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594 results for “Virtual patient”
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Aggregated Virtual Patient Model Dataset
<p>The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc.</p> <p>The dataset contains several medical features which are used by clinicians to assess the overall state of the older people.</p> <p>The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status.</p> <p>A list of the recorded clinical parameters and their description is shown below:</p> <p>- <strong> part_id</strong>: The user ID, which should be a 4-digit number</p> <p>-<strong> q_date</strong>: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z)</p> <p>-<strong> clinical_visit</strong>: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to</p> <p>-<strong> fried</strong>: Ordinal categorization of frailty level according to Fried operational definition of frailty</p> <p>-<strong> hospitalization_one_year</strong>: Number of nonscheduled hospitalizations in the last year</p> <p>- <strong>hospitalization_three_years</strong>: Number of nonscheduled hospitalizations in the last three years</p> <p>- <strong>ortho_hypotension</strong>: Presence of orthostatic hypotension</p> <p>- <strong>vision</strong>: Visual difficulty (qualitative ordinal evaluation)</p> <p>- <strong>audition</strong>: Hearing difficulty (qualitative ordinal evaluation)</p> <p>- <strong>weight_loss</strong>: Unintentional weight loss >4.5 kg in the past year (categorical answer)</p> <p>- <strong>exhaustion_score</strong>: Self-reported exhaustion (categorical answer)</p> <p>- <strong>raise_chair_time</strong>: Time in seconds to perform a lower limb strength clinical test</p> <p>- <strong>balance_single</strong>: Single foot station (Balance) (categorical answer)</p> <p>- <strong>gait_get_up</strong>: Time in seconds to perform the 3meters’ Timed Get Up And Go Test</p> <p>- <strong>gait_speed_4m</strong>: Speed for 4 meters’ straight walk</p> <p>- <strong>gait_optional_binary</strong>: Gait optional evaluation (qualitative evaluation by the investigator)</p> <p>- <strong>gait_speed_slower</strong>: Slowed walking speed (categorical answer)</p> <p>- <strong>grip_strength_abnormal</strong>: Grip strength outside the norms (categorical answer)</p> <p>- <strong>low_physical_activity</strong>: Low physical activity (categorical answer)</p> <p>- <strong>falls_one_year</strong>: Number of falls in the last year</p> <p>- <strong>fractures_three_years</strong>: Number of fractures during the last 3 years</p> <p>- <strong>fried_clinician</strong>: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire)</p> <p>- <strong>bmi_score</strong>: Body Mass Index (in Kg/m²)</p> <p>- <strong>bmi_body_fat</strong>: Body Fat (%)</p> <p>- <strong>waist</strong>: Waist circumference (in cm)</p> <p>- <strong>lean_body_mass</strong>: Lean Body Mass (%)</p> <p>- <strong>screening_score</strong>: Mini Nutritional Assessment (MNA) screening score</p> <p>- <strong>cognitive_total_score</strong>: Montreal Cognitive Assessment (MoCA) test score</p> <p>- <strong>memory_complain</strong>: Memory complain (categorical answer)</p> <p>- <strong>mmse_total_score</strong>: Folstein Mini-Mental State Exam score</p> <p>- <strong>sleep</strong>: Reported sleeping problems (qualitative ordinal evaluation)</p> <p>- <strong>depression_total_score</strong>: 15-item Geriatric Depression Scale (GDS-15)</p> <p>- <strong>anxiety_perception</strong>: Anxiety auto-evaluation (visual analogue scale 0-10)</p> <p>- <strong>living_alone</strong>: Living Conditions (categorical answer)</p> <p>- <strong>leisure_out</strong>: Leisure activities (number of leisure activities per week)</p> <p>- <strong>leisure_club</strong>: Membership of a club (categorical answer)</p> <p>- <strong>social_visits</strong>: Number of visits and social interactions per week</p> <p>- <strong>social_calls</strong>: Number of telephone calls exchanged per week</p> <p>- <strong>social_phone</strong>: Approximate time spent on phone per week</p> <p>- <strong>social_skype</strong>: Approximate time spent on videoconference per week</p> <p>- <strong>social_text</strong>: Number of written messages (SMS and emails) sent by the participant per week</p> <p>- <strong>house_suitable_participant</strong>: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer)</p> <p>- <strong>house_suitable_professional</strong>: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer)</p> <p>- <strong>stairs_number</strong>: Number of steps to access house (without possibility to use elevator)</p> <p>- <strong>life_quality</strong>: Quality of life self-rating (visual analogue scale 0-10)</p> <p>- <strong>health_rate</strong>: Self-rated health status (qualitative ordinal evaluation)</p> <p>- <strong>health_rate_comparison</strong>: Self-assessed change since last year (qualitative ordinal evaluation)</p> <p>- <strong>pain_perception</strong>: Self-rated pain (visual analogue scale 0-10)</p> <p>- <strong>activity_regular</strong>: Regular physical activity (ordinal answer)</p> <p>- <strong>smoking</strong>: Smoking (categorical answer)</p> <p>- <strong>alcohol_units</strong>: Alcohol Use (average alcohol units consumption per week)</p> <p>- <strong>katz_index</strong>: Katz Index of ADL score</p> <p>- <strong>iadl_grade</strong>: Instrumental Activities of Daily Living score</p> <p>- <strong>comorbidities_count</strong>: Number of comorbidities</p> <p>- <strong>comorbidities_significant_count</strong>: Number of comorbidities which affect significantly the person’s functional status</p> <p>- <strong>medication_count</strong>: Number of active substances taken on a regular basis</p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
