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Automated Spelling Correction for Clinical Text Mining in Russian.

https://arctichealth.org/en/permalink/ahliterature305544
Source
Stud Health Technol Inform. 2020 Jun 16; 270:43-47
Publication Type
Journal Article
Date
Jun-16-2020
Author
Ksenia Balabaeva
Anastasia Funkner
Sergey Kovalchuk
Author Affiliation
ITMO University, Saint Petersburg, Russia.
Source
Stud Health Technol Inform. 2020 Jun 16; 270:43-47
Date
Jun-16-2020
Language
English
Publication Type
Journal Article
Keywords
Algorithms
Data Mining
Electronic Health Records
Language
Machine Learning
Natural Language Processing
Russia
Abstract
The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our overall precision is 0.86, lexical precision - 0.975 and error precision is 0.74. We develop spell checker as a part of medical text mining tool regarding the problems of misspelling, negation, experiencer and temporality detection.
PubMed ID
32570343 View in PubMed
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A class of joint models for multivariate longitudinal measurements and a binary event.

https://arctichealth.org/en/permalink/ahliterature286216
Source
Biometrics. 2016 Sep;72(3):917-25
Publication Type
Article
Date
Sep-2016
Author
Sungduk Kim
Paul S Albert
Source
Biometrics. 2016 Sep;72(3):917-25
Date
Sep-2016
Language
English
Publication Type
Article
Keywords
Bayes Theorem
Female
Gestational Age
Humans
Infant, Low Birth Weight
Infant, Newborn
Longitudinal Studies
Machine Learning - statistics & numerical data
Markov Chains
Models, Statistical
Monte Carlo Method
Norway
Pregnancy
Pregnancy outcome
Sweden
Ultrasonography
Abstract
Predicting binary events such as newborns with large birthweight is important for obstetricians in their attempt to reduce both maternal and fetal morbidity and mortality. Such predictions have been a challenge in obstetric practice, where longitudinal ultrasound measurements taken at multiple gestational times during pregnancy may be useful for predicting various poor pregnancy outcomes. The focus of this article is on developing a flexible class of joint models for the multivariate longitudinal ultrasound measurements that can be used for predicting a binary event at birth. A skewed multivariate random effects model is proposed for the ultrasound measurements, and the skewed generalized t-link is assumed for the link function relating the binary event and the underlying longitudinal processes. We consider a shared random effect to link the two processes together. Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. Several variations of the proposed model are considered and compared via the deviance information criterion, the logarithm of pseudomarginal likelihood, and with a training-test set prediction paradigm. The proposed methodology is illustrated with data from the NICHD Successive Small-for-Gestational-Age Births study, a large prospective fetal growth cohort conducted in Norway and Sweden.
Notes
Cites: Paediatr Perinat Epidemiol. 2012 May;26(3):199-20722471679
Cites: Biometrics. 2009 Dec;65(4):1233-4219432784
Cites: Semin Perinatol. 2004 Feb;28(1):23-3215058899
Cites: Stat Med. 2012 Jan 30;31(2):143-5422081439
Cites: Biostatistics. 2005 Apr;6(2):227-3915772102
Cites: Biometrics. 2005 Sep;61(3):767-8016135028
PubMed ID
26753988 View in PubMed
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Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

