Ritterwilladsen8775
This method we introduce focuses on filling the blanks in the selection of preprocessing paths, and the result proves its effectiveness and accuracy. Our research provides useful indicators for the evaluation of RNA-Seq data.
This method we introduce focuses on filling the blanks in the selection of preprocessing paths, and the result proves its effectiveness and accuracy. Our research provides useful indicators for the evaluation of RNA-Seq data.
Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs. In order to extract such knowledge from plain text and transform them into structural form, the relation extraction problem becomes an important issue. Datasets play a critical role in the development of relation extraction methods. However, existing relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their labor-intensive and time-consuming nature.
We construct BioRel, a large-scale dataset for biomedical relation extraction problem, by using Unified Medical Language System as knowledge base and Medline as corpus. We first identify mentions of entities in sentences of Medline and link them to Unified Medical Language System with Metamap. Then, we assign each sentence a relation label by using distant supervision. Finally, we adapt the state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on the BioRel dataset.
Based on the extensive experimental results, we have shown that BioRel is a suitable large-scale datasets for biomedical relation extraction, which provides both reasonable baseline performance and many remaining challenges for both deep learning and statistical methods.
Based on the extensive experimental results, we have shown that BioRel is a suitable large-scale datasets for biomedical relation extraction, which provides both reasonable baseline performance and many remaining challenges for both deep learning and statistical methods.
Identification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate.
In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity.
The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https//github.com/yongzhuang/DNMFilter_Indel .
The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https//github.com/yongzhuang/DNMFilter_Indel .
Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients.
We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays.
Hyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7-a clinically-actionable rate.
Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes.
Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes.
With the rapid development of medical treatment, many patients not only consider the survival time, but also care about the quality of life. Changes in physical, psychological and social functions after and during treatment have caused a lot of troubles to patients and their families. Based on the bio-psycho-social medical model theory, mental health plays an important role in treatment. Therefore, it is necessary for medical staff to know the diseases which have high potential to cause psychological trauma and social avoidance (PTSA).
Firstly, we obtained diseases which can cause PTSA from literatures. Then, we calculated the similarities of related-diseases to build a disease network. The similarities between diseases were based on their known related genes. Then, we obtained these diseases-related proteins from UniProt. These proteins were extracted as the features of diseases. Therefore, in the disease network, each node denotes a disease and contains the information of its related proteins, and the edges of the network are the similarities of diseases. Then, graph convolutional network (GCN) was used to encode the disease network. In this way, each disease's own feature and its relationship with other diseases were extracted. Finally, Xgboost was used to identify PTSA diseases.
We developed a novel method 'GCN-Xgboost' and compared it with some traditional methods. Using leave-one-out cross-validation, the AUC and AUPR were higher than some existing methods. In addition, case studies have been done to verify our results. We also discussed the trajectory of social avoidance and distress during acute survival of breast cancer patients.
We developed a novel method 'GCN-Xgboost' and compared it with some traditional methods. Using leave-one-out cross-validation, the AUC and AUPR were higher than some existing methods. In addition, case studies have been done to verify our results. We also discussed the trajectory of social avoidance and distress during acute survival of breast cancer patients.
It is significant to model clinical activities for process mining, which assists in improving medical service quality. However, current process mining studies in healthcare pay more attention to the control flow of events, while the data properties and the time perspective are generally ignored. Moreover, classifying event attributes from the view of computers usually are difficult for medical experts. There are also problems of model sharing and reusing after it is generated.
In this paper, we presented a constraint-based method using multi-perspective declarative process mining, supporting healthcare personnel to model clinical processes by themselves. Inspired by openEHR, we classified event attributes into seven types, and each relationship between these types is represented in a Constrained Relationship Matrix. Finally, a conformance checking algorithm is designed.
The method was verified in a retrospective observational case study, which consists of Electronic Medical Record (EMR) of 358 patients from a large general hospital in China. We take the ischemic stroke treatment process as an example to check compliance with clinical guidelines. Conformance checking results are analyzed and confirmed by medical experts.
This representation approach was applicable with the characteristic of easily understandable and expandable for modeling clinical activities, supporting to share the models created across different medical facilities.
This representation approach was applicable with the characteristic of easily understandable and expandable for modeling clinical activities, supporting to share the models created across different medical facilities.The fourteenth annual ASCAT conference was held 21-23 October 2019. The theme of the conference was 'Sickle Cell and Thalassaemia disorders new treatment horizon; while ensuring patient safety and delivering excellence in routine patient care.' Over the three-day conference, topics on current and novel models of care, advances in bone marrow transplant and gene therapy, as well as the psychosocial aspects of mind, body and health related quality of life were discussed. In addition, blood transfusion, apheresis, iron chelation therapy and acute haemolytic complications were presented. Quality standards in the diagnosis and treatment of sickle cell and thalassaemia were reviewed. Experts from Europe, the United Kingdom, the Middle East, the United States and Africa reported up-to-date scientific data, guides to comprehensive care, and current research into developing cures and advancing current therapy were described. In addition, oral and poster presentations on novel research from all over the world were shown during the conference.
Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. SU6656 solubility dmso Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs.
Firstly, we extracted the feature of ACPs in two aspects sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method 'DRACP' and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets.
We developed a novel method named 'DRACP' and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.
We developed a novel method named 'DRACP' and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.
Mobile health innovations are well adapted for ambulatory coronavirus disease 2019 (COVID-19) patients who risk clinical deterioration at home during the second week of illness.
A short message service (SMS) communication program was implemented by French physicians to monitor COVID-19 patients after discharge from outpatient or emergency care. The aim of the SMS tracking is to advise patients about their need for medical reassessment if reporting worsening of COVID-19 symptoms. A follow-up via SMS to all confirmed positive patients in the Nîmes area (France) was established. Every morning, patients received four follow-up questions. Daily responses were converted to green, orange or red trees, analysed in real time by physicians. "Red" patients were called immediately to check their condition and organise transfer to hospital if needed. "Orange" patients were called within two hoursto verify whether the specific instructions following the SMS had been followed.
From March 21 to June 30, 2020, 1007 patients agreed to sign up to the SMS tracking, 62% were women and the mean age was41.
