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1.
J Chem Phys ; 151(13): 130901, 2019 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-31594353

RESUMO

Nanoparticles present in any biological environment are exposed to extracellular proteins. These proteins adsorb on the surface of the nanoparticle forming a "protein corona." These proteins control the interaction of nanoparticles with cells. The interaction of proteins with the nanoparticle surface is governed by physical chemistry. Understanding this process requires spectroscopy, microscopy, and computational tools that are familiar to physical chemists. This perspective provides an overview of the protein corona along with two future directions: first, the need for new computational approaches, including machine learning, to predict corona formation and second, the extension of protein corona studies to more complex environments ranging from lung fluids to waste water treatment.


Assuntos
Nanopartículas/química , Coroa de Proteína/química , Adsorção , Animais , Química Física/métodos , Humanos , Aprendizado de Máquina
2.
Medicine (Baltimore) ; 98(42): e17596, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31626135

RESUMO

To date, consumer health tools available over the web suffer from serious limitations that lead to low quality health- related information. While health data in our world are abundant, access to it is limited because of liability and privacy constraints.The objective of the present study was to develop and evaluate an algorithm-based tool which aims at providing the public with reliable, data-driven information based and personalized information regarding their symptoms, to help them and their physicians to make better informed decisions, based on statistics describing "people like you", who have experienced similar symptoms.We studied anonymized medical records of Maccabi Health Care. The data were analyzed by employing machine learning methodology and Natural Language Processing (NLP) tools. The NLP tools were developed to extract information from unstructured free-text written by Maccabi's physicians.Using machine learning and NLP on over 670 million notes of patients' visits with Maccabi physicians accrued since 1993, we developed predictors for medical conditions based on patterns of symptoms and personal characteristics.The algorithm was launched for Maccabi insured members on January 7, 2018 and for members of Integrity Family Care program in Alabama on May 1, 2018.The App. invites the user to describe her/ his main symptom or several symptoms, and this prompts a series of questions along the path developed by the algorithm, based on the analysis of 70 million patients' visits to their physicians.Users started dialogues with 225 different types of symptoms, answering on average 22 questions before seeing how people similar to them were diagnosed. Users usually described between 3 and 4 symptoms (mean 3.2) in the health dialogue.In response to the question "conditions verified by your doctor", 82.4% of responders (895/1085) in Maccabi reported that the diagnoses suggested by K's health dialogues were in agreement with their doctor's final diagnosis. In Integrity Health Services, 85.4% of responders (111/130) were in agreement with the physicians' diagnosis.While the program achieves very high approval rates by its users, its primary achievement is the 85% accuracy in identifying the most likely diagnosis, with the gold standard being the final diagnosis made by the personal physician in each individual case. Moreover, the machine learning algorithm continues to update itself with the feedback given by users.


Assuntos
Algoritmos , Apendicite/diagnóstico , Tomada de Decisões , Diagnóstico por Computador/métodos , Aprendizado de Máquina , Complicações na Gravidez/diagnóstico , Adulto , Apendicectomia , Apendicite/cirurgia , Feminino , Humanos , Gravidez , Smartphone
4.
Soins ; 64(838): 33-35, 2019 Sep.
Artigo em Francês | MEDLINE | ID: mdl-31542117

RESUMO

Artificial intelligence (AI) is rapidly being extended across health systems with multiple cases of its use already reported. The most operational technique is machine learning with image recognition in imaging. Solutions derived from this approach, as well as other applications of AI, are presented in two major fields: cancer management and geriatric care.


Assuntos
Inteligência Artificial , Assistência à Saúde/organização & administração , Difusão de Inovações , Humanos , Aprendizado de Máquina
5.
Stud Health Technol Inform ; 267: 101-109, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483261

RESUMO

One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identification methods on German medical texts. Because of remarkable advancements in natural language processing using supervised machine learning methods on limited training data, we evaluated them for the first time on German medical reports using our annotated data set consisting of 113 medical reports from the cardiology domain. We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports. We performed an extensive evaluation for de-identification and multiclass named entity recognition. Using rule based and out of domain machine learning methods as a baseline, the conditional random field improved F2-score from 70 to 93% for de-identification, the neural approach reached 96% in F2-score while keeping balanced precision and recall rates. These results show, that state-of-the-art machine learning methods can play a crucial role in de-identification of German medical reports.


