Objective: Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.

Readily available proxies for time of disease onset such as time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on current status and a large number of unlabeled observations on imperfect proxies.Under a semiparametric transformation model for onset and a highly flexible measurement error models for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initial estimator solely based on the labelled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semi-supervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Partners Biobank Electronic Health Records (EHR).

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BackgroundRecent initiatives in psychiatry emphasize the utility of characterizing psychiatric symptoms in a multidimensional manner. However, strategies for applying standard self-report scales for multiaxial assessment have not been well-studied, particularly where the aim is to support both categorical and dimensional phenotypes. MethodsWe propose a method for applying natural language processing to derive dimensional measures of psychiatric symptoms from questionnaire data. We utilized nine self-report symptom measures drawn from a large cellular biobanking study that enrolled individuals with mood and psychotic disorders, as well as healthy controls. To summarize questionnaire results we used word embeddings, a technique to represent words as numeric vectors preserving semantic and syntactic meaning. A low-dimensional approximation to the embedding space was used to derive the proposed succinct summary of symptom profiles. To validate our embedding-based disease profiles, these were compared to presence or absence of axis I diagnoses derived from structured clinical interview, and to objective neurocognitive testing. ResultsUnsupervised and supervised classification to distinguish presence/absence of axis I disorders using survey-level embeddings remained discriminative, with area under the receiver operating characteristic curve up to 0.85, 95% confidence interval (CI) (0.74,0.91) using Gaussian mixture modeling, and cross-validated area under the receiver operating

Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.