scholarly journals Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

2018 ◽  
Author(s):  
Franco van Wyk ◽  
Anahita Khojandi ◽  
Robert L. Davis ◽  
Rishikesan Kamaleswaran
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Real Time ◽  
Early Stage ◽  
Time Windows ◽  
Machine Learning Algorithms ◽  
Physiological Data ◽  
Threatening Condition ◽  
Risk Patients ◽  
Life Threatening

AbstractRationale: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.Objective: Our primary goal was to develop machine learning models capable of predicting sepsis using streaming physiological data in real-time.Methods: A dataset consisting of high-frequency physiological data from 1,161 critically ill patients admitted to the intensive care unit (ICU) was analyzed in this IRB-approved retrospective observational cohort study. Of that total, 634 patients were identified to have developed sepsis. In this paper, we define sepsis as meeting the Systemic Inflammatory Response Syndrome (SIRS) criteria in the presence of the suspicion of infection. In addition to the physiological data, we include white blood cell count (WBC) to develop a model that can signal the future occurrence of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients using a total of 108 features extracted from 2-hour moving time-windows. The models were trained on 80% of the patients and were tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 hours.Results: The models, respectively, resulted in F1 scores of 75% and 69% half-hour before sepsis onset and 79% and 76% ten minutes before sepsis onset. On average, the models were able to predict sepsis 210 minutes (3.5 hours) before the onset.Conclusions: The use of robust machine learning algorithms, continuous streams of physiological data, and WBC, allows for early identification of at-risk patients in real-time with high accuracy.

scholarly journals Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5986
Author(s):  
Iwona Doroniewicz ◽  
Daniel J. Ledwoń ◽  
Alicja Affanasowicz ◽  
Katarzyna Kieszczyńska ◽  
Dominika Latos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Time Windows ◽  
Therapeutic Process ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Spontaneous Movement ◽  
Machine Learning Classification ◽  
Movement Assessment ◽  
General Movement

Observation of neuromotor development at an early stage of an infant’s life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the foundation for contemporary attempts at objectification and computer-aided diagnosis based on video recordings’ analysis. The present study attempts to automatically detect writhing movements, one of the normal general movement categories presented by newborns in the first weeks of life. A set of 31 recordings of newborns on the second and third day of life was divided by five experts into videos containing writhing movements (with occurrence time) and poor repertoire, characterized by a lower quality of movement in relation to the norm. Novel, objective pose-based features describing the scope, nature, and location of each limb’s movement are proposed. Three machine learning algorithms are evaluated in writhing movements’ detection in leave-one-out cross-validation for different feature extraction time windows and overlapping time. The experimental results make it possible to indicate the optimal parameters for which 80% accuracy was achieved. Based on automatically detected writhing movement percent in the video, infant movements are classified as writhing movements or poor repertoire with an area under the ROC (receiver operating characteristics) curve of 0.83.

scholarly journals Predicting stroke risk by Migraine using AI

2021 ◽  
pp. 285-290
Author(s):  
Anchal Singh ◽  
Dr. Surabhi Thorat
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Early Stage ◽  
Blood Clot ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Machine Learning Classification ◽  
Life Threatening ◽  
Confusion Matrices ◽  
The Brain

Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. It is the second leading cause of death worldwide and one of the most life- threatening diseases for persons above 65 years. It damages the brain like “heart attack” which damages the heart. Every 4 minutes someone dies of stroke, but up to 80% of stroke can be prevented if we can identify or predict the occurrence of stroke in its early stage. In this paper, I used different types of machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke data. Four types of machine learning classification algorithms were applied; Linear Regression, Confusion matrices, Random Forest Classifier, and Logistic Regression were used to build the stroke prediction model. Support, Precision, Recall, and F1-score were used to calculate performance measures of machine learning models. The results showed that Random Forest Classifier has achieved the best accuracy at 94 % [1].

scholarly journals Study of Interpretability in ML Algorithms for Disease Prognosis

2021 ◽  
Vol 11 (4) ◽  
pp. 4735-4749
Author(s):  
P. Archana Menon ◽  
Dr.R. Gunasundari
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Medical Practitioner ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Disease Prognosis ◽  
Promising Area ◽  
Life Threatening ◽  
Interpretable Model ◽  
Severity Of The Disease

Disease prognosis plays an important role in healthcare. Diagnosing disease at an early stage is crucial to provide treatment to the patient at the earliest in order to save his/her life or to at least reduce the severity of the disease. Application of Machine Learning algorithms is a promising area for the early and accurate diagnosis of chronic diseases. The black-box approach of Machine Learning models has been circumvented by providing different Interpretability methods. The importance of interpretability in health care field especially while taking decisions in life threatening diseases is crucial. Interpretable model increases the confidence of a medical practitioner in taking decisions. This paper gives an insight to the importance of explanations as well as the interpretability methods applied to different machine learning and deep learning models developed in recent years.

scholarly journals The APAC Score: A Novel and Highly Performant Serological Tool for Early Diagnosis of Hepatocellular Carcinoma in Patients with Liver Cirrhosis

