Formative evaluation of a patient-specific clinical knowledge summarization tool

To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians’ perceived decision quality compared with standard search of UpToDate and PubMed

Identification and Use of Frailty Indicators from Text to Examine Associations with Clinical Outcomes Among Patients with Heart Failure.

Frailty is an important health outcomes indicator and valuable for guiding healthcare decisions in older adults, but is rarely collected in a quantitative, systematic fashion in routine healthcare. Using a cohort of 12,000 Veterans with heart failure, we investigated the feasibility of topic modeling to identify frailty topics in clinical notes. Topics were generated through unsupervised learning and then manually reviewed by an expert. A total of 53 frailty topics were identified from 100,000 notes. We further examined associations of frailty with age-, sex-, and Charlson Comorbidity Index-adjusted 1-year hospitalizations and mortality (composite outcome) using logistic regression. Frailty (≤ 4 topics versu

Text summarization in the biomedical domain: A systematic review of recent research

The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data

Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate. In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data