McKesson Corporation

Many people agree that forecasting the trends in the stock market is very critical. It gets a lot of attention because people who are right can make a lot of money. Trying to guess what will happen in the stock market is very hard because the info isn't stable, clear, or organized. In this research we will focus in feature engineering technique which improves the performance of a machine learning model. To evaluate the impact of different feature sets, two groups were considered one consisting of traditional (preexisting) features and another composed of derived (engineered) features. The experimental analysis was conducted using stock market datasets from Apple and Yahoo Finance, employing the Support Vector Regression (SVR) algorithm for prediction. The engineered features, including TRX, ADX, ULT, and PPO, were generated from fundamental attributes such as Open, High, Low, Close, and Volume. The results reveal that the derived feature set improved the performance level of the Mean Squared Error (MSE) by 25.60% and the Root Mean Squared Error (RMSE) by 13.75%, Mean absolute error (MSE) by 13.01% when applied on apple stock and 49.57%, 29.00%, 23.94% when applied on yahoo stock as compared to traditional features. Finally we found if we will use extracted feature sets to train and test our proposed model it improves the performance ability than the preexisting features sets.

1580 Background: Patients with multiple cancers are uncommon. In value-based care models such as EOM (Enhancing Oncology Model),episode attribution and financial accountability rely on assigning a cancer type based on the plurality of predefined E&M visits. However, this administrative approach may not accurately represent the cancer for which a patient is receiving active treatment, especially in the presence of concurrent malignancies. Despite this, little is known about how often the E&M based cancer type differs from the treatment-based cancer type. Methods: We analyzed EOM performance period data (July 2023–June 2025) from practices in The US Oncology Network. Injected based initiating episodes were identified using EOM Part B claims that occurred at practices’ sites, then matched to the Electronic Health Record by patient, drug, and initiating date. For oral agents, additional to patient, drug and prescribing provider, order dates within 60 days of the fill date in Part D Claims was used as a proxy in matching process. Cancer type associated with the initiating treatment was extracted from the EHR and then compared with EOM-attributed cancer types. Results: Episodes were predominantly triggered by injection-based chemotherapy with 13,803 initiated in physician office setting, and 5,441 were initiated by oral chemotherapy. Matching rates to an initiating event in EHR differed substantially by route:~ 90% for injection-initiated episodes vs. about 40% for oral-initiated episodes. Among injection-based episodes, 5% of episodes did not have a treatment-based cancer diagnosis in EHR and 4.5% had discordant cancer diagnoses, while oral-based episodes showed markedly higher rates of missing cancer diagnosis at 32%, with 24% mismatched diagnoses. Chronic leukemia and prostate had a higher proportion of oral initiations and correspondingly lower matching rates. Oral-initiated episodes demonstrated particularly low EHR matching in breast, lung, prostate, and small intestine/colorectal cancers. Conclusions: Cancer attribution in VBC models remains challenging, particularly for episodes initiated with oral therapies. As the use of oral agents continues to expand and patients increasingly present with concurrent cancers, attribution difficulties are likely to grow, resulting in greater discordance over time. Incorporating treatment-based cancer type into attribution could improve accuracy and better align assigned cancer types with actual care in value-based oncology models. EHR matching by route of chemotherapy initiation. Cancer Type Injection Episodes (n) Injection Concordance (%) Oral Episodes (n) Oral Concordance (%) Breast Cancer 3836 91.8% 727 23.9% Chronic Leukemia 171 83.6% 1370 50.1% Lung Cancer 3539 93.1% 323 31.3% Lymphoma 1724 84.6% 512 47.9% Multiple Myeloma 1976 93.2% 1211 73.6% Prostate Cancer 558 83.2% 1210 21.4% Small Intestine/Colorectal Cancer 1999 89.1% 88 21.6%