2021 ◽  
Vol 10 (15) ◽  
pp. 3392
Author(s):  
Joeri Lambrecht ◽  
Mustafa Porsch-Özçürümez ◽  
Jan Best ◽  
Fabian Jost-Brinkmann ◽  
Christoph Roderburg ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
At Risk ◽  
Liver Cirrhosis ◽  
Early Stage ◽  
Treatment Options ◽  
Growth Factor Receptor ◽  
Serum Samples ◽  
Disease Etiology ◽  
Risk Patients ◽  
Scoring Tool

(1) Background: Surveillance of at-risk patients for hepatocellular carcinoma (HCC) is highly necessary, as curative treatment options are only feasible in early disease stages. However, to date, screening of patients with liver cirrhosis for HCC mostly relies on suboptimal ultrasound-mediated evaluation and α-fetoprotein (AFP) measurement. Therefore, we sought to develop a novel and blood-based scoring tool for the identification of early-stage HCC. (2) Methods: Serum samples from 267 patients with liver cirrhosis, including 122 patients with HCC and 145 without, were collected. Expression levels of soluble platelet-derived growth factor receptor beta (sPDGFRβ) and routine clinical parameters were evaluated, and then utilized in logistic regression analysis. (3) Results: We developed a novel serological scoring tool, the APAC score, consisting of the parameters age, sPDGFRβ, AFP, and creatinine, which identified patients with HCC in a cirrhotic population with an AUC of 0.9503, which was significantly better than the GALAD score (AUC: 0.9000, p = 0.0031). Moreover, the diagnostic accuracy of the APAC score was independent of disease etiology, including alcohol (AUC: 0.9317), viral infection (AUC: 0.9561), and NAFLD (AUC: 0.9545). For the detection of patients with (very) early (BCLC 0/A) HCC stage or within Milan criteria, the APAC score achieved an AUC of 0.9317 (sensitivity: 85.2%, specificity: 89.2%) and 0.9488 (sensitivity: 91.1%, specificity 85.3%), respectively. (4) Conclusions: The APAC score is a novel and highly accurate serological tool for the identification of HCC, especially for early stages. It is superior to the currently proposed blood-based algorithms, and has the potential to improve surveillance of the at-risk population.

scholarly journals Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer

2020 ◽  
Vol 48 (10) ◽  
pp. 030006052095880
Author(s):  
Jianping Wu ◽  
Sulai Liu ◽  
Xiaoming Chen ◽  
Hongfei Xu ◽  
Yaoping Tang
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Protein Interactions ◽  
Early Stage ◽  
Predictive Ability ◽  
Improve Patient Care ◽  
Active Role ◽  
Risk Groups ◽  
Recurrence Risk ◽  
Machine Learning Algorithms

Objective Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis. Methods We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence. Results By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein–protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%. Conclusion These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

Development and validation of simplified machine learning algorithms to predict prognosis of hospitalized COVID-19 patients: a multi-center, retrospective study (Preprint)

2021 ◽  
Author(s):  
Fang He ◽  
John H Page ◽  
Kerry R Weinberg ◽  
Anirban Mishra
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
High Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
High Risk Patients ◽  
Risk Patients ◽  
Development And Validation

BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained setting, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients, however there are few risk scores derived from a substantially large EHR dataset, using simplified predictors as input. OBJECTIVE To develop and validate simplified machine learning algorithms which predicts COVID-19 adverse outcomes, to evaluate the AUC (area under the receiver operating characteristic curve), sensitivity, specificity and calibration of the algorithms, to derive clinically meaningful thresholds. METHODS We conducted machine learning model development and validation via cohort study using multi-center, patient-level, longitudinal electronic health records (EHR) from Optum® COVID-19 database which provides anonymized, longitudinal EHR from across US. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, ICU admission, respiratory failure, mechanical ventilator usages at inpatient setting. Data from patients who were admitted prior to Sep 7, 2020, is randomly sampled into development, test and validation datasets; data collected from Sep 7, 2020 through Nov 15, 2020 was reserved as prospective validation dataset. RESULTS Of 3.7M patients in the analysis, a total of 585,867 patients were diagnosed or tested positive for SARS-CoV-2; and 50,703 adult patients were hospitalized with COVID-19 between Feb 1 and Nov 15, 2020. Among the study cohort (N=50,703), there were 6,204 deaths, 9,564 ICU admissions, 6,478 mechanically ventilated or EMCO patients and 25,169 patients developed ARDS or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC = 0.89 (0.89 - 0.89) on validation dataset (N=10,752)), consistent prediction through the second wave of pandemic from September to November (AUC = 0.85 (0.85 - 0.86) on post-development validation (N= 14,863)), great clinical relevance and utility. Besides, a comprehensive 386 input covariates from baseline and at admission was included in the analysis; the end-to-end pipeline automates feature selection and model development process, producing 10 key predictors as input such as age, blood urea nitrogen, oxygen saturation, which are both commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validations demonstrate consistent model performance to predict even beyond the time period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated and reliable prediction model based on only ten clinical features as a prognostic tool to stratifying COVID-19 patients into intermediate, high and very high-risk groups. This simple predictive tool could be shared with a wider healthcare community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize healthcare resources. CLINICALTRIAL N/A

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