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16 results for “medical coding”
Figure 3 in Implementation of the QR Code system in the Medical Malacology Collection of the René Rachou Institute, Oswaldo Cruz Foundation
Figure 3. Use of the QR Code in Fiocruz-CMM. Carrying a cell phone with a camera and internet access, the user photographs the QR Code and has access to a spreadsheet with all the data related to the specimen of interest.
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
Additional File 1 and Notebook Code for "Machine learning approaches for hospital acquired pressure injuries: a retrospective study of electronic medical records"
<p>Supplementary materials (Pressure_Injuries_Additional_File_1_final_double_blind.pdf) and Jupyter Notebook code (HAPI_Prediction_Script.pdf) developed as supplement for study "Machine learning approaches for hospital acquired pressure injuries: a retrospective study of electronic medical records".</p>
Checklist-guided Shared Decision-making for Code Status Discussions in Medical Inpatients.
ClinicalTrials.gov study NCT03872154. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Integral Management of Healthcare Problems Related With Drugs in Polimedicated Patients: Medication Code
ClinicalTrials.gov study NCT03607097. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Figure 4 in Implementation of the QR Code system in the Medical Malacology Collection of the René Rachou Institute, Oswaldo Cruz Foundation
Figure 4. Spreadsheet with data retrieved by reading the QR Code for a specimen with the data available in the digital Inventory Book of the Medical Malacology Collection.
Figure 2 in Implementation of the QR Code system in the Medical Malacology Collection of the René Rachou Institute, Oswaldo Cruz Foundation
Figure 2. QR code used in Fiocruz-CMM. Flask containing specimen 17003, labeled with the QR code that redirects the user to data related to this specimen (A). QR Code that redirects to data related to sample 17003 (B).
Figure 1 in Implementation of the QR Code system in the Medical Malacology Collection of the René Rachou Institute, Oswaldo Cruz Foundation
Figure 1. Schematic representation of the system developed for reading the inventory book of the Medical Malacology Collection and compiling the specimen data for individual QR Codes affixed to the recipients containing the specimens.
Dataset for MedCodER: A Generative AI Assistant for Medical Coding
Open the record for dataset details and reuse information.
Study to Validate Coded Medical Terms Used to Identify Opioid-Related Overdose in Databases Used for PMR Study 1B
ClinicalTrials.gov study NCT02667197. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Physicians in Training and Critical Care Nurses Performance in Medical Code Events: Effect of Simulation-Based Training
ClinicalTrials.gov study NCT02707185. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detection Medication Administration Errors Using Bar-code and RFID Technology (DREAM)
ClinicalTrials.gov study NCT02325336. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Determinants of Time Required by Medical Information Technicians for Quality Control of Hospital Activity Coding
ClinicalTrials.gov study NCT03977636. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Sentiment analysis in medication adherence: full code
<p>This database, associated with the research article on sentiment analysis in medication adherence, comprises an extensive collection of files essential for understanding and replicating the study's findings. It includes 362,806 anonymized medication reviews, meticulously analyzed to explore the correlation between patient sentiments and medication adherence.</p> <p><strong>Organize folders and code:</strong></p> <pre><code>MedicationAdherenceSentimentAnalysis/ │ ├── 01 - Original dataset/ │ └── originalDataset.csv │ ├── 02 - Dataset cleaning/ │ ├── CleaningStep1.py │ ├── CleaningStep2.py │ ├── CleaningStep3.py │ ├── CleaningStep4.py │ ├── Balancing.py │ ├── CleanedDatasetStep1.csv │ ├── CleanedDatasetStep2.csv │ ├── CleanedDatasetStep3.csv │ └── CleanedDatasetStep4.csv │ └── FinalBalancedDataset.csv │ ├── 03 - Vader analysis/ │ ├── vaderAnalysis.py │ └── vaderResults.csv │ ├── 04 - DistilRoBERTa analysis/ │ ├── DistilRoBERTa_Analysis.py │ └── DistilRoBERTa_Results.csv │ ├── 05 - Dataset report/ │ ├── datasetReport.py │ ├── filtredNegativeMeds.py │ ├── filtredPositiveMeds.py │ └── Outputs printed in terminal (Note: Terminal outputs are not stored as files) │ ├── 06 - Charts/ │ ├── charts.py │ ├── sentimentByLikert.html │ ├── vaderVsDistilRoBERTa.html │ ├── emotionsByLevelOfEffectiveness.html │ ├── emotionsByLevelOfEaseofuse.html │ └── emotionsByLevelOfSatisfaction.html │ └── 07 - Model metrics/ ├── modelMetrics.py └── dataserForMetrics.csv </code></pre> <p> </p> <p><strong>Contents:</strong></p> <ol> <li>Anonymized Medication Reviews: The original dataset must be downloaded from the original source indicated in "ReadmeDataset.docx". This is comprehensive dataset of 362,806 medication reviews, anonymized to protect patient privacy. These reviews serve as the primary data source for the sentiment analysis conducted in the study.</li> <li>Sentiment Analysis Results: Detailed files containing the output of the sentiment analysis performed using VADER and DistilRoBERTa models. This includes sentiment polarities, emotional responses, and their correlation with the perceived effectiveness, ease of use, and satisfaction reported by patients.</li> <li>Statistical Analysis Files: Contains the statistical tests and analysis results that establish the significant correlations between sentiment polarities and patient perceptions, as discussed in the article.</li> <li>Methodology Documentation: Detailed documentation of the methodologies used, including the application of AI tools like VADER and DistilRoBERTa for sentiment analysis. This section aids in replicating the study's approach for further research.</li> <li>Supplementary Information: Additional files that support the article's content, possibly including code snippets used for analysis, raw data processing details, and any other supplementary materials that contribute to the transparency and reproducibility of the research.</li> </ol> <p><strong>Usage:</strong></p> <p>This database is intended for researchers, clinicians, and academicians interested in exploring the intersection of artificial intelligence, sentiment analysis, and clinical pharmacy. It provides a rich resource for understanding patient sentiments towards medications and their potential impact on adherence. Researchers can utilize this data to replicate the study, conduct further analyses, or explore new hypotheses in the realm of health informatics and patient care optimization.</p>
Data associated with "Exploring Prescribing Trends: An R Shiny App for Visualizing the Top 100 Most Commonly Prescribed Medications and Their Distribution by ATC Code"
Open the record for dataset details and reuse information.
An R Shiny App for Visualizing the Top 100 Most Commonly Prescribed Medications and Their Distribution by ATC Code"
Open the record for dataset details and reuse information.
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OpenNeuro
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