Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records

Abstract

Background Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. New approaches are therefore necessary to facilitate the timely detection of dementia. This study seeks to identify cases of undiagnosed dementia by developing and validating a weakly supervised machine-learning approach that incorporates the analysis of both structured and unstructured electronic health record (EHR) data. Methods A topic modeling approach that included latent Dirichlet allocation, stable topic extraction, and random sampling was applied to VHA EHRs. Topic features from unstructured data and features from structured data were compared between Veterans with ( n = 1861) and without ( n = 9305) ICD-9 dementia codes. A logistic regression model was used to develop dementia prediction scores, and manual reviews were conducted to validate the machine-learning results. Results A total of 853 features were identified (290 topics, 174 non-dementia ICD codes, 159 CPT codes, 59 medications, and 171 note types) for the development of logistic regression prediction scores. These scores were validated in a subset of Veterans without ICD-9 dementia codes ( n = 120) by experts in dementia who performed manual record reviews and achieved a high level of inter-rater agreement. The manual reviews were used to develop a receiver of characteristic (ROC) curve with different thresholds for case detection, including a threshold of 0.061, which produced an optimal sensitivity (0.825) and specificity (0.832). Conclusions Dementia is underdiagnosed, and thus, ICD codes alone cannot serve as a gold standard for diagnosis. However, this study suggests that imperfect data (e.g., ICD codes in combination with other EHR features) can serve as a silver standard to develop a risk model, apply that model to patients without dementia codes, and then select a case-detection threshold. The study is one of the first to utilize both structured and unstructured EHRs to develop risk scores for the diagnosis of dementia.

Alzheimer’s Disease and Alzheimer’s Disease-Related Dementias in Older African American and White Veterans

“…First, it was based on the Veteran populations, which is predominantly male that therefore limits generalizability. Second, we used diagnostic codes based on structured data to define AD/ADRD, which may have underestimated true incidence [22,39,40]. Third, we did not account for the severity of comorbid conditions, medications, or laboratory data.…”

Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

“…LDA methods assume that each text grouping is a mixture of topics and that such topics are a probabilistic mixture of words. Previous studies have applied these methods to clinical notes to identify topics predictive of specific diseases such as dementia [ 46 ] and heart failure [ 47 ]. In theory, these methods could be used to predict such outcomes (through notes alone) before a diagnosis.…”

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review

“…EHR may also contain some sociodemographic and clinical data mentioned above, depending on the vendor and/or health organization. The four highlighted studies in this section used nationwide administrative claims data (Nori et al, 2019), EHR from a regional Veterans Affairs (VA) healthcare system (a publicly administered program) (Shao et al, 2019), EHR from a regional not-for-profit academic healthcare system (Wang et al, 2019), and EHR from two hospital-based samples (Wang et al, 2018). These datasets record and help to manage patient care and offer a relatively inexpensive source of information collected over long time periods on large numbers of patients.…”

“…The quality and quantity of EHR data are also dependent on external factors (e.g., severity of illness, insurance rules, regional practices, availability of resources) and are heterogeneous in organization and level of detail. For example, the findings from a regional VA health system (as in Shao et al, 2019) may be more representative of care at other VA health systems, whereas there may be considerable regional differences within other nationwide insurers (e.g., Blue Cross Blue Shield versus Kaiser Permanente) due to different patient populations and plan structures. AI will be particularly useful with these data if it can "learn" the different styles of documentation from different providers and different healthcare systemsa excellent area for NLP applications.…”