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AMIA ... Annual Symposium Proceedings

Sicheng Zhou, Hong Kang, Bin Yao, Yang Gong
Medication error is a severe patient safety event in the United States. Medication error reports collected by Patient Safety Organizations provide an opportunity to analyze and learn from previous errors. However, the current workflow of analyzing the error reports is labor-intensive and time-consuming. To reduce the workloads for clinicians and save time, we developed a pipeline for medication error report pre-analysis by applying automated text classification techniques. The pipeline was proven functional in two tasks, i...
2018: AMIA ... Annual Symposium Proceedings
Hansi Zhang, Zhe He, Xing He, Yi Guo, David R Nelson, François Modave, Yonghui Wu, William Hogan, Mattia Prosperi, Jiang Bian
The increasing adoption of electronic health record (EHR) systems and proliferation of clinical data offer unprecedented opportunities for cohort identification to accelerate patient recruitment. However, the effort required to translate trial eligibility criteria to the correct cohort identification queries for clinical investigators is substantial, at least in part due to the lack of clear definitions in both the free-text eligibility criteria and the data models used to structure the available data elements in target patient databases...
2018: AMIA ... Annual Symposium Proceedings
Zhijun Yin, Jeremy L Warner, Bradley A Malin
Hormonal therapy is an effective yet challenging long-term treatment for patients with hormone receptor positive breast cancer. Understanding what factors indicate discontinuation of a recommended hormonal therapy medication can help improve treatment experience. To date, studies on medication discontinuation have focused on patient information gathered through questionnaires, structured electronic medical records and online discussion boards. However, there has been little investigation into the communications between healthcare providers, which may provide additional indicators of patients' medication discontinuation, particularly from a clinical perspective...
2018: AMIA ... Annual Symposium Proceedings
Bin Yao, Hong Kang, Ju Wang, Sicheng Zhou, Yang Gong
To understand and prevent medication errors, spontaneous reporting systems are developed and implemented to aggregate medication error reports for root cause analysis (RCA). Despite of the rich relational information in medication error reports, low quality, especially incompleteness, impedes effective utilization of the reports for analyzing and learning. The lack of a completeness evaluation tool for narrative medication error reports is a barrier to improving the quality of reports. Moreover, no effective mechanisms are integrated in reporting systems for knowledge support upon reporting...
2018: AMIA ... Annual Symposium Proceedings
Chengliang Yang, Anand Rangarajan, Sanjay Ranka
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convo-lutional layer...
2018: AMIA ... Annual Symposium Proceedings
Sherrie Xie, Blanca E Himes
Electronic health record (EHR)-derived data has become an invaluable resource for biomedical research, but is seldom used for the study of the health impacts of social and environmental factors due in part to the unavailability of relevant variables. We describe how EHR-derived data can be enhanced via linking of external sources of social, economic and environmental data when patient-related geospatial information is available, and we illustrate an approach to better understand the geospatial patterns of asthma exacerbation rates in Philadelphia...
2018: AMIA ... Annual Symposium Proceedings
Qiang Wei, Amy Franklin, Trevor Cohen, Hua Xu
Building high-quality annotated clinical corpora is necessary for developing statistical Natural Language Processing (NLP) models to unlock information embedded in clinical text, but it is also time consuming and expensive. Consequently, it important to identify factors that may affect annotation time, such as syntactic complexity of the text- to-be-annotated and the vagaries of individual user behavior. However, limited work has been done to understand annotation of clinical text. In this study, we aimed to investigate how factors inherent to the text affect annotation time for a named entity recognition (NER) task...
2018: AMIA ... Annual Symposium Proceedings
Jiayi Tong, Jing Huang, Jingcheng Du, Yi Cai, Cui Tao, Yong Chen
In 2012, a new influenza vaccine - FLU4 was first licensed in the US. FLU4 is a quadrivalent flu vaccine, which can protect against four flu viruses. Compared to FLU and FLU3, FLU4 gives broader protection against the flu viruses. To our knowledge, few studies have focused on the FLU4 vaccine and its adverse events. Since safety signal detection is important in vaccination, it is necessary to launch such studies on FLU4. In this paper, we used the Vaccine Adverse Event Reporting System (VAERS), which is a national post-marketing vaccine safety surveillance program to identify rare adverse events with year-varying reporting rates for FLU4...
2018: AMIA ... Annual Symposium Proceedings
Carson Tao, Michele Filannino, Özlem Uzuner
Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions that are crucial for patients' well-being and is often detailed in the narrative portions of EHRs. As a result, narratives of EHRs need to be processed with natural language processing (NLP) methods that can extract medication and prescription information from free text. However, automatic methods for medication and prescription extraction from narratives face two major challenges: (1) dictionaries can fall short even when identifying well-defined and syntactically consistent categories of medication entities, (2) some categories of medication entities are sparse, and at the same time lexically (and syntactically) diverse...
2018: AMIA ... Annual Symposium Proceedings
Yuqi Si, Kirk Roberts
We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier...