Assuntos
Anonimização de Dados , Aprendizado Profundo , Registros Eletrônicos de Saúde , Aprendizado de Máquina , Processamento de Linguagem Natural
6.
Stud Health Technol Inform ; 267: 181-186, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483271

RESUMO

Gene expression data is commonly available in cancer research and provides a snapshot of the molecular status of a specific tumor tissue. This high-dimensional data can be analyzed for diagnoses, prognoses, and to suggest treatment options. Machine learning based methods are widely used for such analysis. Recently, a set of deep learning techniques was successfully applied in different domains including bioinformatics. One of these prominent techniques are convolutional neural networks (CNN). Currently, CNNs are extending to non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs, and the edges can depict interactions, regulations and signal flow. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Here, we applied graph CNN to gene expression data of breast cancer patients to predict the occurrence of metastatic events. To structure the data we utilized a protein-protein interaction network. We show that the graph CNN exploiting the prior knowledge is able to provide classification improvements for the prediction of metastatic events compared to existing methods.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Humanos , Aprendizado de Máquina , Metástase Neoplásica , Redes Neurais (Computação)
7.
8.
J Chem Phys ; 151(8): 084106, 2019 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-31470712

RESUMO

A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protein aggregation rate, a feedforward fully connected neural network (FCN) with one hidden layer is trained on a dataset composed of 21 different kinds of amyloid proteins and tested on 4 rest proteins. FCN shows a much better performance than traditional algorithms, such as multivariable linear regression and support vector regression, with an average accuracy higher than 90%. Furthermore, by the correlation analysis and the principal component analysis, seven key features, folding energy, HP patterns for helix, sheet and helices cross membrane, pH, ionic strength, and protein concentration, are shown to constitute a minimum feature set for characterizing the amyloid aggregation kinetics.


Assuntos
Amiloide/química , Aprendizado de Máquina , Agregados Proteicos , Cinética , Redes Neurais (Computação)
9.
Sheng Wu Gong Cheng Xue Bao ; 35(9): 1619-1632, 2019 Sep 25.
Artigo em Chinês | MEDLINE | ID: mdl-31559744

RESUMO

With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarker discovery strategies and methods have become one of research hotspots. Feature selection and machine learning methods have good effects on solving the "dimensionality" and "sparsity" problems of proteomics data, which have been widely used in the discovery of protein biomarkers. Here, we systematically review the strategy of protein biomarker discovery and the frequently-used machine learning methods. Also, the review illustrates the prospects and limitations of deep learning in this field. It is aimed at providing a valuable reference for corresponding researchers.


Assuntos
Aprendizado de Máquina , Algoritmos , Biomarcadores , Humanos , Espectrometria de Massas , Proteômica
11.
Psychiatr Danub ; 31(Suppl 3): 261-264, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31488738

RESUMO

BACKGROUND: Schizophrenia can be interpreted as a pathology involving the neocortex whose cognitive dysfunctions represent a central and persistent characteristic of the disease, as well as one of the more important symptoms in relation to the impairment of psychosocial functioning and the resulting disabilities. Given the implication of cognitive functions in everyday life, they can better predict the degree of schizophrenia. The study proposes to use Machine Learning techniques to identify the specific cognitive deficits of schizophrenia that mostly characterize the disorder, as well as to develop a predictive system that can diagnose the presence of schizophrenia based on neurocognitive tests. BACKGROUND: The study employs a dataset of neurocognitive assessments carried out on 201 people (86 schizophrenic patients and 115 healthy patients) recruited by the Neuroscience Group of the University of Bari "A. Moro". A data analysis process has been carried out, with the aim of selecting the most relevant features as well as to prepare data for training a number of "off-the-shelf" machine learning methods (Decision Tree, Random Forest, Logistic Regression, k-Nearest Neighbor, Neural Network, Support Vector Machine), which have been evaluated in terms of classification accuracy according to stratified 20-fold cross-validation. RESULTS: Among all variables, 14 were selected as the most influential for the classification problem. The variables with greater influence are related to working memory, executive functions, attention, verbal fluency, memory. The best algorithms turned out to be Support Vector Machine (SVM) and Neural Network, showing an accuracy of 87.8% and 84.8% on a test set. CONCLUSIONS: Machine Learning provides "cheap" and non-invasive methods that potentially enable early intervention with specific rehabilitation interventions. The results suggest the need to integrate a thorough neuropsychological evaluation into the more general diagnostic evaluation of patients with schizophrenia disorder.


