scholarly journals A multi-layer model for the early detection of COVID-19

2021 ◽  
Vol 18 (181) ◽  
pp. 20210284
Author(s):  
Erez Shmueli ◽  
Ronen Mansuri ◽  
Matan Porcilan ◽  
Tamar Amir ◽  
Lior Yosha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
General Health ◽  
Medical Condition ◽  
Area Under The Curve ◽  
Sociodemographic Characteristics ◽  
Layer Model ◽  
Machine Learning Model ◽  
Spatio Temporal ◽  
The Individual

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.

scholarly journals A Multilayer Model for Early Detection of COVID-19

2021 ◽  
Author(s):  
Erez Shmueli ◽  
Ronen Mansuri ◽  
Matan Porcilan ◽  
Tamar Amir ◽  
Lior Yosha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Medical Condition ◽  
Area Under The Curve ◽  
Spatiotemporal Patterns ◽  
Sociodemographic Characteristics ◽  
Multilayer Model ◽  
The Past ◽  
Machine Learning Model ◽  
The Individual

ABSTRACTCurrent efforts for COVID-19 screening mainly rely on reported symptoms and potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that utilizes four layers of information: 1) sociodemographic characteristics of the tested individual, 2) spatiotemporal patterns of the disease observed near the testing episode, 3) medical condition and general health consumption of the tested individual over the past five years, and 4) information reported by the tested individual during the testing episode. We evaluated our model on 140,682 members of Maccabi Health Services, tested for COVID-19 at least once between February and October 2020. These individuals had 264,516 COVID-19 PCR-tests, out of which 16,512 were found positive. Our multilayer model obtained an area under the curve (AUC) of 81.6% when tested over all individuals, and of 72.8% when tested over individuals who did not report any symptom. Furthermore, considering only information collected before the testing episode – that is, before the individual may had the chance to report on any symptom – our model could reach a considerably high AUC of 79.5%. Namely, most of the value contributed by the testing episode can be gained by earlier information. Our ability to predict early the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be utilized for a more efficient testing policy.

scholarly journals A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images

2021 ◽  
pp. 104838
Author(s):  
Amith Khandakar ◽  
Muhammad.E.H. Chowdhury ◽  
Mamun Bin Ibne Reaz ◽  
Sawal Hamid Md Ali ◽  
Md Anwarul Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Diabetic Foot ◽  
Learning Model ◽  
Machine Learning Model

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (5) ◽  
pp. 1595-1604
Author(s):  
Fabrizio Buffolo ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Daniel Heinrich ◽  
Christian Adolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Hypertension ◽  
Primary Aldosteronism ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Individual Risk ◽  
The Individual

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.

scholarly journals MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

2020 ◽  
Vol 9 (2) ◽  
pp. 343 ◽  
Author(s):  
Arash Kia ◽  
Prem Timsina ◽  
Himanshu N. Joshi ◽  
Eyal Klang ◽  
Rohit R. Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Area Under The Curve ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Support Vector ◽  
Adult Age ◽  
Machine Learning Model ◽  
Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

P1060USING ARTIFICIAL INTELLIGENCE TO PREDICT HOME THERAPY CANDIDATES

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Jerry Yu ◽  
Andrew Long ◽  
Maria Hanson ◽  
Aleetha Ellis ◽  
Michael Macarthur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Feedback Loop ◽  
Area Under The Curve ◽  
Patient Characteristics ◽  
Training Data ◽  
Quality Improvement Initiative ◽  
Home Therapy ◽  
Test Dataset ◽  
Machine Learning Model

Abstract Background and Aims There are many benefits for performing dialysis at home including more flexibility and more frequent treatments. A possible barrier to election of home therapy (HT) by in-center patients is a lack of adequate HT education. To aid efficient education efforts, a predictive model was developed to help identify patients who are more likely to switch from in-center and succeed on HT. Method We developed a model using machine learning to predict which patients who are treated in-center without prior HT history are most likely to switch to HT in the next 90 days and stay on HT for at least 90 days. Training data was extracted from 2016–2019 for approximately 300,000 patients. We randomly sampled one in-center treatment date per patient and determined if the patient would switch and succeed on HT. The input features consisted of treatment vitals, laboratories, absence history, comprehensive assessments, facility information, county-level housing, and patient characteristics. Patients were excluded if they had less than 30 days on dialysis due to lack of data. A machine learning model (XGBoost classifier) was deployed monthly in a pilot with a team of HT educators to investigate the model’s utility for identifying HT candidates. Results There were approximately 1,200 patients starting a home therapy per month in a large dialysis provider, with approximately one-third being in-center patients. The prevalence of switching and succeeding to HT in this population was 2.54%. The predictive model achieved an area under the curve of 0.87, sensitivity of 0.77, and a specificity of 0.80 on a hold-out test dataset. The pilot was successfully executed for several months and two major lessons were learned: 1) some patients who reappeared on each month’s list should be removed from the list after expressing no interest in HT, and 2) a data collection mechanism should be put in place to capture the reasons why patients are not interested in HT. Conclusion This quality-improvement initiative demonstrates that predictive modeling can be used to identify patients likely to switch and succeed on home therapy. Integration of the model in existing workflows requires creating a feedback loop which can help improve future worklists.