2018: AMIA ... Annual Symposium Proceedings
Megan Shroder, Shilo H Anders, Marian Dorst, Gretchen P Jackson
The rapid evolution of communication technologies has created new ways for healthcare consumers to manage their health. In a mixed-methods study, we examined technology use and willingness to use in pregnant women and caregivers, using surveys and semi-structured interviews. Most participants had used text messaging, automated phone calls, Skype/FaceTime, social media, and online discussion forums. To communicate with healthcare providers, most were willing to use text messaging and had not, but desired to use Skype/FaceTime...
2018: AMIA ... Annual Symposium Proceedings
Feichen Shen, Hongfang Liu
Rare diseases, although individually rare, collectively affect one in ten Americans. Because of their rarity, patients with rare diseases are typically left misdiagnosed or undiagnosed, which leads to a prolonged medical journey. The diagnosis pathway of a rare disease is highly dependent on the associated clinical phenotypes, i.e., the observable characteristics, at the physical, morphologic, or biochemical level, of an individual. In our previous study, we applied a collaborative filtering model on clinical data generated at Mayo Clinic to stratify patients into subgroups of rare diseases...
2018: AMIA ... Annual Symposium Proceedings
Martin G Seneviratne, Juan M Banda, James D Brooks, Nigam H Shah, Tina M Hernandez-Boussard
Cancer stage is rarely captured in structured form in the electronic health record (EHR). We evaluate the performance of a classifier, trained on structured EHR data, in identifying prostate cancer patients with metastatic disease. Using EHR data for a cohort of 5,861 prostate cancer patients mapped to the Observational Health Data Sciences and Informatics (OHDSI) data model, we constructed feature vectors containing frequency counts of conditions, procedures, medications, observations and laboratory values...
2018: AMIA ... Annual Symposium Proceedings
Thomas J Reese, Kensaku Kawamoto, Guilherme Del Fiol, Frank Drews, Teresa Taft, Heidi Kramer, Charlene Weir
Introduction . Preventable adverse drug events are a significant patient-safety concern, yet most medication alerts are disregarded. Pharmacists encounter the highest number of medication alerts and likely have developed behaviors to cope with alerting inefficiencies. The study objective was to better understand alert override behavior relating to a motivational construct framework. Methods . Mixed-methods study of 10 pharmacists (567 verifications) with eye-tracking observations and retrospective think aloud interviews...
2018: AMIA ... Annual Symposium Proceedings
Aditya Ponnapalli, Adela Grando, Anita Murcko, Pete Wertheim
Prescription opioid abuse has become a serious national problem. To respond to the opioid epidemic, states have implemented prescription drug monitoring programs (PDMPs) to monitor and reduce opioid abuse. We conducted a systematic literature review to better understand the PDMP impact on reducing opioid abuse, improving prescriber practices, and how EHR integration has impacted PDMP usability. Lessons learned can help guide federal and state-based efforts to better respond to the opioid crisis.
2018: AMIA ... Annual Symposium Proceedings
Fernanda C G Polubriaginof, Ning Shang, George Hripcsak, Nicholas P Tatonetti, David K Vawdrey
Cardiovascular disease is the leading cause of death in the United States, and abnormal blood glucose is an important risk factor. Delayed diagnosis of diabetes mellitus can increase patients' morbidity. In an urban academic medical center with a large clinical data warehouse, we used a novel algorithm to identify 56,794 family members of diabetic patients that were eligible for disease screening. We found that 30.6% of patients did not receive diabetes screening as recommended by current guidelines. Further, our analysis showed that having more than one family member affected and being a female were important contributors to being screened for diabetes mellitus...
2018: AMIA ... Annual Symposium Proceedings
Panayiotis Petousis, Arash Naeim, Ali Mosleh, William Hsu
Risk prediction models are crucial for assessing the pretest probability of cancer and are applied to stratify patient management strategies. These models are frequently based on multivariate regression analysis, requiring that all risk factors be specified, and do not convey the confidence in their predictions. We present a framework for uncertainty analysis that accounts for variability in input values. Uncertain or missing values are replaced with a range of plausible values. These ranges are used to compute individualized risk confidence intervals...
2018: AMIA ... Annual Symposium Proceedings
Kevin J Peterson, Hongfang Liu
Summary-level clinical text is an important part of the overall clinical record as it provides a condensed and efficient view into the issues pertinent to the patient, or their "problem list." These problem lists contain a wealth of information pertaining to the patient's history as well as current state and well-being. In this study, we explore the structure of these problem list entries both grammatically and semantically in an attempt to learn the specialized rules, or "sublanguage" that governs them...
2018: AMIA ... Annual Symposium Proceedings
Jay Patel, Danielle Mowery, Anand Krishnan, Thankam Thyvalikakath
Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it's unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients' self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR)...
2018: AMIA ... Annual Symposium Proceedings
Mina Ostovari, Denny Yu, Charlotte Joy Steele-Morris
Determining networks of healthcare providers quantitatively can identify impactful care processes that improve health outcomes for a high-risk populations such as elderly people with multiple chronic conditions. By applying social network analysis to health claim data of a large university in the Midwest, we measured healthcare provider networks of patients with diabetes for two consecutive years. Networks were built based on the assumption that having common patients may indicate potential working relationships between providers...
2018: AMIA ... Annual Symposium Proceedings
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