The performance of classic regression-based and modern tree-based variable selection methods is associated with the size of the clinical dataset used. Classic regression-based variable selection methods seem to achieve better parsimony in clinical prediction problems in smaller datasets while modern tree-based methods perform better in larger datasets.

A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Inappropriate coding of conditions leads to poor hospital performance measures and Medicare reimbursement penalties.

BACKGROUND: While concerns remain regarding Electronic Medical Records (EMR) use impeding doctor-patient communication, resident and faculty patient perspectives post-widespread EMR adoption remain largely unexplored. OBJECTIVE: We aimed to describe patient perspectives of outpatient resident and faculty EMR use and identify positive and negative EMR use examples to promote optimal utilization. DESIGN: This was a prospective mixed-methods study. PARTICIPANTS: Internal medicine faculty and resident patients at the University of Chicago's primary care clinic participated in the study. APPROACH: In 2013, one year after EMR implementation, telephone interviews were conducted with patients using open-ended and Likert style questions to elicit positive and negative perceptions of EMR use by physicians. Interview transcripts were analyzed qualitatively to develop a coding classification. Satisfaction with physician EMR use was examined using bivariate statistics. RESULTS: In total, 108 interviews were completed and analyzed. Two major themes were noted: (1) Clinical Functions of EMR and (2) Communication Functions of EMR; as well as six subthemes: (1a) Clinical Care (i.e., clinical efficiency), (1b) Documentation (i.e., proper record keeping and access), (1c) Information Access, (1d) Educational Resource, (2a) Patient Engagement and (2b) Physical Focus (i.e., body positioning). Overall, 85 % (979/1154) of patient perceptions of EMR use were positive, with the majority within the BClinical Care^subtheme (n = 218). Of negative perceptions, 66 % (115/175) related to the BCommunication Functions^theme, and the majority of those related to the BPhysical Focus^subtheme (n = 71). The majority of patients (90 %, 95/106) were satisfied with physician EMR use: 59 % (63/107) reported the computer had a positive effect on their relationship and only 7 % (8/108) reported the EMR made it harder to talk with their doctors. CONCLUSIONS: Despite concerns regarding EMRs impeding doctor-patient communication, patients reported largely positive perceptions of the EMR with many patients reporting high levels of satisfaction. Future work should focus on improving doctors Bphysical focus^when using the EMR to redirect towards the patient.KEY WORDS: electronic health records; patient-centered care; physician patient relations; communication skills.

Background Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. Methods and findings All adult consecutive patient admissions from 2008–2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P < 0.001) and the Sequential Organ Failure Assessment score (AUC: 0.91 vs. 0.57, P < 0.001) in the independent validation set. Class-activation heatmaps were utilized to highlight areas of the picture that were most important for making the prediction, thereby providing clinicians with insight into each individual patient’s prediction. Conclusions We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.