scholarly journals A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

2021 ◽  
Vol 11 (12) ◽  
pp. 5576
Author(s):  
Angier Allen ◽  
Anna Siefkas ◽  
Emily Pellegrini ◽  
Hoyt Burdick ◽  
Gina Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Ischemic Stroke ◽  
Clinical Decision Making ◽  
Simulated Data ◽  
Clinical Decision ◽  
International Classification Of Diseases ◽  
Real Patient ◽  
Digital Twin ◽  
Twin Model

Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUCadversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.

scholarly journals Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Michael L. Birnbaum ◽  
Raquel Norel ◽  
Anna Van Meter ◽  
Asra F. Ali ◽  
Elizabeth Arenare ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Negative Emotion ◽  
Clinical Information ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Schizophrenia Spectrum Disorders ◽  
Real Patient ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

AbstractPrior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.

Machine learning in clinical decision making

Med ◽  
2021 ◽  
Author(s):  
Lorenz Adlung ◽  
Yotam Cohen ◽  
Uria Mor ◽  
Eran Elinav
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imogen Schofield ◽  
David C. Brodbelt ◽  
Noel Kennedy ◽  
Stijn J. M. Niessen ◽  
David B. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cushing’S Syndrome ◽  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Cushing's Syndrome ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

scholarly journals ALK inhibitors, resistance development, clinical trials

2018 ◽  
Vol 25 ◽  
pp. 59 ◽  
Author(s):  
J.M. Rothenstein ◽  
N. Chooback
Keyword(s):  
Clinical Trials ◽  
Patient Outcomes ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Current Treatment ◽  
Resistance Development ◽  
Ongoing Research ◽  
Alk Positive ◽  
Emergence Of Resistance ◽  
Key Questions

The treatment of advanced non-small-cell lung cancer (nsclc) has undergone a paradigm shift since the early 2000s. The identification of molecular subtypes of the disease, based on oncogenic drivers, has led to the development of personalized medicine and the ability to deliver molecularly targeted therapies to patients. In the 10 years that have elapsed since the discovery of the ALK gene in a patient with nsclc, several active drugs have moved rapidly from bench to bedside, and multiple others are currently in clinical trials. Those developments have led to important improvements in patient outcomes, while simultaneously raising key questions about the optimal treatment for ALK-positive nsclc. The inevitable emergence of resistance to alk-directed therapy is central to ongoing research and daily clinical practice for affected patients. In the present review, we highlight the current treatment landscape, the available and emerging clinical trials, and the evolving clinical decision-making in ALK-positive nsclc, with a focus on Canadian practice.

Beyond the Development of Health-Related Quality-of-Life (HRQOL) Measures: A Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials—Does HRQOL Evaluation in Prostate Cancer Research Inform Clinical Decision Making?

2003 ◽  
Vol 21 (18) ◽  
pp. 3502-3511 ◽  
Author(s):  
Fabio Efficace ◽  
Andrew Bottomley ◽  
David Osoba ◽  
Carolyn Gotay ◽  
Henning Flechtner ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Clinical Trials ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cancer Clinical Trials ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related

Purpose: The aim of this study was to evaluate whether the inclusion of health-related quality of life (HRQOL), as a part of the trial design in a randomized controlled trial (RCT) setting, has supported clinical decision making for the planning of future medical treatments in prostate cancer. Materials and Methods: A minimum standard checklist for evaluating HRQOL outcomes in cancer clinical trials was devised to assess the quality of the HRQOL reporting and to classify the studies on the grounds of their robustness. It comprises 11 key HRQOL issues grouped into four broader sections: conceptual, measurement, methodology, and interpretation. Relevant studies were identified in a number of databases, including MEDLINE and the Cochrane Controlled Trials Register. Both their HRQOL and traditional clinical reported outcomes were systematically analyzed to evaluate their consistency and their relevance for supporting clinical decision making. Results: Although 54% of the identified studies did not show any differences in traditional clinical end points between treatment arms and 17% showed a difference in overall survival, 74% of the studies showed some difference in terms of HRQOL outcomes. One third of the RCTs provided a comprehensive picture of the whole treatment including HRQOL outcomes to support their conclusions. Conclusion: A minimum set of criteria for assessing the reported outcomes in cancer clinical trials is necessary to make informed decisions in clinical practice. Using a checklist developed for this study, it was found that HRQOL is a valuable source of information in RCTs of treatment in metastatic prostate cancer.

scholarly journals Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elza Rechtman ◽  
Paul Curtin ◽  
Esmeralda Navarro ◽  
Sharon Nirenberg ◽  
Megan K. Horton
Keyword(s):  
Machine Learning ◽  
New York ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Gradient Boosting ◽  
Hospital System ◽  
Response Strategies

AbstractTimely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

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