Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural la...
The target Kampo formulas were 33 formulas listed in the 17th revision of the Japanese Pharmacopoeia. The information included in the system comes from the package inserts of Kampo medicines, Manuals ...
The precision, recall, and F-measure of the system performance were 0.986, 0.915, and 0.949, respectively. The results were stable even with differences in the amount of expertise of the question auth...
We developed a system using natural language classification that can give appropriate answers to most of the validation questions....
It remains unknown whether capturing data from electronic health records (EHRs) using natural language processing (NLP) can improve venous thromboembolism (VTE) detection in different clinical setting...
The aim of this study was to validate the NLP algorithm in a clinical decision support system for VTE risk assessment and integrated care (DeVTEcare) to identify VTEs from EHRs....
All inpatients aged ≥18 years in the Sixth Medical Center of the Chinese People's Liberation Army General Hospital from January 1 to December 31, 2021, were included as the validation cohort. The sens...
Among 30,152 patients (median age 56 [IQR 41-67] years; 14,247/30,152, 47.3% females), the prevalence of VTE, PE, and DVT was 2.1% (626/30,152), 0.6% (177/30,152), and 1.8% (532/30,152), respectively....
The NLP algorithm in our DeVTEcare identified VTE well across different clinical settings, especially in patients in surgery units, departments with low-risk VTE, and patients aged ≤65 years. This alg...
To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach ...
Overdose is one of the leading causes of death in the US; however, surveillance data lag considerably from medical examiner determination of the death to reporting in national surveillance reports....
To automate the classification of deaths related to substances in medical examiner data using natural language processing (NLP) and machine learning (ML)....
Diagnostic study comparing different natural language processing and machine learning algorithms to identify substances related to overdose in 10 health jurisdictions in the US from January 1, 2020, t...
Text from each case was manually classified to a substance that was related to the death. Three feature representation methods were used and compared: text frequency-inverse document frequency (TF-IDF...
Text data from death certificates were classified as any opioid, fentanyl, alcohol, cocaine, methamphetamine, heroin, prescription opioid, and an aggregate of other substances. Diagnostic metrics and ...
Of 35 433 death records analyzed (decedent median age, 58 years [IQR, 41-72 years]; 24 449 [69%] were male), the most common substances related to deaths included any opioid (5739 [16%]), fentanyl (47...
In this diagnostic study, NLP/ML algorithms demonstrated excellent diagnostic performance at classifying substances related to overdoses. These algorithms should be integrated into workflows to decrea...
Although previous studies have consistently demonstrated that physicians are more likely than non-physicians to experience work-related stressors prior to suicide, the specific nature of these stresso...
The study utilized a mixed methods approach combining thematic analysis and natural language processing to develop themes representing death investigation narratives of 200 physician suicides with imp...
Through thematic analysis, six overarching themes were identified: incapacity to work due to deterioration of physical health, substance use jeopardizing employment, interaction between mental health ...
This is the first known study that integrated thematic analysis and natural language processing to characterize work-related stressors preceding physician suicide. The findings highlight the importanc...
Although holistic review has been used successfully in some residency programs to decrease bias, such review is time-consuming and unsustainable for many programs without initial prescreening. The uns...
Using residency applications to the University of Utah internal medicine-pediatrics program from 2015 to 2019, the authors extracted relevant snippets of text from the narrative sections of applicatio...
Overall, the MLM had a sensitivity of 0.64, specificity of 0.97, positive predictive value of 0.62, negative predictive value of 0.97, and F1 score of 0.63. The mean (SD) total number of annotations p...
The authors created an MLM that can identify several values important for resident success in internal medicine-pediatrics programs with moderate sensitivity and high specificity. The authors will con...
Neuropsychiatric symptoms (NPS) are prevalent in the early clinical stages of Alzheimer's disease (AD) according to proxy-based instruments. Little is known about which NPS clinicians report and wheth...
Two academic memory clinic cohorts were used: the Amsterdam UMC (n = 3001) and the Erasmus MC (n = 646). Patients included in these cohorts had MCI, AD dementia, or mixed AD/VaD dementia. Ten trained ...
Internal validation performance of the classifiers was excellent (AUC range: 0.81-0.91), but external validation performance decreased (AUC range: 0.51-0.93). NPS were prevalent in EHRs from the Amste...
NLP classifiers performed well in detecting a wide range of NPS in EHRs of patients with symptomatic AD visiting the memory clinic and showed that clinicians frequently reported NPS in these EHRs. Cli...
We analyzed the prevalence and type of bias in letters of recommendation (LOR) for pediatric surgical fellowship applications from 2016-2021 using natural language processing (NLP) at a quaternary car...
Demographics were extracted from submitted applications. The Valence Aware Dictionary for sEntiment Reasoning (VADER) model was used to calculate polarity scores. The National Research Council dataset...
Applicants to a single pediatric surgery fellowship were selected for this study from 2016 to 2021. A total of 182 individual applicants were included and 701 letters of recommendation were analyzed....
Black applicants had the highest mean polarity (most positive), while Hispanic applicants had the lowest. Overall differences between polarity distributions were not statistically significant. The int...
This study identified differences in LORs based on racial and gender demographics submitted as part of pediatric surgical fellowship applications to a single training program. The presence of bias in ...
From this work, it can be concluded that bias in LORs, as reflected as differences in polarity, which is likely a result of the intensity of the emotions being used and not the types of emotions being...
Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinica...
To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR)....
Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text...
We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type...
In the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lowe...
These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation....