scholarly journals Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients

2020 ◽  
Vol 10 (11) ◽  
pp. 764
Author(s):  
Kyeong-Rae Kim ◽  
Hyeun Sung Kim ◽  
Jae-Eun Park ◽  
Seung-Yeon Kang ◽  
So-Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Stenosis ◽  
Area Under The Curve ◽  
Individual Characteristics ◽  
Learning Technology ◽  
Short Term ◽  
Korean Patients ◽  
Patient Prognosis ◽  
Prognostic Prediction ◽  
The Individual

Background: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time. Results: Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model. Conclusion: This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.

Efficient simulation of flood events using machine learning

2020 ◽  
Author(s):  
Jihane Elyahyioui ◽  
Valentijn Pauwels ◽  
Edoardo Daly ◽  
Francois Petitjean ◽  
Mahesh Prakash
Keyword(s):  
Machine Learning ◽  
Water Depth ◽  
Input Data ◽  
Learning Model ◽  
Water Levels ◽  
Training Dataset ◽  
Time Step ◽  
Machine Learning Model ◽  
Maximum Water ◽  
Spatio Temporal

<p>Flooding is one of the most common and costly natural hazards at global scale. Flood models are important in supporting flood management. This is a computationally expensive process, due to the high nonlinearity of the equations involved and the complexity of the surface topography. New modelling approaches based on deep learning algorithms have recently emerged for multiple applications.</p><p>This study aims to investigate the capacity of machine learning to achieve spatio-temporal flood modelling. The combination of spatial and temporal input data to obtain dynamic results of water levels and flows from a machine learning model on multiple domains for applications in flood risk assessments has not been achieved yet. Here, we develop increasingly complex architectures aimed at interpreting the raw input data of precipitation and terrain to generate essential spatio-temporal variables (water level and velocity fields) and derived products (flood maps) by training these based on hydrodynamic simulations.</p><p>An extensive training dataset is generated by solving the 2D shallow water equations on simplified topographies using Lisflood-FP.</p><p>As a first task, the machine learning model is trained to reproduce the maximum water depth, using as inputs the precipitation time series and the topographic grid. The models combine the spatial and temporal information through a combination of 1D and 2D convolutional layers, pooling, merging and upscaling. Multiple variations of this generic architecture are trained to determine the best one(s). Overall, the trained models return good results regarding performance indices (mean squared error, mean absolute error and classification accuracy) but fail at predicting the maximum water depths with sufficient precision for practical applications.</p><p>A major limitation of this approach is the availability of training examples. As a second task, models will be trained to bring the state of the system (spatially distributed water depth and velocity) from one time step to the next, based on the same inputs as previously, generating the full solution equivalent to that of a hydrodynamic solver. The training database becomes much larger as each pair of consecutive time steps constitutes one training example.</p><p>Assuming that a reliable model can be built and trained, such methodology could be applied to build models that are faster and less computationally demanding than hydrodynamic models. Indeed, in with the synthetic cases shown here, the simulation times of the machine learning models (< seconds) are far shorter than those of the hydrodynamic model (a few minutes at least). These data-driven models could be used for interpolation and forecasting. The potential for extrapolation beyond the range of training datasets will also be investigated (different topography and high intensity precipitation events). </p>

scholarly journals Machine Learning Prediction for Complete Response to Hypomethylating Agents with or without Additional Agents in Patients with Newly Diagnosed Myelodysplastic Syndrome

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1720-1720
Author(s):  
Koji Sasaki ◽  
Guillermo Montalban Bravo ◽  
Rashmi Kanagal-Shamanna ◽  
Elias Jabbour ◽  
Farhad Ravandi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Board Of Directors ◽  
Research Funding ◽  
Area Under The Curve ◽  
Complete Response ◽  
Learning Model ◽  
Hypomethylating Agents ◽  
Newly Diagnosed ◽  
Advisory Committees ◽  
Machine Learning Model