Respiratory diseases in neonates are among the leading causes of neonatal illness and death, particularly in developing countries. Prompt diagnosis and treatment of these conditions are essential. Thermal imaging emerges as a non-invasive and radiation-free diagnostic approach, utilizing temperature variations and thermal symmetry monitoring as tools in medical diagnostics. This study explores the detection of neonatal respiratory abnormalities using artificial intelligence applied to limited thermal imaging data. Convolutional Neural Network (CNN) models, while highly effective for classification tasks, typically require extensive and balanced datasets. However, obtaining sufficient neonatal thermal imaging data can be challenging due to the delicate nature of care in neonatal intensive care units. To address this limitation, the study incorporates a robust deep learning framework alongside various data augmentation techniques to enhance classification outcomes. Neonates with respiratory abnormalities were grouped into one category, while those with cardiovascular and abdominal issues were grouped into another. Results showed that data augmentation, which increased the dataset size by four times, improved classification accuracy from 84.5

A highly sensitive surface plasmon resonance (SPR) sensor is theoretically presented, including Silver (Ag), Tin Diselenide (SnSe2), Zirconium Diselenide (ZrSe2) and a sensing layer using the Kretschmann configuration. At the optimized thickness of the Ag layer, the sensitivity was measured using the angular interrogation method with a refractive index (RI) of 1.33-1.35. The sensitivity of the sensor was found to be 337.98°/RIU for a 2 nm SnSe2 layer thickness at RI of 1.34 and 320.94°/RIU for a 1 nm SnSe2 layer thickness at RI of 1.35 throughout with remarkable figure of merit (FoM) of 60.78/RIU and 64.57/RIU at 633 nm wavelength. The maximum sensitivity was achieved with 1 nm thickness of the SnSe2 layer. By systematically optimizing the Ag thickness, significant improvements in sensitivity, minimum reflectance (Rmin), detection accuracy (DA), and figure of merit (FoM) were achieved compared with the conventional Ag-only configuration. These additional SnSe2 layers increase the confinement of the electromagnetic field, increase the number of adsorption sites for biomolecules, and increase the effective change in the RI, resulting in larger shifts in the resonance angles. The proposed multilayer sensor provides a promising platform for high-performance, stable, and repeatable biosensing applications in chemical detection, environmental monitoring, and medical diagnostics, according to the results obtained.

The Long-Range Surface Plasmon Resonance (LRSPR) sensor is an advanced SPR sensor known for its label-free analysis, high sensitivity, accuracy, and flexible design. Owing to its extended penetration depth, it is suitable for biomacromolecule detection. With an emphasis on the dielectric buffer layers (DBLs) found in LRSPR chips, this paper describes the various types of LRSPR designs, their sensing properties, theoretical underpinnings, and applications. The potential of LRSPR in biochemistry, medicine, and food inspection is highlighted in a comparison of conventional SPR (cSPR) and LRSPR, especially through prism-based designs with promising applications. By improving the penetration depth (PD), narrowing the resonance curve, and raising the detection accuracy and figure of merit, dielectric buffer layers such as Cytop, Teflon, MgF2, and LiF are essential for improving LRSPR sensor performance, according to the comparative analysis provided in this review. Because of their extremely low refractive indices, Cytop and Teflon exhibit superior sensitivity and imaging performance among these materials, while MgF2 and LiF provide superior mechanical and environmental stability for reliable sensing applications. The reviewed studies also show that the addition of advanced nanomaterials, such as graphene, black phosphorus, MXenes, antimonene, TaS2, and transition metal dichalcogenides (TMDs), greatly enhances the sensing properties, allowing for the highly sensitive detection of pathogens, biomolecules, disease biomarkers, and environmental contaminants. Compared to conventional SPR sensors, the reviewed literature shows that LRSPR sensors offer noticeably higher sensitivity, deeper PD, narrower resonance linewidth, and improved detection accuracy (DA). Additionally, the development of next-generation intelligent LRSPR sensing platforms for healthcare, environmental monitoring, food safety, and biomedical diagnostics is anticipated to be accelerated by the integration of advanced nanomaterials, artificial intelligence (AI), and machine learning (ML) techniques.