Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records

Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records. First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients. The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric

Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study

BACKGROUND: Electronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients\u27 comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations. OBJECTIVE: The objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople\u27s perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas\u27 appropriateness and generalizability on predicting difficulty levels of highly complex technical documents. METHODS: We collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople\u27s perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text. RESULTS: The distributions of both readability formulas\u27 scores (P \u3c .001) and laypeople\u27s perceptions (P=.002) on the 2 genres were different. Correlations of readability predictions and laypeople\u27s perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (P \u3c .001). Word usage in the 2 related genres still differed significantly (P \u3c .001). CONCLUSIONS: Our findings suggested that the readability formulas\u27 predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were highly correlated with each other but did not show adequate correlation with readers\u27 perceived difficulty. Therefore, they were not appropriate to assess the readability of EHR notes

Assessing the Readability of Medical Documents: A Ranking Approach

BACKGROUND: The use of electronic health record (EHR) systems with patient engagement capabilities, including viewing, downloading, and transmitting health information, has recently grown tremendously. However, using these resources to engage patients in managing their own health remains challenging due to the complex and technical nature of the EHR narratives. OBJECTIVE: Our objective was to develop a machine learning-based system to assess readability levels of complex documents such as EHR notes. METHODS: We collected difficulty ratings of EHR notes and Wikipedia articles using crowdsourcing from 90 readers. We built a supervised model to assess readability based on relative orders of text difficulty using both surface text features and word embeddings. We evaluated system performance using the Kendall coefficient of concordance against human ratings. RESULTS: Our system achieved significantly higher concordance (.734) with human annotators than did a baseline using the Flesch-Kincaid Grade Level, a widely adopted readability formula (.531). The improvement was also consistent across different disease topics. This method\u27s concordance with an individual human user\u27s ratings was also higher than the concordance between different human annotators (.658). CONCLUSIONS: We explored methods to automatically assess the readability levels of clinical narratives. Our ranking-based system using simple textual features and easy-to-learn word embeddings outperformed a widely used readability formula. Our ranking-based method can predict relative difficulties of medical documents. It is not constrained to a predefined set of readability levels, a common design in many machine learning-based systems. Furthermore, the feature set does not rely on complex processing of the documents. One potential application of our readability ranking is personalization, allowing patients to better accommodate their own background knowledge

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients\u27 notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. OBJECTIVE: We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. METHODS: First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians\u27 agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. RESULTS: Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen\u27s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P \u3c .001). Rich learning features contributed to FOCUS\u27s performance substantially. CONCLUSIONS: FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care

Arctigenin-induced reversal of drug resistance in cisplastin-resistant cell line A549/DDP, and the mechanism involved

Purpose: To investigate the drug resistance reversal effect of arctigenin (ARG) on cisplatin-insensitive A549/DDP cancer cells, and to elucidate the underlying mechanism(s). Methods: Four groups of cells: control, DDP, ARG and ADP were used. The degrees of inhibition of proliferation, drug resistance and apoptotic changes were measured using MTT assay, CCK-8 assay and flow cytometry, respectively. Expressions of PTEN and STAT3 proteins were determined by Western blotting. Results: At ARG concentration of 5 μmol/L, A549/DDP cells were significantly inhibited (p < 0.05). The combination therapy was more effective in reversing A549/DDP cells resistance than the single therapy. The expression level of PTEN protein increased with increase in ARG concentration, while STAT3 protein expression decreased with increase in ARG concentration. ADP group up-regulated PTEN but decreased STAT3 expression levels. Conclusion: ARG regulates drug resistance in A549/DDP cells, possibly via a mechanism involving reduction of A549/DDP cell sensitivity to DDP, thereby regulating the stress pathways associated with PTEN and STAT3. The combination of ARG and DDP effectively reduces A549/DDP cells resistance

Inhibition of AMPA receptor trafficking at hippocampal synapses by β-amyloid oligomers: the mitochondrial contribution

<p>Abstract</p> <p>Background</p> <p>Synaptic defects represent a major mechanism underlying altered brain functions of patients suffering Alzheimer's disease (AD) <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr><abbr bid="B3">3</abbr></abbrgrp>. An increasing body of work indicates that the oligomeric forms of β-amyloid (Aβ) molecules exert profound inhibition on synaptic functions and can cause a significant loss of neurotransmitter receptors from the postsynaptic surface, but the underlying mechanisms remain poorly understood. In this study, we investigated a potential contribution of mitochondria to Aβ inhibition of AMPA receptor (AMPAR) trafficking.</p> <p>Results</p> <p>We found that a brief exposure of hippocampal neurons to Aβ oligomers not only led to marked removal of AMPARs from postsynaptic surface but also impaired rapid AMPAR insertion during chemically-induced synaptic potentiation. We also found that Aβ oligomers exerted acute impairment of fast mitochondrial transport, as well as mitochondrial translocation into dendritic spines in response to repetitive membrane depolarization. Quantitative analyses at the single spine level showed a positive correlation between spine-mitochondria association and the surface accumulation of AMPARs. In particular, we found that spines associated with mitochondria tended to be more resistant to Aβ inhibition on AMPAR trafficking. Finally, we showed that inhibition of GSK3β alleviated Aβ impairment of mitochondrial transport, and effectively abolished Aβ-induced AMPAR loss and inhibition of AMPAR insertion at spines during cLTP.</p> <p>Conclusions</p> <p>Our findings indicate that mitochondrial association with dendritic spines may play an important role in supporting AMPAR presence on or trafficking to the postsynaptic membrane. Aβ disruption of mitochondrial trafficking could contribute to AMPAR removal and trafficking defects leading to synaptic inhibition.</p

