared with the regular preoperative assessment using measures of diagnostic accuracy and agreement. Secondary outcomes included ASA-PS clas...
Sensitivity to detect patients who needed additional consultation was 97.5% (95%CI 91.2-99.7) and the negative likelihood ratio was 0.08 (95%CI 0.02-0.32). 407 (29.2%) patients were approved for surge...
Electronic screening can reliably identify patients who can have their first contact with an anesthesiologist on the day of surgery, potentially allowing a major proportion of patients to safely bypas...
Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber g...