Assuntos
Transtornos Cognitivos/complicações , Transtornos Cognitivos/diagnóstico , Disfunção Cognitiva/complicações , Disfunção Cognitiva/diagnóstico , Aprendizado de Máquina , Esquizofrenia/complicações , Esquizofrenia/diagnóstico , Cognição , Humanos , Testes Neuropsicológicos , Psicologia do Esquizofrênico
12.
J Am Soc Nephrol ; 30(10): 1780-1781, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31488608
13.
Stud Health Technol Inform ; 264: 1228-1232, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438121

RESUMO

Unhealthy behaviors are a socioeconomic burden and lead to the development of chronic diseases. Relapse is a common issue that most individuals deal with as they adopt and sustain a positive healthy lifestyle. Proper identification of behavioral transitions can help design agile, adaptive, and just-in-time interventions. In this paper, we present a methodology that integrates qualitative coding, machine learning, and formal data analysis using stage transition probabilities and linguistics-based text analysis to track shifts in stages of behavior change as embedded in journal entries recorded by users in an online community for tobacco cessation. Results indicate that our semi-automated stage identification method has an accuracy of 90%. Further analysis revealed stage-specific language features and transition probabilities. Implications for targeted social interventions are discussed.


Assuntos
Mídias Sociais , Humanos , Linguística , Aprendizado de Máquina
14.
Stud Health Technol Inform ; 264: 1417-1418, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438159

RESUMO

Automated wound detection has become a common issue in health care. A broad variety of image processing algorithms already exist, but they are very power consuming on mobile devices. Meanwhile the use of machine learning algorithms is on the rise and new frameworks have been developed to use these techniques with improved on-device-performance such as Apple Core Machine Learning Interface. In this paper, we evaluate the performance of libSVM for wound detection in practice.


Assuntos
Dermatopatias , Máquina de Vetores de Suporte , Algoritmos , Humanos , Processamento de Imagem Assistida por Computador , Aprendizado de Máquina
15.
Stud Health Technol Inform ; 264: 1453, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438177

RESUMO

We completed a pilot study to guide the development of the VA Research Precision Oncology Data Commons infrastructure as a collaboration platform with the greater research community. Our results using a small subset of patients from the VA's Precision Oncology Program demonstrate the feasibility of our data sharing platform to build predictive models for lung cancer survival using machine learning, as well as highlight the potential of target genome sequencing data.


Assuntos
Neoplasias Pulmonares , Veteranos , Humanos , Aprendizado de Máquina , Projetos Piloto , Medicina de Precisão , Estados Unidos , United States Department of Veterans Affairs
16.
Stud Health Technol Inform ; 264: 123-127, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437898

RESUMO

In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Urinary Tract Infection disease identification. Additionally, we intrinsically evaluated our main model using an adapted version of Bio-SimLex for the Portuguese language. Our empirical results showed that the larger, coarse-grained model achieved a slightly better outcome when compared with the small, fine-grained model in the proposed task. Moreover, we obtained satisfactory results with Bio-SimLex intrinsic evaluation.


