Realizing the Value Potential of AI in Service Needs Assessment: Cases in Child Welfare and Mental Health Services

In social and health care the use of technology that utilizes data has great potential from the point of view of value creation. This case study examines the factors that impact the value potential realization of AI prediction models as part of the customer/patient service need assessment process. The research focuses on a pilot project of a Finnish case organization, in which prediction models were tested in child welfare and mental health services. Both positive and negative value-realizing factors were found in the research. The information produced by artificial intelligence has great value potential. Regulation and transparency of data need to be addressed, but at the same time, more flexible use of social and health register data needs to be considered to ensure that resources are allocated in a value-added way

Split-BOLFI for for misspecification-robust likelihood free inference in high dimensions

Likelihood-free inference for simulator-based statistical models has recently grown rapidly from its infancy to a useful tool for practitioners. However, models with more than a very small number of parameters as the target of inference have remained an enigma, in particular for the approximate Bayesian computation (ABC) community. To advance the possibilities for performing likelihood-free inference in high-dimensional parameter spaces, here we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space. Our method achieves computational scalability by using separate acquisition procedures for the discrepancies defined for different parameters. These efficient high-dimensional simulation acquisitions are combined with exponentiated loss-likelihoods to provide a misspecification-robust characterisation of the marginal posterior distribution for all model parameters. The method successfully performs computationally efficient inference in a 100-dimensional space on canonical examples and compares favourably to existing Copula-ABC methods. We further illustrate the potential of this approach by fitting a bacterial transmission dynamics model to daycare centre data, which provides biologically coherent results on the strain competition in a 30-dimensional parameter space

Identification of multiplicatively acting modulatory mutational signatures in cancer

Background A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. Results We carefully analysed such additive model fits from the PCAWG study cataloguing mutational signatures as well as their activities across thousands of cancers. Our analysis identified systematic and non-random structure of residuals that is left unexplained by the additive model. We used hierarchical clustering to identify cancer subsets with similar residual profiles to show that both systematic mutation count overestimation and underestimation take place. We propose an extension to the additive mutational signature model—multiplicatively acting modulatory processes—and develop a maximum-likelihood framework to identify such modulatory mutational signatures. The augmented model is expressive enough to almost fully remove the observed systematic residual patterns. Conclusion We suggest the modulatory processes biologically relate to sample specific DNA repair propensities with cancer or tissue type specific profiles. Overall, our results identify an interesting direction where to expand signature analysis.Peer reviewe

Co-culturing with Streptococcus anginosus alters Staphylococcus aureus transcriptome when exposed to tonsillar cells

IntroductionImproved understanding of Staphylococcus aureus throat colonization in the presence of other co-existing microbes is important for mapping S. aureus adaptation to the human throat, and recurrence of infection. Here, we explore the responses triggered by the encounter between two common throat bacteria, S. aureus and Streptococcus anginosus, to identify genes in S. aureus that are important for colonization in the presence of human tonsillar epithelial cells and S. anginosus, and further compare this transcriptome with the genes expressed in S. aureus as only bacterium.MethodsWe performed an in vitro co-culture experiment followed by RNA sequencing to identify interaction-induced transcriptional alterations and differentially expressed genes (DEGs), followed by gene enrichment analysis.Results and discussionA total of 332 and 279 significantly differentially expressed genes with p-value < 0.05 and log2 FoldChange (log2FC) ≥ |2| were identified in S. aureus after 1 h and 3 h co-culturing, respectively. Alterations in expression of various S. aureus survival factors were observed when co-cultured with S. anginosus and tonsillar cells. The serine-aspartate repeat-containing protein D (sdrD) involved in adhesion, was for example highly upregulated in S. aureus during co-culturing with S. anginosus compared to S. aureus grown in the absence of S. anginosus, especially at 3 h. Several virulence genes encoding secreted proteins were also highly upregulated only when S. aureus was co-cultured with S. anginosus and tonsillar cells, and iron does not appear to be a limiting factor in this environment. These findings may be useful for the development of interventions against S. aureus throat colonization and could be further investigated to decipher the roles of the identified genes in the host immune response in context of a throat commensal landscape