A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

Evaluating the implementation of an online clinical log system for family nurse practitioner students.

This pilot study analyzed the adoption of the Nurse Practitioner Student Tracking (NPST) system by a public university's family nurse practitioner graduate program. Using the diffusion-of-innovations research framework, the ease of transition, acceptance, and perceived advantage of the system were studied in a unique group of students (n = 9) and faculty members (n = 6). The study results pointed to a more rapid progression through the stages of technological adoption for students than for faculty members, a more pronounced acceptance of the database by students than by faculty, a higher learning curve for faculty than for students, and improved efficiency in clinical logging for students. The NPST system served to begin addressing identified issues within the program. Half (n = 3) of the faculty thought that the NPST system assisted them in ensuring student competencies were met. The system also helped them analyze how time was spent in clinical practice, identify how much time was spent conferring with preceptor in clinical practice, helped students improve the quality of their charting practices, and eliminated the concern of dishonest duplication of records. In addition, 80% of faculty stated the NPST system helped them see how much time was spent with each patient in clinical practice.

Chronic hepatitis B infection: a workshop consensus statement and algorithm.

Here, presented with an evidence-based algorithm, are workshop consensus recommendations on whom to screen for hepatitis B and when to pursue further evaluation and management.