Volv Global SA’s Post

📢Publication news! A new study, co-authored by Health Data Insight CIC Principal Health Data Analyst Craig Knott, titled "Factors associated with receipt of systemic anticancer treatment for locally advanced or metastatic urothelial carcinoma in England: a population-based study," has been published in Urologic Oncology: Seminars and Original Investigations.  The study explored anticancer treatment patterns for patients diagnosed with advanced urothelial carcinoma in England between 2013 and 2019, using data collected by the NHS England National Disease Registration Service. Key findings:  - 69% of patients did not receive treatment (within the range reported across other real-world studies of European populations)  - Female sex, older age, poor performance status, greater comorbidities, and living in income-deprived areas were linked to lower treatment rates.  - 91% of treated patients received platinum-based chemotherapy  - Although advanced urothelial carcinoma has a poor prognosis, median overall survival was significantly different between treated (19.9 months) and untreated (5.8 months) patients. The findings highlight a need for further investigation into possible multifactorial reasons for treatment disparities, and whether the introduction of newer therapies will boost treatment rates and improve patient survival.  To read more go to 👉https://lnkd.in/ewEghpDt #UrologicalCancerAwarenessMonth #CancerData

Treating patients with superficial venous thrombosis? Than most likely you work in primary care and often feel doubt on whether or not to treat these patients with anticoagulants. Yes, there is a small yet substantial risk of #deepveinthrombosis and #pulmonaryembolism, but also likely most patients fare well without anticoagulants and 'watchful waiting'. We recently conducting a systematic review and meta-analysis on this topic, published now online in #bmjopen; here is what we found: - Evidence is poor, high variability among studies, poor methods - From what we found, high age, male sex, history of VTE, cancer and absence of varicoses seem to predict clot propagation - Multivariable models of high quality are needed; we are currently underway with such a study in primary care date, so keep tuned! Circulatory Health UMC Utrecht Florien van Royen Frans Rutten Sander van Doorn Maarten van Smeden Want to read more? See link: https://lnkd.in/eSfNgc2A

PALO ALTO, Calif., April 30, 2024 /PRNewswire/ -- Verantos, the global leader in high-validity real-world evidence at scale, today announced the launch of its Inflammatory Bowel Disease Pragmatic Registry, which provides rich and reliable data on ulcerative colitis and Crohn's disease patients, including specific subgroups to life sciences organizations. A pragmatic registry is a condition-specific, high-quality data set generated using artificial intelligence on routinely collected real-world data. By combining unstructured and structured data from electronic health records with linkage to claims and mortality data, Inflammatory Bowel Disease Pragmatic Registry offers an unparalleled view into disease severity, treatments, symptom control, resource utilization, and clinical outcomes variables.

📃Scientific paper: Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction Abstract: Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is us... Continued on ES/IODE ➡️ https://etcse.fr/rfi ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction Abstract: Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is us... Continued on ES/IODE ➡️ https://etcse.fr/rfi ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Diagnosis challenges in rare disease: case example CIDP ⚠️Distinguishing between Guillain-Barré Syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) presents a clinical challenge. ❗️These two disorders belong to a group of inflammatory neuropathies characterized by motor and sensory dysfunction that can result in severe disability. 🔎More research is needed into disease pathophysiology to reveal clinically and functionally relevant disease mechanisms, so doctors can optimize the diagnostic and therapeutic outcomes in GBS and CIDP. ⁉️GBS and CIDP are often mistaken for each other, as their presentation is heterogenous and often non-specific. And the similar features between CIDP and GBS often result in misdiagnosis, overtreatment, treatment failure, and suboptimal outcomes. 💡With novel machine learning techniques, we can develop highly specific algorithms that can detect and differentiate between such difficult to diagnose conditions. And deploy these algorithms in clinical settings, to help HCPs come to the correct diagnosis much sooner for better outcome for patients. 🔑At Volv Global SA we have previously investigated CIDP, and have data on record demonstrating it is possible to detect undiagnosed CIDP patients this way. 🤝If you are interested in driving benefit for CIDP patients, and others suffering from undiagnosed or misdiagnosed inflammatory neuropathies, then please get in touch with me. #inTrigue #inClude #diagnosischallenge #rarediseaseresearch #CIDP #GBS #inflammatoryneuropathies GBS|CIDP Foundation International | Guillain-Barre & Associated Inflammatory Neuropathies | Deutsche GBS CIDP Selbsthilfe | GBS/CIDP Foundation of Canada | Rare Disease Day | References: - Ryner Lai, MBBS | Rare Disease Advisor June 21, 2024 - Volv Global SA

📃Scientific paper: Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction Abstract: Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is us... Continued on ES/IODE ➡️ https://etcse.fr/rfi ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Revolutionizing Care: The Role of Artificial Intelligence in Managing Crohn's Disease Crohn’s disease is a chronic inflammatory bowel disease (IBD) that primarily affects the gastrointestinal tract. While the exact cause remains unidentified, it is believed to result from a combination of genetic, environmental, and immune system factors. The disease is characterized by inflammation that can occur anywhere along the digestive tract, from the mouth to the anus, although it most commonly affects the end of the small intestine and the beginning of the colon. This inflammation can lead to a range of serious complications, making the management of Crohn’s disease particularly complex.... Find out more on: https://lnkd.in/disqetB2 #StudyTime #SmartLearning #QuckLearn #DailyLearning #LearnEveryday #LearningMadeEasy #StudyShorts #EduShorts #LearnWithMe #LearningIsFun #BrainTeasers #OnlineClasses #OnlineLearning #Tech #Technology #TechReview #Gadgets #TechNews #TechTips #TechTalk #TechTrends #TechVideos #Innovation #TechCommunity #TechLife #FutureTech #TechReviews #GadgetReview #ai #deeplearning

📃Scientific paper: Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction Abstract: Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is us... Continued on ES/IODE ➡️ https://etcse.fr/rfi ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction Abstract: Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is us... Continued on ES/IODE ➡️ https://etcse.fr/rfi ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.