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Diabetes machine learning

Konrad Pieszko, Jarosław Hiczkiewicz, Paweł Budzianowski, Janusz Rzeźniczak, Jan Budzianowski, Jerzy Błaszczyński, Roman Słowiński, Paweł Burchardt
BACKGROUND: Increased systemic and local inflammation play a vital role in the pathophysiology of acute coronary syndrome. This study aimed to assess the usefulness of selected machine learning methods and hematological markers of inflammation in predicting short-term outcomes of acute coronary syndrome (ACS). METHODS: We analyzed the predictive importance of laboratory and clinical features in 6769 hospitalizations of patients with ACS. Two binary classifications were considered: significant coronary lesion (SCL) or lack of SCL, and in-hospital death or survival...
December 3, 2018: Journal of Translational Medicine
Yury O Nunez Lopez, Ravi Retnakaran, Bernard Zinman, Richard E Pratley, Attila A Seyhan
OBJECTIVE: Short-term intensive insulin therapy (IIT) early in the course of type 2 diabetes acutely improves beta-cell function with long-lasting effects on glycemic control. However, conventional measures cannot determine which patients are better suited for IIT, and little is known about the molecular mechanisms determining response. Therefore, this study aimed to develop a model that could accurately predict the response to IIT and provide insight into molecular mechanisms driving such response in humans...
November 16, 2018: Molecular Metabolism
Maykel Cruz Monteagudo, Humbert González-Díaz
We are publishing the series of special issues "New experimental and computational tools for drug discovery" focused on the use of enabling technologies like Computational Chemistry, Bioinformatics, OMICS, Combinatorial Chemistry, Data Analysis, etc. in Medicinal Chemistry and Drug Discovery. The series have published a total of five special issues until this moment (1-5). The present issue (Part - VI) includes a new collection of papers exploring the use of Experimental techniques (LC-MS/MS, FTIR, or NMR, etc...
November 30, 2018: Current Topics in Medicinal Chemistry
Katsutoshi Maeta, Yu Nishiyama, Kazutoshi Fujibayashi, Toshiaki Gunji, Noriko Sasabe, Kimiko Iijima, Toshio Naito
BACKGROUND: A 75-g oral glucose tolerance test (OGTT) provides important information about glucose metabolism, although the test is expensive and invasive. Complete OGTT information, such as 1-hour and 2-hour postloading plasma glucose and immunoreactive insulin levels, may be useful for predicting the future risk of diabetes or glucose metabolism disorders (GMD), which includes both diabetes and prediabetes. OBJECTIVE: We trained several classification models for predicting the risk of developing diabetes or GMD using data from thousands of OGTTs and a machine learning technique (XGBoost)...
November 26, 2018: JMIR diabetes
Shao-Chun Chen, Hung-Wen Chiu, Chun-Chen Chen, Lin-Chung Woung, Chung-Ming Lo
PURPOSE: Artificial neural networks (ANNs) are one type of artificial intelligence. Here, we use an ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema. METHODS: Patient data were used to optimize ANNs for regression calculation. The target was established as the final visual acuity at 52, 78, or 104 weeks. The input baseline variables were sex, age, diabetes type or condition, systemic diseases, eye status and treatment time tables...
November 24, 2018: Journal of Clinical Medicine
Theodora S Brisimi, Tingting Xu, Taiyao Wang, Wuyang Dai, Ioannis Ch Paschalidis
OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized)...
November 25, 2018: Statistical Methods in Medical Research
Young Chul Youn, Seong Hye Choi, Hae-Won Shin, Ko Woon Kim, Jae-Won Jang, Jason J Jung, Ging-Yuek Robin Hsiung, SangYun Kim
Purpose: The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE...
2018: Neuropsychiatric Disease and Treatment
Quan Zou, Kaiyang Qu, Yamei Luo, Dehui Yin, Ying Ju, Hua Tang
Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world's diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus...
2018: Frontiers in Genetics
(no author information available yet)
OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e., nonroutine discharge) after elective inpatient spine surgery would be helpful in terms of decreasing hospital length of stay. The purpose of this study was to use machine learning algorithms to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders...
November 1, 2018: Neurosurgical Focus
(no author information available yet)
OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts...
