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

As we’ve learned on previous episodes of Raise the Line, people dealing with rare diseases usually wait 4-7 years before receiving a diagnosis, during which time their condition can deteriorate significantly. Shortening this “diagnostic odyssey” is the mission of today’s guest, Lukas Lange, and in an interesting twist, he’s doing it by involving the patients themselves.  The company he co-founded, Probably Genetic, has developed a system that starts with rare disease patients, or the parents of children with rare conditions, describing the symptoms involved on a website. “We run algorithms on that data in real time as you're on the website and if the algorithms think that this person might have a specific genetic disease, then we have a whole telemedicine system built in the background where we process that information and you get your test kit within about 48 hours of being on the website,” he explains to host Hillary Acer. Once the at-home test results are processed, a genetic counseling session is conducted via telemedicine. Tune in to find out why Lange believes having this knowledge is powerful for patients even if there may be no treatment available yet for their condition, and how it may be useful down the road with clinical trial recruitment, real world evidence tracking, and even early stage R&D for treatments.   Mentioned in this episode: https://lnkd.in/gGnk4VJf   Listen to today’s episode of Raise the Line: https://lnkd.in/gZfCj6Ea #raisetheline

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

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.

📃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.

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young https://lnkd.in/diWuRv7T Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patien...

📃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.

New analysis from the #LIPIDOGRAM & #LIPIDOGEN2015 studies. Using the hierarchical cluster analysis we identified the #phenotypes that have the highest #mortality risk in #primarycare settings. In primary care, the practice of providing appropriate #prevention strategies to high-risk patients is increasingly recognized as an important #strategy for improving patient care and outcomes, to help optimize the allocation of medical resources and more focus on #patients at higher risk of adverse health #events. However, challenges still remain in implementing #riskstratification in primary care. Our conclusion that primary care physicians should focus on #overweight/#obesity #older patients with multiple comorbidities may seem intuitive, but it was confirmed by the scientific hierarchical cluster analysis, providing a potential technical rationale for the further development of more advanced and nuanced risk stratification implementations. Additionally, our study demonstrates that protein thiol groups (#PSH) partially might contribute to the high-risk mortality in overweight/obesity older patients. Given the potential of PSH in assessing #oxidativestress status and predicting all-cause mortality, further studies are warranted. Interestingly, #young group population overweight/obesity does not significantly increase the risk in the long-term follow-up, thus providing a basis for stratified management of the risk of all-cause #mortality. It was next amazing scientific adventure with this data. Thanks to all co-authors - amazing collaboration with #LCCS. 💪 More soon to come 💪 Yang Chen, Ying Gue, Peter P. Toth, MD, PhD, Marek Gierlotka https://lnkd.in/d7ZbhtUu