Abstract TMP44: Personalizing Rehabilitation for Stroke Survivors- A Big Data Approach

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Swathi Kiran ◽  
Jason Godlove ◽  
Mahendra Advani ◽  
Veera Anantha
Keyword(s):  
Clinical Decision Making ◽  
Large Data ◽  
Clinical Decision ◽  
Individual Variability ◽  
Data Sets ◽  
Stroke Patients ◽  
Large Sets ◽  
Number Of Patients ◽  
Severity Scores ◽  
Evidenced Based

Introduction: Advances in connected health delivery provides a unique opportunity to maximize intervention effectiveness for stroke patients. It has also helped collect large sets of data to facilitate clinical decision making. This vastly insightful data is used to personalize neurorehabilitation where the evidence for gains in chronic stroke patients is weak. Methods: Over a span of 2 years (2013-2015), data was anonymously aggregated and analyzed from over 2,500 patients with post-stroke aphasia. Data was collected using a mobile therapy platform, Constant Therapy, which has 60 evidenced-based language and cognitive therapy tasks. The program was used by patients in the home and clinic under the guidance of a clinician, or under their own volition if not regularly seeing a clinician. This program dynamically adapted to each patient’s progress. Patients who completed between 3 and 1000 treatment sessions were analyzed to determine which tasks showed statistically significant changes. These data were compared with control patients who completed tests at two separate time points but with no intervening treatment. Results: Despite the older demographic of patients (median age = 64 yrs), they performed an average of over 20 minutes on home therapy every day. The analyses take into account the number of patients who completed a specific task and show a significant change (all p <.05) for accuracy or latency. For example, the 2-step Auditory Command task (which requires a user to follow 2-step directions) showed a 12 point gain in accuracy and 39% improvement in processing speed in 1,200 patients. The results also show that patients with higher initial severity scores showed significant gains in accuracy, and reach similar post-treatment accuracy to those with lower severity scores, provided that they received an appropriate dosage of therapy. In contrast, control patients showed minimal gains on tasks that were assigned. Conclusion: These results show that all patients, including the most severe, can make progress in their rehabilitation when treatment is individualized for them. Analysis of large data sets can be used to inform rehabilitation by highlighting therapies that are effective while accounting for etiology and individual variability.

scholarly journals clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

2021 ◽  
Author(s):  
Sebastiaan Valkiers ◽  
Max Van Houcke ◽  
Kris Laukens ◽  
Pieter Meysman
Keyword(s):  
T Cell ◽  
Large Data ◽  
Cell Receptor ◽  
Amino Acid Sequences ◽  
Large Data Sets ◽  
Data Sets ◽  
Clustering Methods ◽  
Link Type ◽  
Large Sets ◽  
Similar Accuracy

The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large data sets exceeding 1 million sequences. To account for this limitation, we developed clusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences. Benchmarking comparisons revealed similar accuracy of clusTCR with other TCR clustering methods. clusTCR offers a drastic improvement in clustering speed, which allows clustering of millions of TCR sequences in just a few minutes through efficient similarity searching and sequence hashing.clusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR).

scholarly journals ACADEMIA’S ROLE TO DRIVE CHANGE IN THE ORTHOTICS AND PROSTHETICS PROFESSION

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Géza Kogler ◽  
Christopher Hovorka
Keyword(s):  
Technology Use ◽  
Clinical Training ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Care ◽  
Clinical Decision ◽  
The United States ◽  
Academic Program ◽  
Number Of Patients ◽  
Curricular Emphasis

