Multivariate analyses for the associations of health literacy with mammography by ethnicity and language-preference acculturation.

<p>Multivariate analyses for the associations of health literacy with mammography by ethnicity and language-preference acculturation.</p

Sample characteristics by ethnicity and language-preference acculturation.

<p>Sample characteristics by ethnicity and language-preference acculturation.</p

Multivariate analyses for the associations of health literacy with mammography by ethnicity and language-preference acculturation.

<p>Multivariate analyses for the associations of health literacy with mammography by ethnicity and language-preference acculturation.</p

Sample characteristics by ethnicity and language-preference acculturation.

<p>Sample characteristics by ethnicity and language-preference acculturation.</p

Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review

Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the ‘free-text’ or ‘unstructured’ format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking. This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications. Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.</p

Developing Item Banks for Measuring Pediatric Generic Health-Related Quality of Life: An Application of the International Classification of Functioning, Disability and Health for Children and Youth and Item Response Theory

<div><p>The purpose of this study was to develop item banks by linking items from three pediatric health-related quality of life (HRQoL) instruments using a mixed methodology. Secondary data were collected from 469 parents of children aged 8-16 years. The International Classification of Functioning, Disability and Health-Children and Youth (ICF-CY) served as a framework to compare the concepts of items from three HRQoL instruments. The structural validity of the individual domains was examined using confirmatory factor analyses. Samejima's Graded Response Model was used to calibrate items from different instruments. The known-groups validity of each domain was examined using the status of children with special health care needs (CSHCN). Concepts represented by the items in the three instruments were linked to 24 different second-level categories of the ICF-CY. Eight item banks representing eight unidimensional domains were created based on the linkage of the concepts measured by the items of the three instruments to the ICF-CY. The HRQoL results of CSHCN in seven out of eight domains (except personality) were significantly lower compared with children without special health care needs (<i>p</i><0.05). This study demonstrates a useful approach to compare the item concepts from the three instruments and to generate item banks for a pediatric population.</p></div

Known-groups validity for individual domain scores among children with special health care needs (CSCHN) compared with children without special health care needs.

<p>** <i>p</i><0.01.</p><p>*** <i>p</i><0.001.</p>#<p> Controlling for child's age and gender and parent's race and education.</p><p>Known-groups validity for individual domain scores among children with special health care needs (CSCHN) compared with children without special health care needs.</p

Specific effect of personality characteristics on HRQOL.

<p>Specific effect of personality characteristics on HRQOL.</p

Characteristics of reviewed studies of personality characteristics and HRQOL.

<p>Characteristics of reviewed studies of personality characteristics and HRQOL.</p

Demographic characteristics (N = 469).

<p>SD: standard deviation.</p><p>Demographic characteristics (N = 469).</p