https://arctichealth.org/en/permalink/ahliterature307364
Source
Int J Med Inform. 2020 04; 136:104068
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
04-2020
Author
Rasheed Omobolaji Alabi
Mohammed Elmusrati
Iris Sawazaki-Calone
Luiz Paulo Kowalski
Caj Haglund
Ricardo D Coletta
Antti A Mäkitie
Tuula Salo
Alhadi Almangush
Ilmo Leivo
Author Affiliation
Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland. Electronic address: rasheed.alabi@student.uwasa.fi.
Source
Int J Med Inform. 2020 04; 136:104068
Date
04-2020
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Bayes Theorem
Brazil - epidemiology
Chemoradiotherapy
Child
Cohort Studies
Combined Modality Therapy
Decision Trees
Female
Finland - epidemiology
Humans
Incidence
Male
Middle Aged
Mouth Neoplasms - pathology - therapy
Neoplasm Recurrence, Local - classification - diagnosis - epidemiology
Prognosis
Supervised Machine Learning
Support Vector Machine
Tongue Neoplasms - pathology - therapy
Young Adult
Abstract
The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary centers.
We compared the performance of four machine learning (ML) algorithms for predicting the risk of locoregional recurrences in patients with OTSCC. These algorithms were Support Vector Machine (SVM), Naive Bayes (NB), Boosted Decision Tree (BDT), and Decision Forest (DF).
The study cohort comprised 311 cases from the five University Hospitals in Finland and A.C. Camargo Cancer Center, São Paulo, Brazil. For comparison of the algorithms, we used the harmonic mean of precision and recall called F1 score, specificity, and accuracy values. These algorithms and their corresponding permutation feature importance (PFI) with the input parameters were externally tested on 59 new cases. Furthermore, we compared the performance of the algorithm that showed the highest prediction accuracy with the prognostic significance of depth of invasion (DOI).
The results showed that the average specificity of all the algorithms was 71% . The SVM showed an accuracy of 68% and F1 score of 0.63, NB an accuracy of 70% and F1 score of 0.64, BDT an accuracy of 81% and F1 score of 0.78, and DF an accuracy of 78% and F1 score of 0.70. Additionally, these algorithms outperformed the DOI-based approach, which gave an accuracy of 63%. With PFI-analysis, there was no significant difference in the overall accuracies of three of the algorithms; PFI-BDT accuracy increased to 83.1%, PFI-DF increased to 80%, PFI-SVM decreased to 64.4%, while PFI-NB accuracy increased significantly to 81.4%.
Our findings show that the best classification accuracy was achieved with the boosted decision tree algorithm. Additionally, these algorithms outperformed the DOI-based approach. Furthermore, with few parameters identified in the PFI analysis, ML technique still showed the ability to predict locoregional recurrence. The application of boosted decision tree machine learning algorithm can stratify OTSCC patients and thus aid in their individual treatment planning.
PubMed ID
31923822 View in PubMed
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De-Identifying Swedish EHR Text Using Public Resources in the General Domain.

https://arctichealth.org/en/permalink/ahliterature305543
Source
Stud Health Technol Inform. 2020 Jun 16; 270:148-152
Publication Type
Journal Article
Date
Jun-16-2020
Author
Taridzo Chomutare
Kassaye Yitbarek Yigzaw
Andrius Budrionis
Alexandra Makhlysheva
Fred Godtliebsen
Hercules Dalianis
Author Affiliation
Norwegian Centre for E-health Research, Tromsø, Norway.
Source
Stud Health Technol Inform. 2020 Jun 16; 270:148-152
Date
Jun-16-2020
Language
English
Publication Type
Journal Article
Keywords
Electronic Health Records
Language
Machine Learning
Natural Language Processing
Sweden
Abstract
Sensitive data is normally required to develop rule-based or train machine learning-based models for de-identifying electronic health record (EHR) clinical notes; and this presents important problems for patient privacy. In this study, we add non-sensitive public datasets to EHR training data; (i) scientific medical text and (ii) Wikipedia word vectors. The data, all in Swedish, is used to train a deep learning model using recurrent neural networks. Tests on pseudonymized Swedish EHR clinical notes showed improved precision and recall from 55.62% and 80.02% with the base EHR embedding layer, to 85.01% and 87.15% when Wikipedia word vectors are added. These results suggest that non-sensitive text from the general domain can be used to train robust models for de-identifying Swedish clinical text; and this could be useful in cases where the data is both sensitive and in low-resource languages.
PubMed ID
32570364 View in PubMed
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Early dumping syndrome is not a complication but a desirable feature of Roux-en-Y gastric bypass surgery.

https://arctichealth.org/en/permalink/ahliterature280408
Source
Clin Obes. 2016 Oct;6(5):332-40
Publication Type
Article
Date
Oct-2016
Author
A. Laurenius
M. Engström
Source
Clin Obes. 2016 Oct;6(5):332-40
Date
Oct-2016
Language
English
Publication Type
Article
Keywords
Adaptation, Psychological
Adult
Body mass index
Combined Modality Therapy
Diet, Reducing
Dumping Syndrome - diet therapy - epidemiology - etiology - physiopathology
Female
Gastric Bypass - adverse effects
Humans
Hyperphagia - physiopathology - prevention & control
Male
Middle Aged
Obesity, Morbid - diet therapy - etiology - prevention & control - surgery
Patient Education as Topic
Postoperative Complications - diet therapy - epidemiology - physiopathology
Risk factors
Secondary Prevention
Severity of Illness Index
Supervised Machine Learning
Sweden - epidemiology
Terminology as Topic
Weight Loss
Abstract
Early dumping syndrome after gastric bypass surgery due to rapid delivery of hyperosmolar nutrients into the bowel causing intense symptoms is often described as a complication. Twelve patients, mean age 47 years, were interviewed approximately 9 years post-operation. The interviews were audiotaped and transcribed verbatim, followed by an inductive content analysis to reveal patients' experience of the dumping syndrome. The core category 'Dumping syndrome is a positive consequence of Roux-en-Y gastric bypass surgery and a tool to control food intake' was identified based on the following four sub-categories: (i) 'The multidimensional emergence and effects of dumping syndrome', (ii) 'Dumping syndrome as something positive although unpleasant', (iii) 'Developing coping mechanisms and ingenious strategies' and (iv) 'My own fault if I expose myself to dumping syndrome'. From the patients' perspective, dumping syndrome gives control over food intake; although the symptoms were unpleasant, patients considered dumping syndrome as a positive protection against over-consumption. Hence, healthcare professionals should not present dumping syndrome as a complication but rather as an aid to control eating behaviour and excessive food intake.
PubMed ID
27487971 View in PubMed
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Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers.

