Dataset Information

Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

ABSTRACT: We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychiatric disorders in clinical narrative notes). The highest classification performance of our algorithm was achieved when it included a combination of structured variables (billing codes for insomnia, common psychiatric conditions, and joint disorders) and unstructured variables (sleep disorders and psychiatric disorders). Our algorithm had superior performance in identifying insomnia patients compared to billing codes alone (area under the receiver operating characteristic curve [AUROC]?=?0.83 vs. 0.55 with 95% confidence intervals [CI] of 0.76-0.90 and 0.51-0.58, respectively). When applied to the 314,292-patient population, our algorithm classified 36,810 of the patients with insomnia, of which less than 17% had a billing code for insomnia. In conclusion, an insomnia classification algorithm that incorporates clinical notes is superior to one based solely on billing codes. Compared to traditional methods, our study demonstrates that a classification algorithm that incorporates physician notes can more accurately, comprehensively, and quickly identify large cohorts of insomnia patients.

SUBMITTER: Kartoun U

PROVIDER: S-EPMC5959894 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

Kartoun Uri U Aggarwal Rahul R Beam Andrew L AL Pai Jennifer K JK Chatterjee Arnaub K AK Fitzgerald Timothy P TP Kohane Isaac S IS Shaw Stanley Y SY

Scientific reports 20180518 1

We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychi ...[more]

PMID: 29777125

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Project description:Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.

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Project description:Neonatal diabetes mellitus (NDM) is noted as a genetic, heterogeneous, and rare disease in infants. NDM occurs due to a single-gene mutation in neonates. A common source for developing NDM in an infant is the existence of mutations/variants in the KCNJ11 and ABCC8 genes, encoding the subunits of the voltage-dependent potassium channel. Both KCNJ11 and ABCC8 genes are useful in diagnosing monogenic diabetes during infancy. Genetic analysis was previously performed using first-generation sequencing techniques, such as DNA-Sanger sequencing, which uses chain-terminating inhibitors. Sanger sequencing has certain limitations; it can screen a limited region of exons in one gene, but it cannot screen large regions of the human genome. In the last decade, first generation sequencing techniques have been replaced with second-generation sequencing techniques, such as next-generation sequencing (NGS), which sequences nucleic-acids more rapidly and economically than Sanger sequencing. NGS applications are involved in whole exome sequencing (WES), whole genome sequencing (WGS), and targeted gene panels. WES characterizes a substantial breakthrough in human genetics. Genetic testing for custom genes allows the screening of the complete gene, including introns and exons. The aim of this review was to confirm if the 22 genetic variations previously documented to cause NDM by Sanger sequencing could be detected using second generation sequencing techniques. The author has cross-checked global studies performed in NDM using NGS, ES/WES, WGS, and targeted gene panels as second-generation sequencing techniques; WES confirmed the similar variants, which have been previously documented with Sanger sequencing. WES is documented as a powerful tool and WGS as the most comprehensive test for verified the documented variants, as well as novel enhancers. This review recommends for the future studies should be performed with second generation sequencing techniques to identify the verified 22 genetic and novel variants by screening in NDM (PNDM or TNMD) children.

Dataset Information

Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

Publications

Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

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