This position paper outlines the important role of academia in shaping the orthotics and prosthetics (O&P) profession and preparing for its future. In the United States, most healthcare professions including O&P are under intense pressure to provide cost effective treatments and quantifiable health outcomes. Pivotal changes are needed in the way O&P services are provided to remain competitive. This will require the integration of new technologies and data driven processes that have the potential to streamline workflows, reduce errors and inform new methods of clinical care and device manufacturing. Academia can lead this change, starting with a restructuring in academic program curricula that will enable the next generation of professionals to cope with multiple demands such as the provision of services for an increasing number of patients by a relatively small workforce of certified practitioners delivering these services at a reduced cost, with the expectation of significant, meaningful, and measurable value. Key curricular changes will require replacing traditional labor-intensive and inefficient fabrication methods with the integration of newer technologies (i.e., digital shape capture, digital modeling/rectification and additive manufacturing). Improving manufacturing efficiencies will allow greater curricular emphasis on clinical training and education – an area that has traditionally been underemphasized. Providing more curricular emphasis on holistic patient care approaches that utilize systematic and evidence-based methods in patient assessment, treatment planning, dosage of O&P technology use, and measurement of patient outcomes is imminent. Strengthening O&P professionals’ clinical decision-making skills and decreasing labor-intensive technical fabrication aspects of the curriculum will be critical in moving toward a digital and technology-centric practice model that will enable future practitioners to adapt and survive. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36673/28349 How To Cite: Kogler GF, Hovorka CF. Academia’s role to drive change in the orthotics and prosthetics profession. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.21. https://doi.org/10.33137/cpoj.v4i2.36673 Corresponding Author: Géza F. KoglerOrthotics and Prosthetics Unit, Kennesaw State University.E-Mail: [email protected] ID: https://orcid.org/0000-0003-0212-5520

scholarly journals Deep Learning: A New Horizon for Personalized Treatment of Depression?

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
David Benrimoh ◽  
Robert Fratila ◽  
Sonia Israel ◽  
Kelly Perlman
Keyword(s):  
Deep Learning ◽  
Decision Aid ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Personalized Care ◽  
Treatment Of Depression ◽  
Number Of Patients ◽  
Treatment Plans ◽  
Patient Responses ◽  
Deep Learning Model

Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.

scholarly journals A “Lymphocyte MicroRNA Signature” as Predictive Biomarker of Immunotherapy Response and Plasma PD-1/PD-L1 Expression Levels in Patients with Metastatic Renal Cell Carcinoma: Pointing towards Epigenetic Reprogramming

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3396
Author(s):  
Lorena Incorvaia ◽  
Daniele Fanale ◽  
Giuseppe Badalamenti ◽  
Chiara Brando ◽  
Marco Bono ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Clinical Decision ◽  
Mirna Expression Profile ◽  
Immune Checkpoints ◽  
Cancer Evolution ◽  
Specific Subset ◽  
Number Of Patients

Introduction of checkpoint inhibitors resulted in durable responses and improvements in overall survival in advanced RCC patients, but the treatment efficacy is widely variable, and a considerable number of patients are resistant to PD-1/PD-L1 inhibition. This variability of clinical response makes necessary the discovery of predictive biomarkers for patient selection. Previous findings showed that the epigenetic modifications, including an extensive microRNA-mediated regulation of tumor suppressor genes, are key features of RCC. Based on this biological background, we hypothesized that a miRNA expression profile directly identified in the peripheral lymphocytes of the patients before and after the nivolumab administration could represent a step toward a real-time monitoring of the dynamic changes during cancer evolution and treatment. Interestingly, we found a specific subset of miRNAs, called “lymphocyte miRNA signature”, specifically induced in long-responder patients (CR, PR, or SD to nivolumab >18 months). Focusing on the clinical translational potential of miRNAs in controlling the expression of immune checkpoints, we identified the association between the plasma levels of soluble PD-1/PD-L1 and expression of some lymphocyte miRNAs. These findings could help the development of novel dynamic predictive biomarkers urgently needed to predict the potential response to immunotherapy and to guide clinical decision-making in RCC patients.

Complementing sparse vascular imaging data by physiological adaptation rules

2020 ◽  
Author(s):  
Maarten H.G. Heusinkveld ◽  
Robert J. Holtackers ◽  
Bouke P. Adriaans ◽  
Jos Op't Roodt ◽  
Theo Arts ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Mechanical Load ◽  
Wall Stress ◽  
Clinical Decision ◽  
Physiological Adaptation ◽  
Patient Specific ◽  
Data Sets ◽  
Imaging Data ◽  
Artery Segment ◽  
Flow Waveforms