https://arctichealth.org/en/permalink/ahliterature270793
Source
J Affect Disord. 2015 Sep 15;184:170-5
Publication Type
Article
Date
Sep-15-2015
Author
Karen-Inge Karstoft
Alexander Statnikov
Søren B Andersen
Trine Madsen
Isaac R Galatzer-Levy
Source
J Affect Disord. 2015 Sep 15;184:170-5
Date
Sep-15-2015
Language
English
Publication Type
Article
Keywords
Adult
Afghan Campaign 2001-
Algorithms
Cohort Studies
Denmark
Emotions
Female
Humans
Longitudinal Studies
Machine Learning
Male
Military Personnel - psychology
Predictive value of tests
Prospective Studies
Resilience, Psychological
Risk assessment
Stress Disorders, Post-Traumatic - psychology
Support Vector Machine
Abstract
Pre-deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post-deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post-deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods.
ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses.
Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC = 0.84 (95% CI = 0.81-0.87), post-deployment: AUC = 0.88 (95% CI = 0.85-0.91)).
This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures.
Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war.
PubMed ID
26093830 View in PubMed
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Echocardiography Population Study in Russian Federation for 4P Medicine Using Machine Learning.

https://arctichealth.org/en/permalink/ahliterature302963
Source
Stud Health Technol Inform. 2019; 261:137-142
Publication Type
Journal Article
Date
2019
Author
Oleg Metsker
Alexey Yakovlev
Aleksandr Ilin
Sergey Kovalchuk
Author Affiliation
ITMO University, Saint Petersburg, Russia.
Source
Stud Health Technol Inform. 2019; 261:137-142
Date
2019
Language
English
Publication Type
Journal Article
Keywords
Echocardiography
Humans
Machine Learning
Medicine
Retrospective Studies
Russia
Abstract
This article describes the study results of echocardiographic (ECHO) test data for 4P medicine applied to cardiovascular patients. Data from more than 145,000 echocardiographic tests were analyzed. One of the objectives of the study is the possibility to identify patterns and relationships in patient characteristics for more accurate appointment procedures based on the history of the disease and the individual characteristics of the patient. This is achieved by using classifications models based on machine learning methods. Early detection of disease risks and "accurate" appointment of diagnostic procedures makes a significant contribution to value-based medicine. Moreover, it was also possible to identify the classes and characteristics of patients for whom repeated diagnostic procedures are well founded. Calculation of personal risks from empirical retrospective data helps to detect the disease in early stages. Identifying patients with high risk of disease complications allow physicians to make right decisions about timely treatment, which can significantly improve the quality of treatment, and help to avoid diseases complications, optimize costs and improve the quality of medical care.
PubMed ID
31156105 View in PubMed
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Functionality of Triggers for Epilepsy Patients Assessed by Text and Data Mining of Medical and Nursing Records.