Assuntos
Aprendizado de Máquina , Processamento de Linguagem Natural , Linguagem , Narração , Portugal
17.
Stud Health Technol Inform ; 264: 133-137, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437900

RESUMO

Laboratory data collected in the electronic health record as part of routine care can be used in secondary research. For example, the US Department of Veterans Affairs maintains a data warehouse covering over 20 million individuals and 6.6 billion lab tests. However, data aggregation in such a data warehouse can be difficult. In order to retrieve all or nearly all of one type of lab result with a high degree of precision, we perform clinical concept adjudication, which is the process of an expert determining which database records correspond to a target clinical concept. In this work, we develop an interactive machine learning tool to "extend the reach" of expert laboratory test adjudicators. Our tool provides access to automatic laboratory classification in a user-facing front end that covers all steps in an adjudication workflow, in order to lower barriers to collaboration, increase transparency of adjudication, and to promote efficiencies and data reuse.


Assuntos
Aprendizado de Máquina , Treinamento por Simulação , Bases de Dados Factuais , Registros Eletrônicos de Saúde , Humanos , Fluxo de Trabalho
18.
Stud Health Technol Inform ; 264: 143-147, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437902

RESUMO

Severe maternal morbidity (SMM) is broadly defined as significant complications in pregnancy that have an adverse effect on women's health. Identifying women who experience SMM and reviewing their obstetric care can assist healthcare organizations in recognizing risk factors and best practices for management. Various definitions of SMM have been posited, but there is no consensus. Existing definitions are further limited in that they 1) are often rooted in existing clinical knowledge (which is problematic as many risk factors remain unknown), leading to poor positive predictive performance (PPV), and 2) have limited scalability as they often require substantial chart review. Thus, in this paper, a machine learning framework was introduced to automatically identify SMM and relevant risk factors from electronic health records (EHRs). We evaluated this framework with EHR data from 45,858 deliveries at a large academic medical center. The framework outperformed a state-of-the-art model from the U.S. Centers for Disease Control and Prevention (AUC of 0.94 vs. 0.80). Specially, it improved upon PPV by 59% (CDC: 0.22 vs. our model: 0.35). In the process, we revealed several novel SMM indicators, including disorders of fluid or electrolytes, systemic inflammatory response syndrome, and acidosis.


Assuntos
Parto Obstétrico , Registros Eletrônicos de Saúde , Assistência à Saúde , Feminino , Humanos , Aprendizado de Máquina , Gravidez , Fatores de Risco
19.
Stud Health Technol Inform ; 264: 163-167, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437906

RESUMO

As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering. However, given the wide range of topics of Twitter users, tweets related to drug abuse are rare in most of the datasets. This imbalanced data remains a major issue in building effective tweet classifiers, and is especially obvious for studies that include abuse-related slang terms. In this study, we approach this problem by designing an ensemble deep learning model that leverages both word-level and character-level features to classify abuse-related tweets. Experiments are reported on a Twitter dataset, where we can configure the percentages of the two classes (abuse vs. non abuse) to simulate the data imbalance with different amplitudes. Results show that our ensemble deep learning models exhibit better performance than ensembles of traditional machine learning models, especially on heavily imbalanced datasets.


Assuntos
Mídias Sociais , Coleta de Dados , Aprendizado Profundo , Aprendizado de Máquina , Detecção do Abuso de Substâncias
20.
Stud Health Technol Inform ; 264: 173-177, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437908

RESUMO

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased availability for patients who urgently need it. Common risk assessment tool, like the ASA score, lack objectivity and do account only for some influencing parameters. The aim of our study was (1) to develop a reliable machine learning model predicting ICU admission risk after elective surgery, and (2) to implement it in a clinical workflow. We used electronic medical records from more than 61,000 patients for modelling. A random forest model outperformed other methods with an area under the curve of 0.91 in the retrospective test set. In the prospective implementation, the model achieved a sensitivity of 73.3% and a specificity of 80.8%. Further research is essential to determine physicians' attitudes to machine learning models and assess the long term improvement of ICU management.


Assuntos
Unidades de Terapia Intensiva , Aprendizado de Máquina , Hospitalização , Humanos , Estudos Prospectivos
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