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

Computer-aid molecular docking technology in cereal mycotoxin analysis: Poster

Computer-aid molecular docking is a simulative process that receptors and ligands recognize each other through energy matching and geometric matching. It is widely used in bioactive compounds simulative screening and preliminary exploring the bioactivity and toxicity of molecular, which plays important guiding role in toxicity and bioactivity study of molecular entities. In our study, we used the computer-aid molecular docking software-discovery studio 3.1 client to test the mechanism of aflatoxins such as aflatoxin B1, B2, M1, M2, G1, G2 and the results of our experiment help to illustrate the pathway of aflatoxin’s toxication. We also used this technology to test the preliminary toxicity of zearalenone (ZEN) and its two degradation products: a- zearalenol (a-ZOL) and ß-zearalenol (ß-ZOL), which indicates that these three products possessed significant estrogenic activity. The order of the estrogenic activity is: a-zearalenol > zearalenone >ß-zearalenol.Computer-aid molecular docking is a simulative process that receptors and ligands recognize each other through energy matching and geometric matching. It is widely used in bioactive compounds simulative screening and preliminary exploring the bioactivity and toxicity of molecular, which plays important guiding role in toxicity and bioactivity study of molecular entities. In our study, we used the computer-aid molecular docking software-discovery studio 3.1 client to test the mechanism of aflatoxins such as aflatoxin B1, B2, M1, M2, G1, G2 and the results of our experiment help to illustrate the pathway of aflatoxin’s toxication. We also used this technology to test the preliminary toxicity of zearalenone (ZEN) and its two degradation products: a- zearalenol (a-ZOL) and ß-zearalenol (ß-ZOL), which indicates that these three products possessed significant estrogenic activity. The order of the estrogenic activity is: a-zearalenol > zearalenone >ß-zearalenol

Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

BACKGROUND: Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first. OBJECTIVE: We aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation-that is, creating lay definitions for these terms. METHODS: Adapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, including distributed word representations learned from large EHR data for ADS. For evaluating ADS, we asked domain experts to annotate 6038 candidate terms as important or nonimportant for EHR comprehension. We then randomly divided these data into the target-domain training data (1000 examples) and the evaluation data (5038 examples). We compared ADS with 2 strong baselines, including standard supervised learning, on the evaluation data. RESULTS: The ADS system using feature space augmentation achieved the best average precision, 0.850, on the evaluation set when using 1000 target-domain training examples. The ADS system using supervised distant supervision achieved the best average precision, 0.819, on the evaluation set when using only 100 target-domain training examples. The 2 ADS systems both performed significantly better than the baseline systems (P \u3c .001 for all measures and all conditions). Using a rich set of learning features contributed to ADS\u27s performance substantially. CONCLUSIONS: ADS can effectively rank terms mined from EHRs. Transfer learning improved ADS\u27s performance even with a small number of target-domain training examples. EHR terms prioritized by ADS were used to expand a lay language resource that supports patient EHR comprehension. The top 10,000 EHR terms ranked by ADS are available upon request

Detecting Hypoglycemia Incidents Reported in Patients\u27 Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety. OBJECTIVE: We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients\u27 secure messages. METHODS: An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical teams as containing patient-reported hypoglycemia incidents or not. A physician independently annotated 100 threads randomly selected from this dataset to determine interannotator agreement. We used this dataset to develop and evaluate HypoDetect. HypoDetect incorporates 3 machine learning algorithms widely used for text classification: linear support vector machines, random forest, and logistic regression. We explored different learning features, including new knowledge-driven features. Because only 114 (3.80%) messages were annotated as positive, we investigated cost-sensitive learning and oversampling methods to mitigate the challenge of imbalanced data. RESULTS: The interannotator agreement was Cohen kappa=.976. Using cross-validation, logistic regression with cost-sensitive learning achieved the best performance (area under the receiver operating characteristic curve=0.954, sensitivity=0.693, specificity 0.974, F1 score=0.590). Cost-sensitive learning and the ensembled synthetic minority oversampling technique improved the sensitivity of the baseline systems substantially (by 0.123 to 0.728 absolute gains). Our results show that a variety of features contributed to the best performance of HypoDetect. CONCLUSIONS: Despite the challenge of data imbalance, HypoDetect achieved promising results for the task of detecting hypoglycemia incidents from secure messages. The system has a great potential to facilitate early detection and treatment of hypoglycemia

Newcastle disease virus-vectored Nipah encephalitis vaccines induce B and T cell responses in mice and long-lasting neutralizing antibodies in pigs

AbstractNipah virus (NiV), a member of the Paramyxoviridae family, causes deadly encephalitis in humans and huge economic losses to the pig industry. Here, we generated recombinant avirulent Newcastle disease virus (NDV) LaSota strains expressing the NiV G and F proteins respectively (designated as rLa-NiVG and rLa-NiVF), and evaluated their immunogenicity in mice and pigs. Both rLa-NiVG and rLa-NiVF displayed growth properties similar to those of LaSota virus in chicken eggs. Co-infection of rLa-NiVG and rLa-NiVF caused marked syncytia formation, while intracerebral co-inoculation of these viruses in mice showed they were safe in at least one mammalian species. Animal immunization studies showed rLa-NiVG and rLa-NiVF induced NiV neutralizing antibody responses in mice and pigs, and F protein-specific CD8+ T cell responses in mice. Most importantly, rLa-NiVG and rLa-NiVF administered alone or together, induced a long-lasting neutralizing antibody response in pigs. Recombinant rLa-NiVG/F thus appear to be promising NiV vaccine candidates for pigs and potentially humans

Protocol for the safety and efficacy of fecal microbiota transplantation liquid in children with autism spectrum disorder: a randomized controlled study

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction, repetitive behavior and language impairment, and its worldwide prevalence has been found to be increasing annually in recent years. Till now, ASD is uncurable as its pathogenesis remains unknown. However, studies on both animals and humans have demonstrated that fecal microbiota transplantation (FMT) may ameliorate the symptoms of ASD, as well as gastrointestinal symptoms. Nonetheless, there is still no agreement regarding the optimal dosage or duration of FMT treatment for individuals with ASD.MethodsThis clinical study is a double-blind, randomized, interventional trial conducted at a single center. The aim is to investigate the safety and efficacy of a pediatric formulation of FMT for ASD. A total of 42 children between the ages of 3–9 with ASD will be randomly assigned in a 2:1 ratio to either an FMT treatment group (n = 28) or a placebo group (n = 14), forming cohort 1. Additionally, 30 healthy children of similar age and gender will be recruited as the control group (cohort 2). Cohort 1 will be assessed using a variety of scales, including the Autism Behavior Checklist, Childhood Autism Rating Scale, Social Responsiveness Scale, Gastrointestinal Symptom Rating Scale, Children’s Sleep Habits Questionnaire, and Psychoeducational Profile (Third Edition). These assessments will evaluate the effectiveness of FMT in reducing core symptoms and comorbidities (such as gastrointestinal symptoms and sleep disturbances) in children with ASD. The study will use metagenomic and metabolomic sequencing to assess changes in the composition and structure of the intestinal flora and its metabolites in blood, urine, and feces following treatment. Furthermore, the study will evaluate the acceptability of the FMT formulation by participants’ legal guardians and investigate differences in the intestinal flora and metabolism in the FMT group before and after treatment compared to 30 healthy children.Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2200058459

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl