Robust Counterfactual Explanations for Tree-Based Ensembles

Counterfactual explanations inform ways to achieve a desired outcome from a machine learning model. However, such explanations are not robust to certain real-world changes in the underlying model (e.g., retraining the model, changing hyperparameters, etc.), questioning their reliability in several applications, e.g., credit lending. In this work, we propose a novel strategy -- that we call RobX -- to generate robust counterfactuals for tree-based ensembles, e.g., XGBoost. Tree-based ensembles pose additional challenges in robust counterfactual generation, e.g., they have a non-smooth and non-differentiable objective function, and they can change a lot in the parameter space under retraining on very similar data. We first introduce a novel metric -- that we call Counterfactual Stability -- that attempts to quantify how robust a counterfactual is going to be to model changes under retraining, and comes with desirable theoretical properties. Our proposed strategy RobX works with any counterfactual generation method (base method) and searches for robust counterfactuals by iteratively refining the counterfactual generated by the base method using our metric Counterfactual Stability. We compare the performance of RobX with popular counterfactual generation methods (for tree-based ensembles) across benchmark datasets. The results demonstrate that our strategy generates counterfactuals that are significantly more robust (nearly 100% validity after actual model changes) and also realistic (in terms of local outlier factor) over existing state-of-the-art methods.Comment: Accepted at ICML 202

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees

There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $\|\text{Params}(M){-}\text{Params}(m)\|{<}\Delta$. However, models can often change significantly in the parameter space with little to no change in their predictions or accuracy on the given dataset. In this work, we introduce a mathematical abstraction termed \emph{naturally-occurring} model change, which allows for arbitrary changes in the parameter space such that the change in predictions on points that lie on the data manifold is limited. Next, we propose a measure -- that we call \emph{Stability} -- to quantify the robustness of counterfactuals to potential model changes for differentiable models, e.g., neural networks. Our main contribution is to show that counterfactuals with sufficiently high value of \emph{Stability} as defined by our measure will remain valid after potential ``naturally-occurring'' model changes with high probability (leveraging concentration bounds for Lipschitz function of independent Gaussians). Since our quantification depends on the local Lipschitz constant around a data point which is not always available, we also examine practical relaxations of our proposed measure and demonstrate experimentally how they can be incorporated to find robust counterfactuals for neural networks that are close, realistic, and remain valid after potential model changes. This work also has interesting connections with model multiplicity, also known as, the Rashomon effect.Comment: International Conference on Machine Learning (ICML), 202

A conceptual framework on health professionals' engagement towards pharmacovigilance: a qualitative exploration

Background: With the growing reliance on drug therapy in the recent era, the safety of medications is one of the vital parameters for the success of any medicine. Considering this, pharmacovigilance (PV) was developed to provide adequate identification, reporting, evaluation, and understanding of adverse drug reactions (ADR). The objective of this study was to understand the opinion of health care providers on PV, the current reporting mechanisms, identifying the causes for underreporting, and the existing process in clinical practice.Methods: A qualitative study using pretested interview guide was conducted among 20 different cadres of healthcare personnel (doctors, pharmacists, and staff nurses) from various hospitals such as government, private, corporate, and medical college of Odisha state. The data were analysed using a thematic analysis. The meaning units have been identified from the transcript and coded with MAXQDA software (MAXQDA Analytics Pro 2020, VERBI GmbH Berlin).Results: Participants showed a lack of awareness regarding the concept of PV. A cluster of challenges such as lack of ADR monitoring, non-conducive work atmosphere and lack of cooperation between staff, lack of knowledge among the health professionals, and fear of legal liability as major pitfalls causing poor ADR reporting. To enhance the pharmacovigilance practice, participants suggested context-specific strategies such as IEC activities, innovative ideas to improve ADR monitoring, regular monitoring.Conclusions: Capacity building through training, regular monitoring and supervision to strengthen the pharmacovigilance practices is the current need in India

ZERUMBONE, A NATURAL PLANT DIETARY COMPOUND INDUCES EXPRESSION OF INTERLEUKIN-12P70 CYTOKINE IN HUMAN PERIPHERAL BLOOD MONONUCLEAR CELLS

ABSTRACTObjective: Despite possessing many biological activities as antiproliferative, antioxidant, anti-inflammatory, and anticancerous, and zerumbone lacksany evidence for its immunomodulatory activity. This naturally occurring dietary compound needs to be developed as drug to support therapeuticclaims in various infections and diseases.Methods: Hence, in this study, the immunomodulatory effects of zerumbone were investigated by evaluating the effect of this compound toward thelymphocytes proliferation in human peripheral blood mononuclear cells.Results: Lymphocyte proliferation assay showed that zerumbone was able to activate human lymphocytes at dosage-dependent manner at the highestconcentration 40 μl/mL. The production of human interleukin-12p70 cytokine in culture supernatant from activated lymphocytes was upregulatedby zerumbone at 24 hrs and gradually decreased at 48 hrs. Hence, the study confirms the immunomodulatory activity of zerumbone which play animportant role in boosting up the immune system through cytokine production in dosage dependent manner.Conclusion: The study concludes that zerumbone could be used as a lead molecule in herbal therapeutic world as an immunomodulatory drug in thetreatment of chronic infections and various autoimmune disorders.Keywords: Zerumbone, Peripheral blood mononuclear cells, Immunomodulation, Cytokine, Lymphocyte proliferation

Pathogenesis of cerebral malaria: new diagnostic tools, biomarkers, and therapeutic approaches.

Cerebral malaria is a severe neuropathological complication of Plasmodium falciparum infection. It results in high mortality and post-recovery neuro-cognitive disorders in children, even after appropriate treatment with effective anti-parasitic drugs. While the complete landscape of the pathogenesis of cerebral malaria still remains to be elucidated, numerous innovative approaches have been developed in recent years in order to improve the early detection of this neurological syndrome and, subsequently, the clinical care of affected patients. In this review, we briefly summarize the current understanding of cerebral malaria pathogenesis, compile the array of new biomarkers and tools available for diagnosis and research, and describe the emerging therapeutic approaches to tackle this pathology effectively

Utilization of modern temporary contraceptive methods and its predictors among reproductive-aged women in India: insights from NFHS-5 (2019–21)

Evidence from various studies on modern contraceptive methods shows that the utilization varies greatly. The present study aimed to estimate the magnitude and determinants for temporary modern contraceptive utilization among reproductive-aged (15-49 years) women in India. We analysed National Family Health Survey-5 data using the “svyset” command in STATA software. Modern contraception utilization was estimated using the weighted prevalence, and its correlates were assessed by multivariable regression by reporting an adjusted prevalence ratio (aPR) with 95% confidence interval (CI). QGIS 3.2.1 software was used for spatial analysis of different temporary modern contraceptives. The mean (SD) age of 359,825 respondents was 31.6 (8.5) years with 75.1% (n = 270,311) and 49.2% (n = 177,165) of them being from rural area and having completed education up to secondary school, respectively. The overall utilization of modern temporary contraception was 66.1% [95%CI: 65.90–66.35, n = 237,953]. Multigravida (vs. nulligravida) [aPR = 2.13 (1.98–2.30)], higher education of husband (vs. not educated) [aPR = 1.20 (1.14–1.27)], urban (vs. rural) [aPR = 1.06 (1.03–1.10)], watching television less than once a week (vs. not at all) [aPR = 1.04 (1.01–1.08)], divorced (vs. married) [aPR = 0.65 (0.45–0.94)], and Scheduled Tribe (ST) (vs. unreserved) [aPR = 0.92 (0.88–0.96)] were significant independent determinants. The highest utilization of male condoms, IUCDs, pills and injections were in Himachal Pradesh (86%), Nagaland (64%), Tripura (85%), and Ladakh (20%), respectively. Out of every ten reproductive-aged (15–49 years) women in India, six are using temporary modern contraceptive methods. More intervention strategies should be planned, considering factors like gravida, education, residence, health promotion and caste to attain replacement fertility level

Agricultural and empowerment pathways from land ownership to women's nutrition in India.

Land size is an important equity concern for the design of 'nutrition-sensitive' agricultural interventions. We unpack some of the pathways between land and nutrition using a cross-sectional baseline survey data set of 4,480 women from 148 clusters from the 'Upscaling Participatory Action and Videos for Agriculture and Nutrition' trial in Keonjhar district in Odisha, India. Variables used are household ln-land size owned (exposure) and maternal dietary diversity score out of 10 food groups and body mass index (BMI; kg/m2 ) (outcomes); and mediators investigated are production diversity score, value of agricultural production, and indicators for women's empowerment (decision-making in agriculture, group participation, work-free time and land ownership). We assessed mediation using a non-parametric potential outcomes framework method. Land size positively affects maternal dietary diversity scores [β 0.047; 95% confidence interval (CI) (0.011, 0.082)] but not BMI. Production diversity, but not value of production, accounts for 17.6% of total effect mediated. We observe suppression of the effect of land size on BMI, with no evidence of a direct effect for either of the agricultural mediators but indirect effects of β -0.031 [95% CI (-0.048, -0.017)] through production diversity and β -0.047 [95% CI (-0.075, -0.021)] through value of production. An increase in land size positively affects women's decision-making, which in turn negatively affects maternal BMI. The positive effect of work-free time on maternal BMI is suppressed by the negative effect of household land size on work-free time. Agriculture interventions must consider land quality, women's decision-making and implications for women's workload in their design

Effectiveness of intermittent screening and treatment for the control of malaria in pregnancy: a cluster randomised trial in India.

BACKGROUND: The control of malaria in pregnancy (MiP) in India relies on testing women who present with symptoms or signs suggestive of malaria. We hypothesised that intermittent screening and treatment for malaria at each antenatal care visit (ISTp) would improve on this approach and reduce the adverse effects of MiP. METHODS: A cluster randomised controlled trial comparing ISTp versus passive case detection (PCD) was conducted in Jharkhand state. Pregnant women of all parities with a gestational age of 18-28 weeks were enrolled. Women in the ISTp group were screened with a rapid diagnostic test (RDT) for malaria at each antenatal clinic visit and those in the PCD group were screened only if they had symptoms or signs suggestive of malaria. All RDT positive women were treated with artesunate/sulfadoxine-pyrimethamine. The primary endpoint was placental malaria, determined by placental histology, and the key secondary endpoints were birth weight, gestational age, vital status of the newborn baby and maternal anaemia. RESULTS: Between April 2012 and September 2015, 6868 women were enrolled; 3300 in 46 ISTp clusters and 3568 in 41 PCD clusters. In the ISTp arm, 4.9% of women were tested malaria positive and 0.6% in the PCD arm. There was no difference in the prevalence of placental malaria in the ISTp (87/1454, 6.0%) and PCD (65/1560, 4.2%) groups (6.0% vs 4.2%; OR 1.34, 95% CI 0.78 to 2.29, p=0.29) or in any of the secondary endpoints. CONCLUSION: ISTp detected more infections than PCD, but monthly ISTp with the current generation of RDT is unlikely to reduce placental malaria or impact on pregnancy outcomes. ISTp trials with more sensitive point-of-care diagnostic tests are needed

Oriya Character Recognition using Neural Networks

The biggest challenge in the field of image processing is to recognize documents both in printed and handwritten format. Optical Character Recognition (OCR) is a type of document image analysis where scanned digital image that contains either machine printed or handwritten script input into an OCR software engine and translating it into an editable machine readable digital text format. Development of OCRs for Indian script is an active area of research today. We are making an attempt to develop the OCR system for Oriya language, which is the official language of Orissa. Oriya language present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. In this paper, we argue that a number of automatic and semi-automatic tools can ease the development of recognizers for new font styles and new scripts. We discuss briefly and show how they have helped build new OCRs for the purpose of recognizing Oriya script. We have used the Back propagation Neural Network for efficient recognition where the errors were corrected through back propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers, i.e. the input layer, the output layer and the middle layer or hidden layers