scholarly journals Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning

2018 ◽  
Vol 5 (4) ◽  
pp. 107
Author(s):  
Jamie Scanlan ◽  
Francis Li ◽  
Olga Umnova ◽  
Gyorgy Rakoczy ◽  
Nóra Lövey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Large Population ◽  
The Elderly ◽  
Signal Acquisition ◽  
Impulse Responses ◽  
Electronic Stethoscope ◽  
Increased Risk ◽  
Bone Condition

Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications.

scholarly journals Prioritization of Candidate Genes for Congenital Diaphragmatic Hernia in a Critical Region on Chromosome 4p16 using a Machine-Learning Algorithm

2018 ◽  
Vol 07 (04) ◽  
pp. 164-173 ◽  
Author(s):  
Ian Campbell ◽  
Samantha Stover ◽  
Andres Hernandez-Garcia ◽  
Shalini Jhangiani ◽  
Jaya Punetha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Congenital Diaphragmatic Hernia ◽  
Diaphragmatic Hernia ◽  
Critical Region ◽  
Large Scale ◽  
Learning Algorithm ◽  
Heart Defects ◽  
Machine Learning Algorithm ◽  
Increased Risk ◽  
Chromosome 4P16

AbstractWolf–Hirschhorn syndrome (WHS) is caused by partial deletion of the short arm of chromosome 4 and is characterized by dysmorphic facies, congenital heart defects, intellectual/developmental disability, and increased risk for congenital diaphragmatic hernia (CDH). In this report, we describe a stillborn girl with WHS and a large CDH. A literature review revealed 15 cases of WHS with CDH, which overlap a 2.3-Mb CDH critical region. We applied a machine-learning algorithm that integrates large-scale genomic knowledge to genes within the 4p16.3 CDH critical region and identified FGFRL1, CTBP1, NSD2, FGFR3, CPLX1, MAEA, CTBP1-AS2, and ZNF141 as genes whose haploinsufficiency may contribute to the development of CDH.

Using machine learning to predict 30-day readmissions after posterior lumbar fusion: an NSQIP study involving 23,264 patients

2020 ◽  
Vol 32 (3) ◽  
pp. 399-406 ◽  
Author(s):  
Benjamin S. Hopkins ◽  
Jonathan T. Yamaguchi ◽  
Roxanna Garcia ◽  
Kartik Kesavabhotla ◽  
Hannah Weiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spine Surgery ◽  
Learning Algorithm ◽  
Lumbar Fusion ◽  
False Positive Rate ◽  
Hospital Readmissions ◽  
Posterior Spinal Fusion ◽  
Machine Learning Techniques ◽  
Improvement Program ◽  
Predictive Values

OBJECTIVEUnplanned preventable hospital readmissions within 30 days are a great burden to patients and the healthcare system. With an estimated $41.3 billion spent yearly, reducing such readmission rates is of the utmost importance. With the widespread adoption of big data and machine learning, clinicians can use these analytical tools to understand these complex relationships and find predictive factors that can be generalized to future patients. The object of this study was to assess the efficacy of a machine learning algorithm in the prediction of 30-day hospital readmission after posterior spinal fusion surgery.METHODSThe authors analyzed the distribution of National Surgical Quality Improvement Program (NSQIP) posterior lumbar fusions from 2011 to 2016 by using machine learning techniques to create a model predictive of hospital readmissions. A deep neural network was trained using 177 unique input variables. The model was trained and tested using cross-validation, in which the data were randomly partitioned into training (n = 17,448 [75%]) and testing (n = 5816 [25%]) data sets. In training, the 17,448 training cases were fed through a series of 7 layers, each with varying degrees of forward and backward communicating nodes (neurons).RESULTSMean and median positive predictive values were 78.5% and 78.0%, respectively. Mean and median negative predictive values were both 97%, respectively. Mean and median areas under the curve for the model were 0.812 and 0.810, respectively. The five most heavily weighted inputs were (in order of importance) return to the operating room, septic shock, superficial surgical site infection, sepsis, and being on a ventilator for > 48 hours.CONCLUSIONSMachine learning and artificial intelligence are powerful tools with the ability to improve understanding of predictive metrics in clinical spine surgery. The authors’ model was able to predict those patients who would not require readmission. Similarly, the majority of predicted readmissions (up to 60%) were predicted by the model while retaining a 0% false-positive rate. Such findings suggest a possible need for reevaluation of the current Hospital Readmissions Reduction Program penalties in spine surgery.