https://arctichealth.org/en/permalink/ahliterature281933
Source
Stud Health Technol Inform. 2016;225:128-32
Publication Type
Article
Date
2016
Author
Eija Kivekäs
Ulla-Mari Kinnunen
Pekka Paananen
Reetta Kälviäinen
Kaisa Haatainen
Kaija Saranto
Source
Stud Health Technol Inform. 2016;225:128-32
Date
2016
Language
English
Publication Type
Article
Keywords
Causality
Data Mining - methods
Decision Support Systems, Clinical - organization & administration
Electronic Health Records - statistics & numerical data
Epilepsy - diagnosis - epidemiology - prevention & control
Finland - epidemiology
Humans
Machine Learning
Medical Errors - prevention & control - statistics & numerical data
Natural Language Processing
Nursing Records - statistics & numerical data
Patient Safety
Prognosis
Reproducibility of Results
Risk Assessment - methods
Sensitivity and specificity
Terminology as Topic
Abstract
A trigger is a powerful tool for identifying adverse events to measure the level of any kind of harm caused in patient care. Studies with epilepsy patients have illustrated that using triggers as a methodology with data mining may increase patient well-being. The purpose of this study is to test the functionality and validity of the previously defined triggers to describe the status of epilepsy patient's well-being. In both medical and nursing data, the triggers described patients' well-being comprehensively. The narratives showed that there was overlapping in triggers. The preliminary results of triggers encourage us to develop some reminders to the documentation of epilepsy patient well-being. These provide healthcare professionals with further and more detailed information when necessary.
PubMed ID
27332176 View in PubMed
Less detail

Hidden Markov model using Dirichlet process for de-identification.

https://arctichealth.org/en/permalink/ahliterature276372
Source
J Biomed Inform. 2015 Dec;58 Suppl:S60-6
Publication Type
Article
Date
Dec-2015
Author
Tao Chen
Richard M Cullen
Marshall Godwin
Source
J Biomed Inform. 2015 Dec;58 Suppl:S60-6
Date
Dec-2015
Language
English
Publication Type
Article
Keywords
Cohort Studies
Computer Security
Computer simulation
Confidentiality
Data Mining - methods
Electronic Health Records - organization & administration
Machine Learning
Markov Chains
Models, Statistical
Narration
Natural Language Processing
Newfoundland and Labrador
Pattern Recognition, Automated - methods
Vocabulary, Controlled
Abstract
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance.
PubMed ID
26407642 View in PubMed
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Longitudinal Changes in Early Nasal Microbiota and the Risk of Childhood Asthma.

https://arctichealth.org/en/permalink/ahliterature304738
Source
Pediatrics. 2020 10; 146(4):
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2020
Author
Laura Toivonen
Sinikka Karppinen
Linnea Schuez-Havupalo
Matti Waris
Qiushui He
Kristi L Hoffman
Joseph F Petrosino
Orianne Dumas
Carlos A Camargo
Kohei Hasegawa
Ville Peltola
Author Affiliation
Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts; laura.toivonen@utu.fi.
Source
Pediatrics. 2020 10; 146(4):
Date
10-2020
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Aerococcaceae - isolation & purification
Age Factors
Asthma - diagnosis - etiology - microbiology
Child
Child, Preschool
Female
Finland
Follow-Up Studies
Gene Expression Profiling - methods
Haemophilus - isolation & purification
Humans
Incidence
Infant
Infant, Newborn
Machine Learning
Male
Microbiota - genetics
Moraxella - isolation & purification
Multivariate Analysis
Nose - microbiology
Prospective Studies
RNA, Ribosomal, 16S - genetics
Respiratory Tract Infections - complications - epidemiology - microbiology
Risk
Streptococcus - isolation & purification
Abstract
Although the airway microbiota is a highly dynamic ecology, the role of longitudinal changes in airway microbiota during early childhood in asthma development is unclear. We aimed to investigate the association of longitudinal changes in early nasal microbiota with the risk of developing asthma.
In this prospective, population-based birth cohort study, we followed children from birth to age 7 years. The nasal microbiota was tested by using 16S ribosomal RNA gene sequencing at ages 2, 13, and 24 months. We applied an unsupervised machine learning approach to identify longitudinal nasal microbiota profiles during age 2 to 13 months (the primary exposure) and during age 2 to 24 months (the secondary exposure) and examined the association of these profiles with the risk of physician-diagnosed asthma at age 7 years.
Of the analytic cohort of 704 children, 57 (8%) later developed asthma. We identified 4 distinct longitudinal nasal microbiota profiles during age 2 to 13 months. In the multivariable analysis, compared with the persistent Moraxella dominance profile during age 2 to 13 months, the persistent Moraxella sparsity profile was associated with a significantly higher risk of asthma (adjusted odds ratio, 2.74; 95% confidence interval, 1.20-6.27). Similar associations were observed between the longitudinal changes in nasal microbiota during age 2 to 24 months and risk of asthma.
Children with an altered longitudinal pattern in the nasal microbiota during early childhood had a high risk of developing asthma. Our data guide the development of primary prevention strategies (eg, early identification of children at high risk and modification of microbiota) for childhood asthma. These observations present a new avenue for risk modification for asthma (eg, microbiota modification).
PubMed ID
32934151 View in PubMed
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31 records – page 1 of 4.