View clinical trials related to Machine Learning.
Filter by:Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disturbances in communication, poor social skills, and aberrant behaviors. Particularly detrimental are the presence of restricted and repetitive stereotyped behaviors and uncontrollable temper outbursts over trivial changes in the environment, which often cause emotional stress for the children, their families, schools and neighborhood communities. Fundamental to these cognitive and behavioral problems is the disordered cortical connectivity and resultant executive dysfunction that underpin the use of effective strategies to integrate information across contexts. Brain connectivity problems affect the rate at which information travels across the brain. Slow processing speed relates to a reduced capacity of executive function to recall and formulate thoughts and actions automatically, with the result that autistic children with poor processing speed have great difficulty learning or perceiving relationships across multiple experiences. In consequence, these children compensate for the impaired ability to integrate information from the environment by memorizing visual details or individual rules from each situation. This explains why children with autism tend to follow routines in precise detail and show great distress over seemingly trivial changes in the environment. To date, there is no known cure for ASD, and the disorder remains a highly disabling condition. Recently, a non-invasive brain stimulation technique, transcranial direct current Stimulation (tDCS) has shown great promise as a potentially effective and costeffective tool for reducing core symptoms such as anxiety, aggression, impulsivity, and inattention in patients with autism. This technique has been shown to modify behavior by inducing changes in cortical excitability and enhancing connectivity between the targeted brain areas. However, not all ASD patients respond to this intervention the same way and predicting the behavioral impact of tDCS in patients with ASD remains a clinical challenge. This proposed study thus aims to address these challenges by determining whether resting-state EEG and clinical data at baseline can be used to differentiate responders from non-responders to tDCS treatment. Findings from the study will provide new guidance for designing intervention programs for individuals with ASD.
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The researcher here developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes. The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The investigatorshere developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes. The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.
Drug-related problems in newborn babies have been reported with a rate of 4-30%. It is estimated that the higher rates of these problems in hospitalized children under the age of two are related to the variety of drugs used and the differences in the age, weight and diagnosis of the patients. In this context, with the clinical parameters and demographic data obtained in the first 24 hours of the patients hospitalized in the neonatal intensive care unit, machine learning algorithms are used to predict the risks that may arise from possible drug-related problems (prescribing and administration errors, side effects and drug-drug interactions) that may occur during hospitalization. The algorithm, which will be created by modeling with a high number of big data pool, is planned to be transformed into a clinical decision support system software that can be used easily in clinical practice with online and mobile applications. By processing the data of the patients to be included in the model, it is aimed to prevent and manage drug-related problems before they occur, as well as to provide cost-effective medşcation treatment to patients hospitalized in the neonatal intensive care unit, together with a reduction in the risk of drug-related mortality and morbidity.
To study the effects of the use of a mobile application plus recommendations based on a Mediterranean diet on the intake of micronutrients from natural sources (not drugs) on health indicators, cardiovascular parameters (blood pressure...), physical condition and body composition in a Spanish adult population.
Background: Insufficient physical activity is one of the leading risk factors of death worldwide. Behavioral treatments delivered via smartphone apps, hold great promise for helping people engage in healthy behaviors including becoming more physically active. However, similar to 'face-to-face' treatments, effects typically do not seem to be sustained over longer periods of time. Methods: the investigators developed a smartphone application that uses different types of motivational and feedback text-messaging to motivate individuals to increase physical activity. Here, participants are randomized to either receive messages by a uniform random distribution (n=50), or chosen by a reinforcement learning algorithm (n=50), which learns from daily participant data to personalize the frequency and type of motivation of messages. Objectives: In the current study, the investigators examine this application in undergraduate and graduate students at the University of California, Berkeley. The investigators compare whether participants in the uniform random or adaptive group have higher increases in steps during the study. The investigators also examine the effect of the different types of messages on step counts. Further the investigators assess the influence of patient characteristics, such as socio-demographic, psychological questionnaire scores and baseline physical activity on the effect of the adaptive arm and effectiveness of the messages. Finally, the investigators assess participant qualitative feedback on the text-messaging program, through feedback provided via questionnaires, text-message and phone interviews.
The investigators aimed to combine the image of near-infrared vision and machine learning method to evaluate the microcirculatory status of critical ill patients.
To develop and validate a machine-learning model based on clinical, laboratory, and radiological characteristics alone or combination of COVID-19 patients to facilitate risk Assessment before and after symptoms and triage (home, hospitalization inward or ICU).
Patients with Chronic Obstructive Pulmonary Disease (COPD) who are admitted to hospital are at high risk of readmission. While therapies have improved and there are evidence-based guidelines to reduce readmissions, there are significant challenges to implementation including 1) identifying all patients with COPD early in admission to ensure evidence-based, high value care is provided and 2) identifying those who are at high risk of readmission in order to effectively target resources. Using machine learning and natural language processing, we want to develop models to 1) identify all patients with COPD exacerbations admitted to hospital and 2) stratify them to distinguish those who are at high risk of readmission b) How will you undertake your work? From Toronto hospitals, we will develop a very large dataset of patient admissions for all medical conditions including exacerbations of COPD from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with COPD exacerbation and identify those patients who will be at high risk of readmission within 30 days. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.
Intraoperative hypotension occurs often and is associated with adverse patient outcomes such as stroke, myocardial infarction and renal injury. The aim of this study was to test the accuracy of a physiology-based machine-learning algorithm using continuous non-invasive measurement of the blood pressure waveform with the Nexfin® finger cuff during surgery.