Challenges in successful implementation of Digital contact tracing to curb COVID-19 from global citizen’s perspective: A text analysis study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Praveen S.V. ◽  
Rajesh Ittamalla ◽  
Dhilip Subramanian
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Large Population ◽  
Analysis Data ◽  
Contact Tracing ◽  
Successful Implementation ◽  
South American ◽  
Content Type ◽  
Global Citizens ◽  
The One

Purpose The word “digital contact tracing” is often met with different reactions: the reaction that passionately supports it, the reaction that neither supports nor oppose and the one that vehemently opposes it. Those who support the notion of digital contact tracing vouch for its effectiveness and how the complicated process can be made simpler by implementing digital contact tracing, and those who oppose it often criticize the imminent threats it possesses. However, without earning the support of a large population, it would be difficult for any government to implement digital contact tracing to perfection. The purpose of this paper is to analyze, using machine learning, how different continents have different sentiments over digital contact tracing being used as a measure to curb COVID-19. Design/methodology/approach For the analysis, data were collected from Twitter. Tweets that contain the hashtag and the word “digital contact tracing” were crawled using Python library Tweepy. Tweets across countries of four continents were collected from March 2020 to August 2020. In total, 70,212 tweets were used for this study. Using the machine learning algorithm, the authors detected the sentiment of all the tweets belonging to each continent. Structural topic modeling was used to understand the overall significant issues people voice out by global citizens while sharing their opinions on digital contact tracing. Findings This study was conducted in two parts. Study one results show that North American and European citizens share more negative sentiments toward “digital contact tracing.” The citizens of the Asian and South American continent mostly share neutral sentiments regarding the digital contact tracing. Overall, only 33% of total tweets were positively related to contact tracing, whereas 52% of the total tweets were neutral. Study two results show that factors such as fear of government using contact tracing to spy on its people, the feeling of being unsafe and contact tracing being used to promote an agenda were the three major issues concerning the overall general public. Originality/value Despite numerous studies being conducted about how to implement the contact tracing efficiently, minimal studies were done to explore the possibility and challenges in implementing it. This study fills the gap.

scholarly journals A novel machine learning algorithm has the potential to reduce by 1/3 the quantity of ILR episodes needing review

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
A Rosier ◽  
E Crespin ◽  
A Lazarus ◽  
G Laurent ◽  
A Menet ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Funding Sources ◽  
High Workload ◽  
Positive Rate ◽  
The Impact

Abstract Background Implantable Loop Recorders (ILRs) are increasingly used and generate a high workload for timely adjudication of ECG recordings. In particular, the excessive false positive rate leads to a significant review burden. Purpose A novel machine learning algorithm was developed to reclassify ILR episodes in order to decrease by 80% the False Positive rate while maintaining 99% sensitivity. This study aims to evaluate the impact of this algorithm to reduce the number of abnormal episodes reported in Medtronic ILRs. Methods Among 20 European centers, all Medtronic ILR patients were enrolled during the 2nd semester of 2020. Using a remote monitoring platform, every ILR transmitted episode was collected and anonymised. For every ILR detected episode with a transmitted ECG, the new algorithm reclassified it applying the same labels as the ILR (asystole, brady, AT/AF, VT, artifact, normal). We measured the number of episodes identified as false positive and reclassified as normal by the algorithm, and their proportion among all episodes. Results In 370 patients, ILRs recorded 3755 episodes including 305 patient-triggered and 629 with no ECG transmitted. 2821 episodes were analyzed by the novel algorithm, which reclassified 1227 episodes as normal rhythm. These reclassified episodes accounted for 43% of analyzed episodes and 32.6% of all episodes recorded. Conclusion A novel machine learning algorithm significantly reduces the quantity of episodes flagged as abnormal and typically reviewed by healthcare professionals. FUNDunding Acknowledgement Type of funding sources: None. Figure 1. ILR episodes analysis

scholarly journals Risk of Relapse after Anti-PD1 Discontinuation and Efficacy of Anti-PD1 Re-Treatment in Patients with Hodgkin Lymphoma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1549-1549 ◽  
Author(s):  
Guillaume Manson ◽  
Pauline Brice ◽  
Charles Herbaux ◽  
Maria Silva ◽  
Krimo Bouabdallah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hodgkin Lymphoma ◽  
Research Funding ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Allogenic Stem Cell Transplantation ◽  
Increased Risk ◽  
Disease Free ◽  
Unacceptable Toxicity ◽  
Risk Of Relapse

