Empirically and clinically useful decision making in psychotherapy: Differential predictions with treatment response models.

The work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.

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Immuno-Imaging to Predict Treatment Response in Infection, Inflammation and Oncology

Journal of Clinical Medicine ◽

10.3390/jcm8050681 ◽

2019 ◽

Vol 8 (5) ◽

pp. 681 ◽

Cited By ~ 5

Author(s):

Alberto Signore ◽

Chiara Lauri ◽

Sveva Auletta ◽

Kelly Anzola ◽

Filippo Galli ◽

...

Keyword(s):

Decision Making ◽

Nuclear Medicine ◽

Treatment Response ◽

Fungal Infections ◽

Inflammatory Diseases ◽

Imaging Techniques ◽

Nuclear Medicine Imaging ◽

Tumour Immunology ◽

Therapy Decision

Background: Molecular nuclear medicine plays a pivotal role for diagnosis in a preclinical phase, in genetically susceptible patients, for radio-guided surgery, for disease relapse evaluation, and for therapy decision-making and follow-up. This is possible thanks to the development of new radiopharmaceuticals to target specific biomarkers of infection, inflammation and tumour immunology. Methods: In this review, we describe the use of specific radiopharmaceuticals for infectious and inflammatory diseases with the aim of fast and accurate diagnosis and treatment follow-up. Furthermore, we focus on specific oncological indications with an emphasis on tumour immunology and visualizing the tumour environment. Results: Molecular nuclear medicine imaging techniques get a foothold in the diagnosis of a variety of infectious and inflammatory diseases, such as bacterial and fungal infections, rheumatoid arthritis, and large vessel vasculitis, but also for treatment response in cancer immunotherapy. Conclusion: Several specific radiopharmaceuticals can be used to improve diagnosis and staging, but also for therapy decision-making and follow-up in infectious, inflammatory and oncological diseases where immune cells are involved. The identification of these cell subpopulations by nuclear medicine techniques would provide personalized medicine for these patients, avoiding side effects and improving therapeutic approaches.

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Feedback Approaches to Modeling Structural Shifts in Market Response

Journal of Marketing ◽

10.1177/002224298004400110 ◽

1980 ◽

Vol 44 (1) ◽

pp. 71-80 ◽

Cited By ~ 7

Author(s):

Vijay Mahajan ◽

Stuart I. Bretschneider ◽

John W. Bradford

Keyword(s):

Decision Making ◽

Market Response ◽

Response Models ◽

Marketing Decision ◽

Develop Market ◽

Structural Shifts ◽

Self Adaptive

In the presence of a dynamic marketing decision making environment, there is a need to develop market response models which can adjust automatically to changing data patterns. This paper demonstrates the use of feedback approaches to develop self-adaptive market response models.

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Lameness control in a UK dairy goat herd

Livestock ◽

10.12968/live.2019.24.4.189 ◽

2019 ◽

Vol 24 (4) ◽

pp. 189-192

Author(s):

Peter O'Malley

Keyword(s):

Decision Making ◽

Treatment Response ◽

Control Strategy ◽

Dairy Goat ◽

Nutritional Management ◽

Intervention Period ◽

White Line ◽

Farm Productivity ◽

Infection Pressure

This case study documents a lameness control strategy in a commercial dairy goat herd with treponeme-associated non-healing white line disease (nhWLD). A similar control strategy to nhWLD in cattle was used, focusing on reducing environmental infection pressure of treponemes and culling of cases unresponsive to treatment. This case study highlights the importance of regular and accurate lameness scoring, correct lesion categorisation, and critical decision making on treatment versus culling. Over the 12 month intervention period lameness prevalence reduced from 45% to 15%. Treatment response of existing nhWLD was poor, but prevention of new cases was good. A total of 27% of the lactating herd were culled due to lameness, however total herd yield dropped by less than 1% despite nutritional management remaining broadly the same, demonstrating the wider benefits of culling in terms of farm productivity and efficiency.

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Disease burden estimation to support policy decision-making and research prioritization for arsenic mitigation

Journal of Water and Health ◽

10.2166/wh.2006.056 ◽

2006 ◽

Vol 5 (1) ◽

pp. 67-81 ◽

Cited By ~ 28

Author(s):

Guy Howard ◽

M. Feroze Ahmed ◽

Shamsul Gaifur Mahmud ◽

Peter Teunis ◽

Annette Davison ◽

...