Background: Myelodysplastic syndrome (MDS) is a heterogeneous malignant myeloid neoplasm of hematopoietic stem cells due to cytogenetic alterations and somatic mutations in genes (DNA methylation, DNA repair, chromatin regulation, RNA splicing, transcription regulation, and signal transduction). Hypomethylating agents (HMA) are the standard of care for MDS, and 40-60% of patients achieved response to HMA. However, the prediction for response is difficult due to the nature of heterogeneity and the context of clinical conditions such as the degree of cytopenias and the dependency on transfusion. Machine learning outperforms conventional statistical models for prediction in statistical competitions. Prediction with machine learning models may predict response in patients with MDS. The aim of this study is to develop a machine learning model for the prediction of complete response (CR) to HMA with or without additional therapeutic agents in patients with newly diagnosed MDS. Methods: From November 2012 to August 2017, we analyzed 435 patients with newly diagnosed MDS who received frontline therapy as follows; azacitidine (AZA) (3-day, 5-day, or 7-day) ± vorinostat ± ipilimumab ± nivolumab; decitabine (DAC) (3-day or 5-day) ± vorinostat; 5-day guadecitabine. Clinical variables, cytogenetic abnormalities, and the presence of genetic mutations by next generation sequencing (NGS) were included for variable selection. The whole cohort was randomly divided into training/validation and test cohorts at an 8:2 ratio. The training/validation cohort was used for 4-fold cross validation. Hyperparameter optimization was performed with Stampede2, which was ranked as the 15th fastest supercomputer at Texas Advanced Computing Center in June 2018. A gradient boosting decision tree-based framework with the LightGBM Python module was used after hyperparameter tuning for the development of the machine learning model with training/validation cohorts. The performance of prediction was assessed with an independent test dataset with the area under the curve. Results: We identified 435 patients with newly diagnosed MDS who enrolled on clinical trials as follows: 33 patients, 5-day AZA; 23, 5-day AZA + vorinostat; 43, 3-day AZA; 20, 5-day AZA + ipilimumab; 19 patients, AZA + nivolumab; 7, AZA + ipilumumab + nivolumab; 114, 5-day DAC; 74, 3-day DAC; 4, DAC + vorinostat; 97, 5-day guadecitabine. In the whole cohort, the median age at diagnosis was 68 years (range, 13.0-90.3); 117 (27%) patients had a history of prior radiation or cytotoxic chemotherapy; the median white blood cell count was 2.9 (×109/L) (range, 0.5-102); median absolute neutrophil count, 1.1 (×109/L) (range, 0.0-55.1); median hemoglobin count, 9.5 (g/dL) (range, 4.7-15.4); median platelet count, 63 (×109/L) (range, 2-881); and median blasts in bone marrow, 8% (range, 0-20). Among 411 evaluable patients for the revised international prognostic scoring system, 15 (4%) had very low risk disease; 42 (10%), low risk; 68 (17%), intermediate risk; 124 (30%), high risk; and 162 (39%), very high risk. Overall, 153 patients (53%) achieved CR. Hyperparameter tuning identified the optimal hyperparameters with colsample by tree of 0.175, learning rate of 0.262, the maximal depth of 2, minimal data in leaf of 29, number of leaves of 11, alpha regularization of 0.010, lambda regularization of 2.085, and subsample of 0.639. On the test cohort with 87 patients, the machine learning model accurately predicted response in 65 patients (75%); 53 non-CR among 56 non-CR (95% accuracy); and 12 CR among 31 CR (39% accuracy). The trend of accuracy improvement by iteration (i.e., the number of decision trees) is shown in Figure 1. The area under the curve was 0.761521 in the test cohort. Conclusion: Our machine learning model with clinical, cytogenetic, and NGS data can predict CR to HMA in patients with newly diagnosed MDS. This approach can identify patients who may benefit from HMA therapy with and without additional agents for response, and can optimize the timing of allogeneic stem cell transplant. Disclosures Sasaki: Otsuka: Honoraria; Pfizer: Consultancy. Jabbour:Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding. Ravandi:Cyclacel LTD: Research Funding; Selvita: Research Funding; Menarini Ricerche: Research Funding; Macrogenix: Consultancy, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xencor: Consultancy, Research Funding. Kadia:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Bioline RX: Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Research Funding; BMS: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Takahashi:Symbio Pharmaceuticals: Consultancy. DiNardo:syros: Honoraria; jazz: Honoraria; agios: Consultancy, Honoraria; celgene: Consultancy, Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees; medimmune: Honoraria; abbvie: Consultancy, Honoraria; daiichi sankyo: Honoraria. Cortes:Novartis: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; BiolineRx: Consultancy; Biopath Holdings: Consultancy, Honoraria; Takeda: Consultancy, Research Funding. Kantarjian:AbbVie: Honoraria, Research Funding; Cyclacel: Research Funding; Pfizer: Honoraria, Research Funding; Astex: Research Funding; Agios: Honoraria, Research Funding; Jazz Pharma: Research Funding; Daiichi-Sankyo: Research Funding; Novartis: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Immunogen: Research Funding; Takeda: Honoraria; BMS: Research Funding; Ariad: Research Funding; Amgen: Honoraria, Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding.

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