McCoy, T. H. | bioRxiv Science

bioRxiv2023

Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology

INTRODUCTIONTransdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records. METHODSWe adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimers disease (AD) neuropathological measures. RESULTSOur results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal ({beta}=0.38, p=0.0004), parietal ({beta}=0.35, p=0.0008), temporal ({beta}=0.37, p=0. 0004) and occipital ({beta}=0.37, p=0.0003) lobes. DISCUSSIONThis proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.

neuroscience↗

bioRxiv2019

High-throughput Phenotyping with Temporal Sequences

ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs are often underutilized in developing computational phenotypic definitions. The objective of this study is to develop a high-throughput phenotyping method, leveraging temporal sequential patterns of discrete events from electronic health records. Materials and MethodsWe develop a representation mining algorithm to extract five classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (AVR), the traditional immediate sequential patterns (SPM), the transitive sequential patterns (tSPM), as well as two hybrid classes of SPM+AVR and tSPM+AVR. A final small set of representations were selected from each class using the MSMR dimensionality reduction algorithm. Using EHR data on 10 phenotypes from Mass General Brigham Biobank, we trained regularized logistic regression algorithms, which we validated using labeled data. ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the AVR representations that are conventionally used in electronic phenotyping. Although this study only utilizes the diagnosis and medication records, the high-throughput algorithms classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations. DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. A transitive sequence can offer a more accurate characterization of the phenotype, compared with its individual components. Additionally, the identified transitive sequences of a given phenotype reflect the actual lived experiences of the patients with that particular disease. ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream Machine Learning.

bioinformatics↗