Introduction Patients with relapsed/refractory Hodgkin lymphoma (R/R HL) experience high response rates upon anti-PD1 therapy. In these patients, the optimal duration of treatment and the risk of relapse after anti-PD1 discontinuation are unknown. Furthermore, the efficacy of anti-PD1 re-treatment in patients who relapse after anti-PD1 discontinuation remains to be determined. Here, we investigated the risk of relapse in patients who responded to anti-PD1 therapy and discontinued the treatment, as well as the efficacy of anti-PD1 re-treatment in patients who relapsed after anti-PD1 discontinuation. Methods We retrospectively analyzed patients with R/R HL who responded to anti-PD1 monotherapy (concomitant radiotherapy was permitted) and discontinued the treatment either because of unacceptable toxicity or prolonged remission (based on the clinician's decision). Patients who discontinued because of relapse/progression or underwent consolidation with allogenic stem cell transplantation [alloSCT] were not included. A random forest machine-learning algorithm was trained to predict relapse using 14 candidate biomarkers. Finally, we analyzed the outcome of patients who relapsed after anti-PD1 discontinuation and their response to anti-PD1 re-treatment. Results We included 32 patients from 13 Centers in France, Portugal and Belgium. Patients' characteristics are summarized in Table 1. At the time of anti-PD1 discontinuation, patients had received either nivolumab (N=27, 84.4%) or pembrolizumab (N=5, 12.5%) for a median duration of 14.6 (range, 0-33.5) months. Patients discontinued anti-PD1 treatment either because of prolonged remission (N=23, 71.9%) or unacceptable toxicity (N=9, 28.1%). Most patients were in CR (N=29, 90.1%) at the time of anti-PD1 discontinuation. After a median follow-up of 20.8 months (range, 0.7-47.6) from anti-PD1 discontinuation, 21 (65.6%) patients had not relapsed/progressed. All 3 patients who were in PR at the time of anti-PD1 discontinuation had relapsed. Among the 29 patients who were in CR at the time of anti-PD1 discontinuation, the estimated disease-free survival was 64.3% (CI 95, 46.6-88.7%) at 24 months (Figure 1). Three patients died: two from disease progression and one from severe GVHD while in CR. Interestingly, 4 patients remain in CR more than 3 years after anti-PD1 discontinuation although these patients had received only short courses of anti-PD1 (<6 months). One of them received a single dose of nivolumab for a relapse post-alloSCT and remains disease-free 47.6 months later. Using a testing set of 25 patients, the machine-learning algorithm predicted an increased risk of relapse at 12 months based on three main patients characteristics: the absence of complete metabolic response at the end of anti-PD1 treatment, prolonged time to achieve best overall response, and older age. Among the 11 patients who relapsed, 7 were re-treated with (the same) anti-PD1 (Figure 2). Five achieved a CR, 1 achieved a PR and one patient has not been evaluated yet (but is in clinical response). Conclusion A significant proportion of patients experience prolonged remissions after anti-PD1 discontinuation and thus might be cured. Using a machine-learning algorithm, we identified biomarkers capable of predicting the risk of relapse after anti-PD1 discontinuation. These biomarkers are currently being validated in an independent set of patients. Finally, among patients who relapse after anti-PD1 discontinuation, re-treatment with anti-PD1 appears to be efficient. Disclosures Manson: Bristol Myers Squibb: Honoraria. Brice:Takeda France: Consultancy, Honoraria; Millennium Takeda: Research Funding; BMS: Honoraria. Herbaux:Janssen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Abbvie: Honoraria; Gilead: Honoraria. Silva:Abbvie Inc: Consultancy; Celgene: Consultancy; Gilead Sciences: Consultancy, Research Funding; Janssen Cilag: Consultancy; Roche: Consultancy. Stamatoulas Bastard:Celgene: Honoraria; Takeda: Consultancy. Houot:Bristol Myers Squibb: Honoraria; Merck Sharp Dohme: Honoraria.

Thyroid Function Changes in the Elderly and Their Relationship to Cardiovascular Health: A Mini-Review

Gerontology ◽  
2018 ◽  
Vol 65 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Giuseppe Barbesino
Keyword(s):  
Thyroid Function ◽  
Cardiovascular Health ◽  
Large Population ◽  
The Elderly ◽  
Thyroid Stimulating Hormone ◽  
Subclinical Hyperthyroidism ◽  
Potential Harm ◽  
Cardiovascular Morbidity And Mortality ◽  
Increased Risk ◽  
Sixth Decade

Background: Thyroid hormones have significant effects on the cardiovascular systems. In general, hyperthyroidism is associated with an increased risk of dysrhythmias, while hypothyroidism may cause atherosclerosis. Recent large studies have sought to identify aging-associated changes in thyroid function and their relevance to cardiovascular morbidity and mortality in the elderly. Conflicting results have often been published, likely due to the heterogeneity of the studied populations. Objective: This review seeks to briefly summarize the most recent large population studies analyzing thyroid changes with aging and interpreting their effects on cardiovascular health in the elderly. Methods: Selective review of recent literature. Results: The emerging pattern suggests a slight decrease in thyroid function in the elderly leading to slightly higher thyroid stimulating hormone (TSH) levels. However, the incidence of mild hyperthyroidism also increases, especially in populations with historical or current iodine deficiency. Large observational studies suggest that the potential harm from mild hypothyroidism seen in younger population tends to diminish in older subjects, while the harm from mild hyperthyroidism becomes more significant. A markedly increased risk of atrial fibrillation is a well-established consequence of subclinical hyperthyroidism in patients in the sixth decade of life and beyond. Conclusions: The absence of large prospective interventional data does not allow the formulation of strict clinical recommendations, but a higher TSH threshold for treating both subclinical hypothyroidism and subclinical hyperthyroidism in the elderly seems reasonable.

