An end-to-end machine learning system for harmonic analysis of music

We present a new system for simultaneous estimation of keys, chords, and bass notes from music audio. It makes use of a novel chromagram representation of audio that takes perception of loudness into account. Furthermore, it is fully based on machine learning (instead of expert knowledge), such that it is potentially applicable to a wider range of genres as long as training data is available. As compared to other models, the proposed system is fast and memory efficient, while achieving state-of-the-art performance.Comment: MIREX report and preparation of Journal submissio

Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children

AbstractBackgroundEarly warning scores (EWS) are designed to identify early clinical deterioration by combining physiologic and/or laboratory measures to generate a quantified score. Current EWS leverage only a small fraction of Electronic Health Record (EHR) content. The planned widespread implementation of EHRs brings the promise of abundant data resources for prediction purposes. The three specific aims of our research are: (1) to develop an EHR-based automated algorithm to predict the need for Pediatric Intensive Care Unit (PICU) transfer in the first 24h of admission; (2) to evaluate the performance of the new algorithm on a held-out test data set; and (3) to compare the effectiveness of the new algorithm's with those of two published Pediatric Early Warning Scores (PEWS).MethodsThe cases were comprised of 526 encounters with 24-h Pediatric Intensive Care Unit (PICU) transfer. In addition to the cases, we randomly selected 6772 control encounters from 62516 inpatient admissions that were never transferred to the PICU. We used 29 variables in a logistic regression and compared our algorithm against two published PEWS on a held-out test data set.ResultsThe logistic regression algorithm achieved 0.849 (95% CI 0.753–0.945) sensitivity, 0.859 (95% CI 0.850–0.868) specificity and 0.912 (95% CI 0.905–0.919) area under the curve (AUC) in the test set. Our algorithm's AUC was significantly higher, by 11.8 and 22.6% in the test set, than two published PEWS.ConclusionThe novel algorithm achieved higher sensitivity, specificity, and AUC than the two PEWS reported in the literature

Automated detection of medication administration errors in neonatal intensive care

AbstractObjectiveTo improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.MethodsWe developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.ResultsIn the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.ConclusionsAutomated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows.�This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context.�Steps required for mediation analyses in the seasonality model are shown.�The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study

Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. Objective: To identify built environment characteristics predictive of rapid CF lung function decline. Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [−0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. Conclusion: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions

Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder

Objective: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. Methods: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children’s Hospital (BCH) (N = 150) and Cincinnati Children’s Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. Results: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children’s Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. Conclusions: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD

Distance phrase reordering for MOSES - User Manual and Code Guide

We describe the implementation of a novel distance phrase reordering (DPR) model for a public domain statistical machine translation (SMT) system - MOSES (http://www.statmt.org/moses/). The model mainly focuses on the application of machine learning (ML) techniques to a specific problem in machine translation: learning the grammatical rules and content dependent changes, which are simplified as phrase reorderings. This document serves two purposes: a user manual for the functions of the DPR model and a code guide for developers

Kernel methods for fmri pattern prediction

In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artifacts, spatial smoothing, removing low frequency drifts and applying multivariate linear and non-linear kernel methods. Two novel techniques are applied: one utilizes the Cosine Transform to remove low-frequency drifts over time and the other involves using prior knowledge about the spatial contribution of different brain regions for the various tasks. Our experiment results on the PBAIC2007 competition data set show a great improvement for brain activity prediction, especially on some sensory experience such as hearing and vision

Handling phrase reordering for machine translation

We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to capture phrase reorderings using a structure learning framework. On both the reordering classification and a Chinese-to-English translation task, we show improved performance over a baseline SMT system