November 1, 2018: Neurosurgical Focus
Haoqi Sun, Luis Paixao, Jefferson T Oliva, Balaji Goparaju, Diego Z Carvalho, Kicky G van Leeuwen, Oluwaseun Akeju, Robert J Thomas, Sydney S Cash, Matt T Bianchi, M Brandon Westover
The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age (BA)," which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18-80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40-80). The model obtains a mean absolute deviation of 7...
October 19, 2018: Neurobiology of Aging
Cynthia Khanji, Lyne Lalonde, Céline Bareil, Marie-Thérèse Lussier, Sylvie Perreault, Mireille E Schnitzer
BACKGROUND: Cardiovascular disease morbidity and mortality are largely influenced by poor control of hypertension, dyslipidemia, and diabetes. Process indicators are essential to monitor the effectiveness of quality improvement strategies. However, process indicators should be validated by demonstrating their ability to predict desirable outcomes. The objective of this study is to identify an effective method for building prediction models and to assess the predictive validity of the TRANSIT indicators...
November 14, 2018: Medical Care
Parham Khojasteh, Leandro Aparecido Passos Júnior, Tiago Carvalho, Edmar Rezende, Behzad Aliahmad, João Paulo Papa, Dinesh Kant Kumar
Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate detection, but with poor performance. This study has investigated different deep learning techniques to maximize the sensitivity and specificity. We have compared multiple deep learning methods, and both supervised and unsupervised classifiers for improving the performance of automatic exudate detection, i...
November 3, 2018: Computers in Biology and Medicine
Tomas Karpati, Maya Leventer-Roberts, Becca Feldman, Chandra Cohen-Stavi, Itamar Raz, Ran Balicer
AIMS: To identify clinically meaningful clusters of patients with similar glycated hemoglobin (HbA1c) trajectories among patients with type 2 diabetes. METHODS: A retrospective cohort study using unsupervised machine learning clustering methodologies to determine clusters of patients with similar longitudinal HbA1c trajectories. Stability of these clusters was assessed and supervised random forest analysis verified the clusters' reproducibility. Clinical relevance of the clusters was assessed through multivariable analysis, comparing differences in risk for a composite outcome (macrovascular and microvascular outcomes, hypoglycemic events, and all-cause mortality) at HbA1c thresholds for each cluster...
2018: PloS One
Micael Pedrosa, Jorge Miguel Silva, João Figueira Silva, Sérgio Matos, Carlos Costa
BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection...
December 2018: International Journal of Medical Informatics
D Jude Hemanth, J Anitha, Le Hoang Son, Mamta Mittal
Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process...
October 31, 2018: Journal of Medical Systems
Hsin-Yi Tsao, Pei-Ying Chan, Emily Chia-Yu Su
BACKGROUND: The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions...
August 13, 2018: BMC Bioinformatics
Muthuraman Alagappan, Jeremy R Glissen Brown, Yuichi Mori, Tyler M Berzin
Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy...
October 16, 2018: World Journal of Gastrointestinal Endoscopy
Andy K O Wong, Eva Szabo, Marta Erlandson, Marshall S Sussman, Sravani Duggina, Anny Song, Shannon Reitsma, Hana Gillick, Jonathan D Adachi, Angela M Cheung
The accumulation of INTERmuscular fat and INTRAmuscular fat (IMF) has been a hallmark of individuals with diabetes, those with mobility impairments such as spinal cord injuries and is known to increase with aging. An elevated amount of IMF has been associated with fractures and frailty, but the imprecision of IMF measurement has so far limited the ability to observe more consistent clinical associations. Magnetic resonance imaging has been recognized as the gold standard for portraying these features, yet reliable methods for quantifying IMF on magnetic resonance imaging is far from standardized...
September 22, 2018: Journal of Clinical Densitometry
Jun S Kim, Varun Arvind, Eric K Oermann, Deepak Kaji, Will Ranson, Chierika Ukogu, Awais K Hussain, John Caridi, Samuel K Cho
STUDY DESIGN: Cross-sectional database study. OBJECTIVE: To train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD). SUMMARY OF BACKGROUND DATA: Machine learning models such as logistic regression (LR) and artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex data sets. ANNs have yet to be used for risk factor analysis in orthopedic surgery...
November 2018: Spine Deformity
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