scholarly journals LC-MS Peak Assignment Based on Unanimous Selection by Six Machine Learning Algorithms

2021 ◽  
Author(s):  
Hiroaki Ito ◽  
Takashi Matsui ◽  
Ryo Konno ◽  
Makoto Itakura ◽  
Yoshio Kodera
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Weak Signals ◽  
Accuracy And Precision ◽  
Peak Assignment ◽  
Positive Rate ◽  
Assignment Strategy

Abstract Recent Mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance resulted in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

Abstract 17133: The Use of Novel Hematological Markers in Predicting Stroke After TAVR Using a Machine Learning Algorithm

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Aliaskar Z Hasani ◽  
Kusha Rahgozar ◽  
Aaron Wengrofsky ◽  
Narasimha Kuchimanchi ◽  
Mohammad Hashim Mustehsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Medical Center ◽  
Hematological Parameters ◽  
Significant Risk ◽  
The Elderly ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Tree Algorithms

Introduction: Aortic Stenosis is the most common valvular disorder with a predominance in the elderly. Trans-Aortic Valve Replacement (TAVR) has been an effective procedure with marked improvement in quality of life for patients. The procedure carries a small, yet clinically significant risk of stroke. The use of Neutrophil-Lymphocyte Ratios (NLR) and Platelet-Lymphocyte Ratios (PLR) have been growing as novel markers of systemic inflammation. We investigated the ability of a machine learning algorithm (Light GBM) to predict and weigh these ratios along with other clinical parameters for prediction of stroke after TAVR. Objective: To demonstrate the efficacy of the Supervised Machine Learning algorithm, Light GBM, in identifying important variables to predict stroke after TAVR. Methods: We performed a retrospective analysis of 291 patients who underwent TAVR from 2015-2019 at Montefiore Medical Center. Age (80±8), 50.2% Female, BMI (28.7 ±6.3). Clinical data was collected through our Hospital EMR. NLR and PLR averages were obtained using the mean of baseline (prior to surgery), Immediate Post-operative, and Post-operative Day 1 values. A supervised machine learning algorithm, Light GBM, used decision tree algorithms with both level-wise growth and leaf-wise growth. The algorithm was trained on 80% of the data and internally validated on the other 20%. Results: We identified NLR and PLR as the second and third most important feature of importance (Table 1) Clinical and demographic features of importance included BMI, Age, and Sex. Our model when internally validated yield a Sensitivity of 75.0%, Specificity of 91.5%, Accuracy of 91.5%, and F1 of 0.75. The AUC for the model was 0.84. Conclusions: Using Novel hematological parameters in conjunction with machine learning algorithms have highlighted important variables in predicting stroke after TAVR. Extrapolated, average NLR and PLR can be an inexpensive tool in stratifying patients those patients most at risk.

scholarly journals Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 79 ◽  
Author(s):  
S. Kok ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Phase I ◽  
Phase Ii ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Application Programming Interface ◽  
Detection Algorithm ◽  
Positive Rate ◽  
New Type ◽  
Two Phases

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious program would be captured and analyzed using the learning algorithm (LA). The LA can determine whether the suspicious program was a crypto-ransomware or not, through API pattern recognition. This approach was used to ensure the most comprehensive detection of both known and unknown crypto-ransomware, but it may have a high false positive rate (FPR). If the prediction was a crypto-ransomware, PEDA would generate a signature of the suspicious program, and store it in the signature repository, which was in Phase-II. In PEDA-Phase-II, the signature repository allows the detection of crypto-ransomware at a much earlier stage, which was at the pre-execution stage through the signature matching method. This method can only detect known crypto-ransomware, and although very rigid, it was accurate and fast. The two phases in PEDA formed two layers of early detection for crypto-ransomware to ensure zero files lost to the user. However in this research, we focused upon Phase-I, which was the LA. Based on our results, the LA had the lowest FPR of 1.56% compared to Naive Bayes (NB), Random Forest (RF), Ensemble (NB and RF) and EldeRan (a machine learning approach to analyze and classify ransomware). Low FPR indicates that LA has a low probability of predicting goodware wrongly.

scholarly journals Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hugues Caly ◽  
Hamed Rabiei ◽  
Perrine Coste-Mazeau ◽  
Sebastien Hantz ◽  
Sophie Alain ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Behavioral Interventions ◽  
Learning Algorithm ◽  
Statistical Tests ◽  
False Positive Rate ◽  
Supervised Machine Learning ◽  
Fetal Head ◽  
Positive Rate ◽  
History Of

AbstractTo identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.

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