Pathophysiology and transcriptomic analysis of Picea koraiensis inoculated by bark beetle-vectored fungus Ophiostoma bicolor

Ophiostomatoid fungi exhibit a complex relationship with bark beetles; exhausting of host tree defenses is traditionally regarded as one of the key benefits provided to beetle vectors. Ophiostoma bicolor is one of the dominant species of the mycobiota associated with Ips genus bark beetles which infect the spruce trees across the Eurasian continent. Host spruce trees resist fungal invasion through structural and inducible defenses, but the underlying mechanisms at the molecular level, particularly with respect to the interaction between bark beetle-associated fungi and host trees, remain unclear. The aim of this study was to observe the pathological physiology and molecular changes in Picea koraiensis seedlings after artificial inoculation with O. bicolor strains (TS, BH, QH, MX, and LWQ). This study showed that O. bicolor was a weakly virulent pathogen of spruce, and that the virulent of the five O. bicolor strains showed differentiation. All O. bicolor strains could induce monoterpenoid release. A positive correlation between fungal virulence and release of monoterpenoids was observed. Furthermore, the release rate of monoterpenoids peaked at 4 days post-inoculation (dpi) and then decreased from 4 to 90 dpi. Transcriptomic analysis at 4 dpi showed that many plant-pathogen interaction processes and mitogen-activated protein kinase (MAPK) metabolic processes were activated. The expression of monoterpenoid precursor synthesis genes and diterpenoid synthesis genes was upregulated, indicating that gene expression regulated the release rate of monoterpenoids at 4 dpi. The enriched pathways may reveal the immune response mechanism of spruce to ophiostomatoid fungi. The dominant O. bicolor possibly induces the host defense rather than defense depletion, which is likely the pattern conducted by the pioneers of beetle-associated mycobiota, such as Endoconidiophora spp.. Overall, these results facilitate a better understanding of the interaction mechanism between the dominant association of beetles and the host at the molecular level

Observations of aerosol optical properties at a coastal site in Hong Kong, South China

Temporal variations in aerosol optical properties were investigated at a coastal station in Hong Kong based on the field observation from February 2012 to February 2015. At 550 nm, the average light-scattering (151 +/- 100Mm(-1) / and absorption coefficients (8.3 +/- 6.1Mm(-1) / were lower than most of other rural sites in eastern China, while the single-scattering albedo (SSA = 0.93 +/- 0.05) was relatively higher compared with other rural sites in the Pearl River Delta (PRD) region. Correlation analysis confirmed that the darkest aerosols were smaller in particle size and showed strong scattering wavelength dependencies, indicating possible sources from fresh emissions close to the measurement site. Particles with D-p of 200-800 nm were less in number, yet contributed the most to the light-scattering coefficients among submicron particles. In summer, both Delta BC / Delta CO and SO2 / BC peaked, indicating the impact of nearby combustion sources on this site. Multi-year backward Lagrangian particle dispersion modeling (LPDM) and potential source contribution (PSC) analysis revealed that these particles were mainly from the air masses that moved southward over Shenzhen and urban Hong Kong and the polluted marine air containing ship exhausts. These fresh emission sources led to low SSA during summer months. For winter and autumn months, contrarily, Delta BC / Delta CO and SO2 / BC were relatively low, showing that the site was more under influence of well-mixed air masses from long-range transport including from South China, East China coastal regions, and aged aerosol transported over the Pacific Ocean and Taiwan, causing stronger abilities of light extinction and larger variability of aerosol optical properties. Our results showed that ship emissions in the vicinity of Hong Kong could have visible impact on the light-scattering and absorption abilities as well as SSA at Hok Tsui.Peer reviewe

Molecular mechanisms of ferroptosis and its roles in leukemia

Cell death is a complex process required to maintain homeostasis and occurs when cells are damage or reach end of life. As research progresses, it is apparent that necrosis and apoptosis do not fully explain the whole phenomenon of cell death. Therefore, new death modalities such as autophagic cell death, and ferroptosis have been proposed. In recent years, ferroptosis, a new type of non-apoptotic cell death characterized by iron-dependent lipid peroxidation and reactive oxygen species (ROS) accumulation, has been receiving increasing attention. Ferroptosis can be involved in the pathological processes of many disorders, such as ischemia-reperfusion injury, nervous system diseases, and blood diseases. However, the specific mechanisms by which ferroptosis participates in the occurrence and development of leukemia still need to be more fully and deeply studied. In this review, we present the research progress on the mechanism of ferroptosis and its role in leukemia, to provide new theoretical basis and strategies for the diagnosis and treatment of clinical hematological diseases

Using natural language processing to classify social work interventions

Objectives: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms. Study design: Secondary data analysis of a longitudinal cohort. Methods: We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme. Results: Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92). Conclusions: NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs