Qing Zeng-Treitler

Electronic medical records for discovery research in rheumatoid arthritis

Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone.

Developing Informatics Tools and Strategies for Consumer-centered Health Communication

As the emphasis on individuals active partnership in health care grows, so does the publics need for effective, comprehensible consumer health resources. Consumer health informatics has the potential to provide frameworks and strategies for designing effective health communication tools that empower users and improve their health decisions. This article presents an overview of the consumer health informatics field, discusses promising approaches to supporting health communication, and identifies challenges plus direction for future research and development. The authors recommendations emphasize the need for drawing upon communication and social science theories of information behavior, reaching out to consumers via a range of traditional and novel formats, gaining better understanding of the publics health information needs, and developing informatics solutions for tailoring resources to users needs and competencies. This article was written as a scholarly outreach and leadership project by members of the American Medical Informatics Associations Consumer Health Informatics Working Group.

Consumer Health Concepts That Do Not Map to the UMLS: Where Do They Fit?

OBJECTIVEnThis study has two objectives: first, to identify and characterize consumer health terms not found in the Unified Medical Language System (UMLS) Metathesaurus (2007 AB); second, to describe the procedure for creating new concepts in the process of building a consumer health vocabulary. How do the unmapped consumer health concepts relate to the existing UMLS concepts? What is the place of these new concepts in professional medical discourse?nnnDESIGNnThe consumer health terms were extracted from two large corpora derived in the process of Open Access Collaboratory Consumer Health Vocabulary (OAC CHV) building. Terms that could not be mapped to existing UMLS concepts via machine and manual methods prompted creation of new concepts, which were then ascribed semantic types, related to existing UMLS concepts, and coded according to specified criteria.nnnRESULTSnThis approach identified 64 unmapped concepts, 17 of which were labeled as uniquely lay and not feasible for inclusion in professional health terminologies. The remaining terms constituted potential candidates for inclusion in professional vocabularies, or could be constructed by post-coordinating existing UMLS terms. The relationship between new and existing concepts differed depending on the corpora from which they were extracted.nnnCONCLUSIONnNon-mapping concepts constitute a small proportion of consumer health terms, but a proportion that is likely to affect the process of consumer health vocabulary building. We have identified a novel approach for identifying such concepts.

Computer-Assisted Update of a Consumer Health Vocabulary Through Mining of Social Network Data

Background Consumer health vocabularies (CHVs) have been developed to aid consumer health informatics applications. This purpose is best served if the vocabulary evolves with consumers’ language. Objective Our objective was to create a computer assisted update (CAU) system that works with live corpora to identify new candidate terms for inclusion in the open access and collaborative (OAC) CHV. Methods The CAU system consisted of three main parts: a Web crawler and an HTML parser, a candidate term filter that utilizes natural language processing tools including term recognition methods, and a human review interface. In evaluation, the CAU system was applied to the health-related social network website PatientsLikeMe.com. The system’s utility was assessed by comparing the candidate term list it generated to a list of valid terms hand extracted from the text of the crawled webpages. Results The CAU system identified 88,994 unique terms 1- to 7-grams (“n-grams” are n consecutive words within a sentence) in 300 crawled PatientsLikeMe.com webpages. The manual review of the crawled webpages identified 651 valid terms not yet included in the OAC CHV or the Unified Medical Language System (UMLS) Metathesaurus, a collection of vocabularies amalgamated to form an ontology of medical terms, (ie, 1 valid term per 136.7 candidate n-grams). The term filter selected 774 candidate terms, of which 237 were valid terms, that is, 1 valid term among every 3 or 4 candidates reviewed. Conclusion The CAU system is effective for generating a list of candidate terms for human review during CHV development.

Assessment of Pictographs Developed Through a Participatory Design Process Using an Online Survey Tool

Background Inpatient discharge instructions are a mandatory requirement of the Centers for Medicare and Medicaid Services and Joint Commission on Accreditation of Healthcare Organizations. The instructions include all the information relevant to post-discharge patient care. Prior studies show that patients often cannot fully understand or remember all the instructions. To address this issue, we have previously conducted a pilot study in which pictographs were created through a participatory design process to facilitate the comprehension and recall of discharge instructions. Objective The main objective of this study was to verify the individual effectiveness of pictographs created through a participatory design process. Methods In this study, we included 20 pictographs developed by our group and 20 pictographs developed by the Robert Wood Johnson Foundation as a reference baseline for pictographic recognition. To assess whether the participants could recognize the meaning of the pictographs, we designed an asymmetrical pictograph–text label-linking test. Data collection lasted for 7 days after the email invitation. A total of 44 people accessed the survey site. We excluded 7 participants who completed less than 50% of the survey. A total of 719 answers from 37 participants were analyzed. Results The analysis showed that the participants recognized the pictographs developed in-house significantly better than those included in the study as a baseline (P< .001). This trend was true regardless of the participant’s gender, age, and education level. The results also revealed that there is a large variance in the quality of the pictographs developed using the same design process—the recognition rate ranged from below 50% to above 90%. Conclusions This study confirmed that the majority of the pictographs developed in a participatory design process involving a small number of nurses and consumers were recognizable by a larger number of consumers. The variance in recognition rates suggests that pictographs should be assessed individually before being evaluated within the context of an application.

