With the growing prevalence of cognitive impairment, early detection has become increasingly critical. Prior studies have examined the association between neuropsychiatric symptoms (NPS) and cognitive impairment, identifying potential predictive relationships. However, they hardly evaluated the heterogeneous relationships between serial patterns of NPS and evolving cognition status of the patients. To address this limitation, we investigate the statistical causal relationship between NPS and cognitive impairment, as well as the dynamic changes in their predictive effects over time, with a specific focus on sex differences. Our approach accounts for the fluctuating nature of NPS and varying follow-up durations across participants by implementing a bootstrap strategy that repeatedly samples a fixed number of visits per participant in a temporal order. Then, we apply causal discovery techniques and counterfactual framework-based causal inference methods to estimate the independent effects of NPS over time. Our findings highlight apathy as a key predictive symptom of cognitive impairment. Moreover, its predictive effect peaks earlier in females than in males, indicating that early-stage tracking is particularly informative in female participants. This suggests sex-specific monitoring strategies may improve early detection and intervention of cognitive impairment.

Mild Cognitive Impairment (MCI) is a transitional stage between normal cognitive aging and dementia. Some individuals with MCI revert to normal, while others progress to dementia. There are limited studies using explainable artificial intelligence on longitudinal data, particularly including genotypes, biomarkers and chronic diseases, to explore these differences. This study introduces a novel approach to understanding MCI progression using explainable graph neural networks. Utilizing longitudinal temporal data, we constructed a comprehensive graph representation of each individual in the study cohort. Our temporal graph convolutional network achieved 72.4% accuracy in predicting MCI transitions, while our causal explanation method outperformed existing explanation techniques in stability, accuracy, and faithfulness. We identified a causal subgraph with informative variables including hypertension, arrhythmia, congestive heart failure, coronary artery disease, stroke, lipid-related issues, and sex.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES: This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS: We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS: The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION: The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.

more than 44 million people have been diagnosed with dementia worldwide, and this number is estimated to triple by next three decades. Given this increasing trend of older adults with cognitive impairment (CI; dementia and mild cognitive impairment) and its significant underdiagnosis, early identification of CI and understanding its progression is a critical step towards a better quality of life for the aging population. Early alert of individual health changes could facilitate better ways for clinicians to diagnose CI in its early stages and come up with more effective treatment plans. However, there is a lack of approaches to characterize patient health conditions accounting for temporal information in an unsupervised manner. Limited CI cases and its costly ascertainment in clinical settings also make unsupervised learning more promising in CI research. In this paper, a streaming clustering model was used to determine distinct patterns of older adults' health changes from their clinical visits in Mayo Clinic Study of Aging. The streaming clustering was also examined to study its ability to generate early alerts for potential incidents of CI. Our analysis demonstrated that temporal characteristics incorporated in a streaming clustering model has a promising potential to increase power in predicting CI.

About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinely-collected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.

Healthcare data provides a wealth of information that can be used to study and improve patient outcomes. Electronic Medical Records and other sources of healthcare data are often managed in relational database system and archived using modern data warehousing techniques. Contemporary semantic database technology has many advantages over traditional database systems; however, the utility of the semantic data can be limited if the data is not converted properly from a tabular representation. There are a variety of tools which will naively convert tabular data into a Resource Description Format semantic graph. Without proper guidance from the operator, the tools will generate a semantically weak database which doesn't have the necessary richness for semantic analysis. This paper describes the conversion process for two healthcare databases, with the goal of creating a robust dataset for semantic analysis. The “lessons learned” from this process are detailed in order to serve as a resource for other biomedical researchers and clinicians interested in generating a useful semantic dataset from their own relational databases.

OBJECTIVE: We validated an algorithm designed to identify new or prevalent users of antidepressant medications via population-based drug prescription records. PATIENTS AND METHODS: We obtained population-based drug prescription records for the entire Olmsted County, Minnesota, population from 2011 to 2012 (N=149,629) using the existing electronic medical records linkage infrastructure of the Rochester Epidemiology Project (REP). We selected electronically a random sample of 200 new antidepressant users stratified by age and sex. The algorithm required the exclusion of antidepressant use in the 6 months preceding the date of the first qualifying antidepressant prescription (index date). Medical records were manually reviewed and adjudicated to calculate the positive predictive value (PPV). We also manually reviewed the records of a random sample of 200 antihistamine users who did not meet the case definition of new antidepressant user to estimate the negative predictive value (NPV). RESULTS: 161 of the 198 subjects electronically identified as new antidepressant users were confirmed by manual record review (PPV 81.3%). Restricting the definition of new users to subjects who were prescribed typical starting doses of each agent for treating major depression in non-geriatric adults resulted in an increase in the PPV (90.9%). Extending the time windows with no antidepressant use preceding the index date resulted in only modest increases in PPV. The manual abstraction of medical records of 200 antihistamine users yielded an NPV of 98.5%. CONCLUSIONS: Our study confirms that REP prescription records can be used to identify prevalent and incident users of antidepressants in the Olmsted County, Minnesota, population.