SILICOFCM_Virtual patients repository_clinical features
<p>Generation of a virtual dataset based on one of the integrated virtual generators into the SILICOFCM platform. The dataset contains 1000 virtually generated patients with 20 virtually generated clinical features by the real dataset of SILICOFCM: age, sex, NYHA class, systolic pressure, diastolic pressure, syncope, heart murmurs, left ventricular ejection fraction (LVEF or EFLV), left ventricular internal dimension at end-diastole (LVIDd), left ventricular internal dimension at end systole (LVIDs), intraventricular septal thickness at end-diastole (IVSd), posterior wall thickness at end-diastole (PLWd), endsystolic volume of left ventricle (SVLV), left ventricular outflow tract maximum pressure gradient (maxLVOTPG), Doppler E/E’ ratio (EE), body to mass index (BMI), left atrium size (LA), Alanine aminotransferase (ALT), aorta size (AO), and aortic valve (AV). The dataset can be utilized for Machine Learning modeling.</p>
In silico database of ~500 000 virtual patients with diverse cardiovascular disease
<p>The presented dataset contains a large virtual cohort of more than 50'000 heart failure patients characterized by realistic traces of volumes, pressures, flows, and regional mechanics. We used the well-established CircAdapt model <a href="https://www.circadapt.org/">(https://www.circadapt.org/</a> , <a href="http://framework.circadapt.org/">http://framework.circadapt.org/</a>) of the human heart and circulation to simulate a large cohort of virtual patients, covering a wide range of HF-related disease heterogeneity and severity. <br>In the construction of virtual patients, generating a variety of parameter sets for the initial population is essential and can be accomplished using various sampling techniques. In our investigation, we utilized the Sobol-low discrepancy sequence to ensure uniformity across the high-dimensional parameter space. For a more detailed explanation please refer to the document <em>"MARCIUS_deliverable_D1.5_VirtualDatabase.pdf"</em></p> <p><strong>Acknowledgments:</strong> This work was supported by the European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement No. 86074, <strong>"MARCIUS - MARie Curie Intelligent UltraSound"(<a href="https://www.marcius-project.com/">https://www.marcius-project.com/</a>)</strong>. MARCIUS rationale was to develop a comprehensive in silico simulation platform comprising both the generation of virtual patients (<em>presented dataset</em>) and their associated realistic image data. Such approach would make it possible to learn the most relevant patterns within a wide representative set of patients to lead the training of ML-based image processing algorithms in order to analyze real-world clinical data.</p> <p> </p>
Virtual Pulmonary Rehabilitation Program for COPD Patients; a Pilot Study
ClinicalTrials.gov study NCT06915740. IPD Sharing: NO. Countries: 1. Publications: 3.
Pilot of Virtual Health Coaching Utilizing Lifestyle for Under-Resourced Patients With Type II Diabetes
ClinicalTrials.gov study NCT04872647. IPD Sharing: NO. Countries: 1. Publications: 4.
Immersive Virtual Reality (VR) at the Time of Clinical Evaluation to Improve Psychological Distress and Anxiety in Primary Brain Tumor (PBT) Patients
ClinicalTrials.gov study NCT04301089. IPD Sharing: YES. Countries: 1. Publications: 7.
Role of Virtual Reality (VR) in Patients With Sickle Cell Disease (SCD)
ClinicalTrials.gov study NCT03387033. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
The Comparison of Virtual and Real Boxing Training in Hemiparetic Stroke Patients
ClinicalTrials.gov study NCT03651479. IPD Sharing: NO. Countries: 1. Publications: 2.
Exercises for Early Mobilization of Patients Undergoing Open Heart Surgery With Virtual Reality Application
ClinicalTrials.gov study NCT06918600. IPD Sharing: YES. Countries: 1. Publications: 6.
Effectiveness of Immersive Virtual Reality on Biomarkers and Clinical and Clinimetric Variables in Patients with Persistent Shoulder Pain
ClinicalTrials.gov study NCT06795464. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Immersive Virtual Reality and Physical Exercise on Cognitive and Functional Performance in Hospitalized Older Patients
ClinicalTrials.gov study NCT06340282. IPD Sharing: YES. Countries: 1. Publications: 2.
Using Virtual Reality During PICC and PICC-PORT Placement in Cancer Patients
ClinicalTrials.gov study NCT07384741. IPD Sharing: NO. Countries: 1. Publications: 4.
Virtual Continuity and Its Impact on Complex Hospitalized Patients' Care
ClinicalTrials.gov study NCT01397253. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Virtual Lifestyle Management: Prevention Through the UPMC Patient Portal
ClinicalTrials.gov study NCT00409786. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Smartphone-Based Virtual Reality Relaxation (S-VR) in Cancer Patients Undergoing Chemotherapy
ClinicalTrials.gov study NCT05756465. IPD Sharing: NO. Countries: 1. Publications: 6.
Effects of Virtual Reality Rehabilitation in Patients With Total Knee Arthroplasty
ClinicalTrials.gov study NCT02413996. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Implementing a Virtual Tobacco Treatment for Cancer Patients in Community Oncology Practices
ClinicalTrials.gov study NCT03808818. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Virtual Reality Applications on Upper Extremity Functions in Patients With Duchenne Muscular Dystrophy
ClinicalTrials.gov study NCT07039799. IPD Sharing: NO. Countries: 0. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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