Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.

Objective To validate the use of electronic health records (EHRs) for the diagnosis of bipolar disorder (BD) and controls. Methods EHR data were obtained from a healthcare system of more than 4.2 million patients spanning more than 20 years. Chart review by experienced clinicians was used to identify text features and coded data consistent or inconsistent with a diagnosis of BD. Natural language processing (NLP) was used to train a diagnostic algorithm with 95% specificity for classifying BD. Filtered coded data were used to derive three additional classification rules for cases and one for controls. The positive predictive value (PPV) of EHR-based BD and subphenotype diagnoses was calculated against direct semi-structured interview diagnoses by trained clinicians blind to EHR diagnosis in a sample of 190 patients. Results The PPV of NLP-defined BD was 0.85. A coded classification based on strict filtering achieved a PPV of 0.79, but BD classifications based on less stringent criteria performed less well. None of the EHR-classified controls was given a diagnosis of BD on direct interview (PPV = 1.0). For most subphenotypes, PPVs exceeded 0.80. The EHR-based classifications were used to accrue 4500 BD cases and 5000 controls for genetic analyses. Conclusions Semi-automated mining of EHRs can be used to ascertain BD cases and controls with high specificity and predictive value compared to a gold-standard diagnostic interview. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.

Background There are unique challenges associated with measuring development in early childhood. Two primary sources of information are used: parent report and direct assessment. Each approach has strengths and weaknesses, particularly when used to identify and diagnose developmental delays. The present study aimed to evaluate consistency between parent report and direct assessment of child skills in toddlers with and without Autism Spectrum Disorder (ASD) across receptive language, expressive language, and fine motor domains. Method 109 children were evaluated at an average age of two years; data on child skills were collected via parent report and direct assessment. Children were classified into three groups (i.e., ASD, Other Developmental Disorder, or Typical Development) based on DSM-IV-TR diagnosis. Mixed design ANOVAs, with data source as a within subjects factor and diagnostic group as a between subjects factor, were used to assess agreement. Chi square tests of agreement were then used to examine correspondence at the item level. Results Results suggested that parent report of language and fine motor skills did not significantly differ from direct assessment, and this finding held across diagnostic groups. Item level analyses revealed that, in most cases of significant disagreement, parents reported a skill as present, but it was not seen on direct testing. Conclusions Results indicate that parents are generally reliable reporters of child language and fine motor abilities in toddlerhood, even when their children have developmental disorders such as ASD. However, the fullest picture may be obtained by using both parent report and direct assessment.