Introduction:Mathematical modeling of pressure and flow waveforms in blood vessels using pulse wave propagation (PWP)-models has tremendous potential to support clinical decision-making. For a personalized model outcome, measurements of all modeled vessel radii and wall thicknesses are required. In clinical practice, however, data sets are often incomplete. To overcome this problem, we hypothesized that the adaptive capacity of vessels in response to mechanical load could be utilized to fill in the gaps of incomplete patient-specific data sets. Methods:We implemented homeostatic feedback loops in a validated PWP model to allow adaptation of vessel geometry to maintain physiological values of wall stress and wall shear stress. To evaluate our approach, we gathered vascular MRI and ultrasound data sets of wall thicknesses and radii of central and arm arterial segments of ten healthy subjects. Reference models (i.e. termed RefModel, n=10) were simulated using complete data, whereas adapted models (AdaptModel, n=10) used data of one carotid artery segment only while the remaining geometries in this model were estimated using adaptation. We evaluated agreement between RefModel and AdaptModel geometries, as well as between pressure and flow waveforms of both models. Results:Limits of agreement (bias±2SD of difference) between AdaptModel and RefModel radii and wall thicknesses were 0.2±2.6 mm and -140±557 μm, respectively. Pressure and flow waveform characteristics of the AdaptModel better resembled those of the RefModels as compared to the model in which the vessels were not adapted.Conclusions:Our adaptation-based PWP-model enables personalization of vascular geometries even when not all required data is available.

Switching and Combining Antipsychotics

CNS Spectrums ◽  
2010 ◽  
Vol 15 (S6) ◽  
pp. 8-11 ◽  
Author(s):  
Christoph U. Correll
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Individual Variability ◽  
The Body ◽  
Striatal Dopamine ◽  
Caloric Content ◽  
Pharmacokinetic Profiles ◽  
Positron Emission

Pharmacologic knowledge can inform clinical decision-making, particularly the dosing and switching decisions made with antipsychotics. Of relevance are the pharmacokinetic (what does the body do to the drug) and the pharmacodynamic (what does the drug do to the body) properties of antipsychotics.The goal of antipsychotic dosing is to achieve sufficient dopa-mine blockade in areas where dopamine excess can lead to psychosis, mania, or aggression. Using positron emission topography, one investigation showed that response rates were considerably higher in patients who achieved >65% striatal dopamine blockade. Conversely, striatal dopamine blockade >80% predicted the emergence of extrapyramidal symptoms (EPS) or akathisia.There is, however, considerable intra-individual variability in achieving the desired 60% to 80% striatal dopamine blockade. Such variability is likely due to inter-individual differences in the absorption, distribution, metabolism and elimination of medications. At the same time, antipsychotics themselves differ in their general pharmacokinetic profiles. For example, ziprasidone absorption is ~50% less when ingested on an empty stomach than when taken with a meal; the degree of absorption depends on the caloric content, while the fat content is not relevant.

Using imaging to identify psychogenic parkinsonism before deep brain stimulation surgery

2012 ◽  
Vol 116 (1) ◽  
pp. 114-118 ◽  
Author(s):  
Michael H. Pourfar ◽  
Chris C. Tang ◽  
Alon Y. Mogilner ◽  
Vijay Dhawan ◽  
David Eidelberg
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Metabolic Networks ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Number Of Patients ◽  
Normal Expression ◽  
Deep Brain Stimulation Surgery ◽  
Deep Brain

The frequency with which patients with atypical parkinsonism and advanced motor symptoms undergo deep brain stimulation (DBS) procedures is unknown. However, the potential exposure of these patients to unnecessary surgical risks makes their identification critical. As many as 15% of patients enrolled in recent early Parkinson disease (PD) trials have been found to lack evidence of a dopaminergic deficit following PET or SPECT imaging. This suggests that a number of patients with parkinsonism who are referred for DBS may not have idiopathic PD. The authors report on 2 patients with probable psychogenic parkinsonism who presented for DBS surgery. They found that both patients had normal caudate and putamen [18F]-fluorodopa uptake on PET imaging, along with normal expression of specific disease-related metabolic networks for PD and multiple system atrophy, a common form of atypical neurodegenerative parkinsonism. The clinical and PET findings in these patients highlight the role of functional imaging in assisting clinical decision making when the diagnosis is uncertain.