Limbic system white matter microstructure and long-term treatment outcome in major depressive disorder: A diffusion tensor imaging study using legacy data

Abstract Objectives. Treatment-resistant depression is a common clinical occurrence among patients with major depressive disorder (MDD), but its neurobiology is poorly understood. We used data collected as part of routine clinical care to study white matter integrity of the brains limbic system and its association to treatment response. Methods. Electronic medical records of multiple large New England hospitals were screened for patients with an MDD billing diagnosis, and natural language processing was subsequently applied to find those with concurrent diffusion-weighted images, but without any diagnosed brain pathology. Treatment outcome was determined by review of clinical charts. MDD patients (n = 29 non-remitters, n = 26 partial-remitters, and n = 37 full-remitters), and healthy control subjects (n = 58) were analyzed for fractional anisotropy (FA) of the fornix and cingulum bundle. Results. Failure to achieve remission was associated with lower FA among MDD patients, statistically significant for the medial body of the fornix. Moreover, global and regional-selective age-related FA decline was most pronounced in patients with treatment-refractory, non-remitted depression. Conclusions. These findings suggest that specific brain microstructural white matter abnormalities underlie persistent, treatment-resistant depression. They also demonstrate the feasibility of investigating white matter integrity in psychiatric populations using legacy data.

Estimating Consumer Familiarity with Health Terminology: A Context-based Approach

OBJECTIVESnEffective health communication is often hindered by a vocabulary gap between language familiar to consumers and jargon used in medical practice and research. To present health information to consumers in a comprehensible fashion, we need to develop a mechanism to quantify health terms as being more likely or less likely to be understood by typical members of the lay public. Prior research has used approaches including syllable count, easy word list, and frequency count, all of which have significant limitations.nnnDESIGNnIn this article, we present a new method that predicts consumer familiarity using contextual information. The method was applied to a large query log data set and validated using results from two previously conducted consumer surveys.nnnMEASUREMENTSnWe measured the correlation between the survey result and the context-based prediction, syllable count, frequency count, and log normalized frequency count.nnnRESULTSnThe correlation coefficient between the context-based prediction and the survey result was 0.773 (p < 0.001), which was higher than the correlation coefficients between the survey result and the syllable count, frequency count, and log normalized frequency count (p < or = 0.012).nnnCONCLUSIONSnThe context-based approach provides a good alternative to the existing term familiarity assessment methods.

Active learning for clinical text classification: is it better than random sampling?

OBJECTIVEnThis study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks.nnnDESIGNnThree existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results.nnnMEASUREMENTSnClassification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences.nnnRESULTSnThe DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm.nnnCONCLUSIONnFor medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.

Mining Online Social Network Data for Biomedical Research: A Comparison of Clinicians' and Patients' Perceptions About Amyotrophic Lateral Sclerosis Treatments

Background While only one drug is known to slow the progress of amyotrophic lateral sclerosis (ALS), numerous drugs can be used to treat its symptoms. However, very few randomized controlled trials have assessed the efficacy, safety, and side effects of these drugs. Due to this lack of randomized controlled trials, consensus among clinicians on how to treat the wide range of ALS symptoms and the efficacy of these treatments is low. Given the lack of clinical trials data, the wide range of reported symptoms, and the low consensus among clinicians on how to treat those symptoms, data on the prevalence and efficacy of treatments from a patient’s perspective could help advance the understanding of the symptomatic treatment of ALS. Objective To compare clinicians’ and patients’ perspectives on the symptomatic treatment of ALS by comparing data from a traditional survey study of clinicians with data from a patient social network. Methods We used a survey of clinicians’ perceptions by Forshew and Bromberg as our primary data source and adjusted the data from PatientsLikeMe to allow for comparisons. We first extracted the 14 symptoms and associated top four treatments listed by Forshew and Bromberg. We then searched the PatientsLikeMe database for the same symptom–treatment pairs. The PatientsLikeMe data are structured and thus no preprocessing of the data was required. Results After we eliminated pairs with a small sample, 15 symptom–treatment pairs remained. All treatments identified as useful were prescription drugs. We found similarities and discrepancies between clinicians’ and patients’ perceptions of treatment prevalence and efficacy. In 7 of the 15 pairs, the differences between the two groups were above 10%. In 3 pairs the differences were above 20%. Lorazepam to treat anxiety and quinine to treat muscle cramps were among the symptom–treatment pairs with high concordance between clinicians’ and patients’ perceptions. Conversely, amitriptyline to treat labile emotional effect and oxybutynin to treat urinary urgency displayed low agreement between clinicians and patients. Conclusions Assessing and comparing the efficacy of the symptomatic treatment of a complex and rare disease such as ALS is not easy and needs to take both clinicians’ and patients’ perspectives into consideration. Drawing a reliable profile of treatment efficacy requires taking into consideration many interacting aspects (eg, disease stage and severity of symptoms) that were not covered in the present study. Nevertheless, pilot studies such as this one can pave the way for more robust studies by helping researchers anticipate and compensate for limitations in their data sources and study design.

A bootstrapping algorithm to improve cohort identification using structured data

Cohort identification is an important step in conducting clinical research studies. Use of ICD-9 codes to identify disease cohorts is a common approach that can yield satisfactory results in certain conditions; however, for many use-cases more accurate methods are required. In this study, we propose a bootstrapping method that supplements ICD-9 codes with lab results, medications, etc. to build classification models that can be used to identify cohorts more accurately. The proposed method does not require prior information about the true class of the patients. We used the method to identify Diabetes Mellitus (DM) and Hyperlipidemia (HL) patient cohorts from a database of 800 thousand patients. Evaluation results show that the method identified 11,000 patients who did not have DM related ICD-9 codes as positive for DM and 52,000 patients without HL codes as positive for HL. A review of 400 patient charts (200 patients for each condition) by two clinicians shows that in both the conditions studied, the labeling assigned by the proposed approach is more consistent with that of the clinicians compared to labeling through ICD-9 codes. The method is reasonably automated and, we believe, holds potential for inexpensive, more accurate cohort identification.