Discovering Patient Phenotypes Using Generalized Low Rank Models

The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a p henotype ), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets

Development of a decision support tool to facilitate primary care management of patients with abnormal liver function tests without clinically apparent liver disease [HTA03/38/02]. Abnormal Liver Function Investigations Evaluation (ALFIE)

Liver function tests (LFTs) are routinely performed in primary care, and are often the gateway to further invasive and/or expensive investigations. Little is known of the consequences in people with an initial abnormal liver function (ALF) test in primary care and with no obvious liver disease. Further investigations may be dangerous for the patient and expensive for Health Services. The aims of this study are to determine the natural history of abnormalities in LFTs before overt liver disease presents in the population and identify those who require minimal further investigations with the potential for reduction in NHS costs

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Representing and coding the knowledge embedded in texts of Health Science Web published articles

Despite the fact that electronic publishing is a common activity to scholars electronic journals are still based in the print model and do not take full advantage of the facilities offered by the Semantic Web environment. This is a report of the results of a research project with the aim of investigating the possibilities of electronic publishing journal articles both as text for human reading and in machine readable format recording the new knowledge contained in the article. This knowledge is identified with the scientific methodology elements such as problem, methodology, hypothesis, results, and conclusions. A model integrating all those elements is proposed which makes explicit and records the knowledge embedded in the text of scientific articles as an ontology. Knowledge thus represented enables its processing by intelligent software agents The proposed model aims to take advantage of these facilities enabling semantic retrieval and validation of the knowledge contained in articles. To validate and enhance the model a set of electronic journal articles were analyzed

Evaluating Prescriber Adherence to Guideline-Based Treatment Pathways of a Newly Initiated Antimicrobial Stewardship Program at a Rehabilitation Hospital

Background: Inappropriate use of antimicrobials in the healthcare setting is associated with consequences including antimicrobial resistance, Clostridium difficile infection (CDI), adverse drug reactions, and increased healthcare costs. To combat this, hospitals are creating antimicrobial stewardship programs (ASPs) which seek to optimize antimicrobial utilization. To date, no studies have been done to assess adherence to an ASP in a rehabilitation hospital setting. The objective of this study is to evaluate prescriber compliance to treatment pathways for common infections before and after ASP implementation. Methods: This was a retrospective cohort study of patients admitted to the Rehabilitation Hospital of Indiana (RHI) who received an antibiotic between October 1, 2015-December 31, 2015 (pre-ASP group) and January 1, 2016-September 30, 2016 (post-ASP group) for one of the following indications: pneumonia, urinary tract infection, CDI, bone and joint infection, skin or skin structure infection, febrile neutropenia, or central/peripherally inserted central catheter line bloodstream infection. Data extracted from the hospital’s electronic medical record system included patient demographic and clinical information, laboratory data, culture and susceptibility results, and antibiotic information. The primary outcome of this study was prescriber compliance to treatment pathways defined as correct drug based on the documented indication before and after the implementation of the antimicrobial stewardship program on January 1, 2016. Descriptive statistics were performed to analyze baseline characteristics and culture data, as well as antimicrobial class, indication, and overall compliance to the guideline-based treatment pathways. Results: Data was extracted from the hospital’s electronic medical record system for 381 patients (n=381) who received an antibiotic at RHI. There were 121 and 260 patients included in the pre- and post-ASP study groups, respectively. Urinary tract infections were the most common infection for which antibiotics were prescribed (n=293; 76.9%). The three most common antibiotics prescribed were ciprofloxacin (n=101; 26.5%), sulfamethoxazole/trimethoprim (n=81; 21.3%), and nitrofurantoin (n=49; 12.9%). Compliance was found to be 81% in the pre-ASP group and 78.5% in the post-ASP group (p=0.571). Overall compliance was found to be the highest (100% in both pre- and postASP groups) for osteomyelitis infections and CDI. Urinary tract infections had the next highest rate of compliance in both the pre- and post-ASP groups (86.5% and 81.7% respectively). Conclusions: No difference in rates of prescriber compliance to guideline-based treatment pathways was found in the pre- and post-ASP groups. Urinary tract infections were found to be the most common indication requiring antimicrobial usage at RHI and had the third highest rate of compliance out of the infections included in this study. Our study highlights a need for further investigation regarding the impact of the ASP on appropriate antimicrobial dose, duration of therapy, administration, and de-escalation based on culture data. Additionally, our study identified a need for formal prescriber education focusing on how to utilize the treatment pathways, especially for those infections with the lowest compliance rates