scholarly journals Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

2020 ◽  
Author(s):  
Michael P. McRae ◽  
Glennon W. Simmons ◽  
Nicolaos J. Christodoulides ◽  
Zhibing Lu ◽  
Stella K. Kang ◽  
...  
Keyword(s):  
Decision Support ◽  
Disease Severity ◽  
Clinical Decision Support ◽  
Severity Score ◽  
Troponin I ◽  
Point Of Care ◽  
Clinical Decision ◽  
Adverse Outcomes ◽  
Number Of Patients ◽  
Severity Scores

AbstractSARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

scholarly journals Knowledge transfer to enhance the performance of deep learning models for automated classification of B-cell neoplasms

2021 ◽  
Author(s):  
Nanditha Mallesh ◽  
Max Zhao ◽  
Lisa Meintker ◽  
Alexander Höllein ◽  
Franz Elsner ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Knowledge Transfer ◽  
Clinical Decision Making ◽  
Dimensional Space ◽  
Clinical Decision ◽  
Data Sets ◽  
Automated Classification ◽  
Learning Models ◽  
Hematological Disorders

AbstractMulti-parameter flow cytometry (MFC) is a cornerstone in clinical decision making for hematological disorders such as leukemia or lymphoma. MFC data analysis requires trained experts to manually gate cell populations of interest, which is time-consuming and subjective. Manual gating is often limited to a two-dimensional space. In recent years, deep learning models have been developed to analyze the data in high-dimensional space and are highly accurate. Such models have been used successfully in histology, cytopathology, image flow cytometry, and conventional MFC analysis. However, current AI models used for subtype classification based on MFC data are limited to the antibody (flow cytometry) panel they were trained on. Thus, a key challenge in deploying AI models into routine diagnostics is the robustness and adaptability of such models. In this study, we present a workflow to extend our previous model to four additional MFC panels. We employ knowledge transfer to adapt the model to smaller data sets. We trained models for each of the data sets by transferring the features learned from our base model. With our workflow, we could increase the model’s overall performance and more prominently, increase the learning rate for very small training sizes.

Stratification of patients by tumor type using molecular profiling in real-world data.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19262-e19262
Author(s):  
Alyssa Antonopoulos ◽  
Elizabeth Eldridge ◽  
George Managadze ◽  
Elia Stupka ◽  
Hakim Lakhani ◽  
...  
Keyword(s):  
Real World ◽  
Clinical Decision Making ◽  
Single Gene ◽  
Data Access ◽  
Clinical Decision ◽  
Molecular Data ◽  
Tumor Type ◽  
Real World Data ◽  
Number Of Patients ◽  
Biomarker Data

e19262 Background: While Next-Generation Sequencing (NGS) tests become increasingly more common for diagnosis, molecular characterization, and treatment, a significant amount of molecular data derives from single-gene or analyte tests. Single gene test information is stored in disparate sources including electronic medical record (EMR) and data access for clinical use remains a challenge. A solution that harmonizes biomarker data beyond standard NGS-centric data and linked to rich clinical data is required for the complete patient picture. Methods: Health Catalyst’s extended real-world database, Touchstone includes a molecular data mart that integrates data from provider and life sciences proprietary NGS panels, Laboratory Information Systems, and other repositories. A portion of the data is derived from single-gene tests documented in the EMR. Biomarker data from EMRs was extracted from six health systems via a proprietary pipeline for extracting biomarker data. The algorithm relies on a curated ontology for molecular terms and publicly available terminologies for human genetics. Minor transformations extract pertinent variant information where available to harmonize with NGS-level data. Results: Over 44 thousand molecular labs from over 24 thousand patients were identified with this method. The oncology classes for which molecular data was identified in the greatest number of patients include skin, hematological, breast, digestive, and lung cancers (Table). PRTN3, EGFR, BRAF, JAK2, ERBB2, and KRAS are among the most commonly tested genes. Conclusions: Integrated real-world clinical and biomarker data from single gene tests can inform clinical decision-making and support clinical trial recruitment across a broader set of patient population. [Table: see text]

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