Keyword(s):

Decision Making ◽

Water Supply ◽

Dose Response ◽

Disease Burden ◽

Population Characteristics ◽

Quality Data ◽

Water Quality Data ◽

Response Models ◽

Microbial Indicators ◽

Alternative Water

The main response to arsenic contamination of shallow tubewells in Bangladesh is the provision of alternative water supplies. To support decision-making in relation to alternative water supply selection, the Arsenic Policy Support Unit commissioned the development of a tool for estimating disease burdens for specific options using disability-adjusted life years as the metric. This paper describes the assumptions in dose-responses, relationships between microbial indicators and pathogens, water consumed and population characteristics used, and presents a case study of how the tool was applied. Water quality data and dose-response models were used to predict disease burdens due to microbial pathogens and arsenic. Disease burden estimates predicted by the tool were based on evidence in the published literature. There were uncertainties in key assumptions of water consumed and the ratio of microbial indicators and pathogens, which led to broad confidence intervals and the need to consider the results in a wider context and further research needs. Deep tubewells and rainwater harvesting had the lowest disease burden estimates, while pond sand filters and dug wells had much higher predicted disease burden due to frequent microbial contamination. The need for rigorous water supply protection through water safety plans was highlighted. At present, the risk assessment is useful for informing judgement by experienced water and health professionals and identifying key research questions. Improved arsenic dose-response models and a better understanding of the relationship between microbial indicators and pathogens in tropical settings are required.

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Semiparametric instrumental variable estimation of treatment response models

Journal of Econometrics ◽

10.1016/s0304-4076(02)00201-4 ◽

2003 ◽

Vol 113 (2) ◽

pp. 231-263 ◽

Cited By ~ 297

Author(s):

Alberto Abadie

Keyword(s):

Treatment Response ◽

Instrumental Variable ◽

Response Models ◽

Instrumental Variable Estimation

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How I treat acne scars?

10.25259/csdm_45_2021 ◽

2021 ◽

Vol 1 ◽

pp. 46

Author(s):

Renita Lourdhurajan ◽

Subashini Selvadurairaj

Keyword(s):

Decision Making ◽

Treatment Response ◽

Treatment Approach ◽

Care Home ◽

Comprehensive System ◽

Revision Process ◽

Scar Revision ◽

Acne Scars ◽

Algorithmic Treatment ◽

Partnership Approach

The approach to managing acne scars is unique to every dermatologist. This depends on the skin type of his/her clientele, the tools, techniques and devices available and/or used, and the protocols developed based on his/her experience with treating acne scars, developed over a period of time. Herein, we share our algorithmic treatment approach to acne scars, which allows for a consultative decision-making together with the patient, while offering adequate flexibility to modify the plan based on treatment response. Eventually, a customized and comprehensive system works best, and a partnership approach signified by a robust self-care home plan, helps accelerate the scar revision process.

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A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy

Frontiers in Oncology ◽

10.3389/fonc.2020.592556 ◽

2021 ◽

Vol 10 ◽

Author(s):

Dong Song ◽

Xiaxia Man ◽

Meng Jin ◽

Qian Li ◽

Han Wang ◽

...

Keyword(s):

Breast Cancer ◽

Decision Making ◽

Neoadjuvant Chemotherapy ◽

Treatment Response ◽

Prediction Method ◽

Clinical Information ◽

Support Vector ◽

Breast Cancer Patients ◽

Svm Model ◽

Patient Prognosis

Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computational methods can improve decision-making by evaluating and predicting treatment responses using a multidisciplinary approach, as there are no uniformly accepted protocols for all institutions for adopting different treatment regiments. In this study, 166 Chinese breast cancer cases were collected from patients who received NAC treatment at the First Bethune Hospital of Jilin University. The Miller–Payne grading system was used to evaluate the treatment response. Four machine learning multiple classifiers were constructed to predict the treatment response against the 26 features extracted from the patients’ clinical data, including Random Forest (RF) model, Convolution Neural Network (CNN) model, Support Vector Machine (SVM) model, and Logistic Regression (LR) model, where the RF model achieved the best performance using our data. To allow a more general application, the models were reconstructed using only six selected features, and the RF model achieved the highest performance with 54.26% accuracy. This work can efficiently guide optimal treatment planning for breast cancer patients.

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