Machine learning determination of applied behavioral analysis treatment plan type.

BACKGROUND: Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10-20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment. METHODS: Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811-0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629-0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model's predictions were bathing ability, age, and hours per week of past ABA treatment. CONCLUSION: This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.

Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment.

Objective This study examines the implementation of a hybrid applied behavioral analysis (ABA) treatment model to determine its impact on autism spectrum disorder (ASD) patient outcomes.  Methods Retrospective data were collected for 25 pediatric patients to measure progress before and after the implementation of a hybrid ABA treatment model under which therapists consistently captured session notes electronically regarding goals and patient progress. ABA treatment was streamlined for consistent delivery, with improved software utilization for tracking scheduling and progress. Eleven goals within three domains (behavioral, social, and communication) were examined.  Results After the implementation of the hybrid model, the goal success rate improved by 9.7% compared to the baseline; 41.8% of goals showed improvement, 38.4% showed a flat trend, and 19.8% showed deterioration. Multiple goals trended upwards in 76% of the patients.  Conclusion This pilot study demonstrated that enhancing the consistency with which ABA treatment is monitored/delivered can improve patient outcomes as seen through improved attainment of goals.

Genomic and phenotypic signatures of bacteriophage coevolution with the phytopathogen Pseudomonas syringae.

The rate and trajectory of evolution in an obligate parasite is critically dependent on those of its host(s). Adaptation to a genetically homogeneous host population should theoretically result in specialization, while adaptation to an evolving host population (i.e., coevolution) can result in various outcomes including diversification, range expansion, and/or local adaptation. For viruses of bacteria (bacteriophages, or phages), our understanding of how evolutionary history of the bacterial host(s) impacts viral genotypic and phenotypic evolution is currently limited. In this study, we used whole genome sequencing and two different metrics of phage impacts to compare the genotypes and phenotypes of lytic phages that had either coevolved with or were repeatedly passaged on an unchanging (ancestral) strain of the phytopathogen Pseudomonas syringae. Genomes of coevolved phages had more mutations than those of phages passaged on a constant host, and most mutations were in genes encoding phage tail-associated proteins. Phages from both passaging treatments shared some phenotypic outcomes, including range expansion and divergence across replicate populations, but coevolved phages were more efficient at reducing population growth (particularly of sympatric coevolved hosts). Genotypic similarity correlated with infectivity profile similarity in coevolved phages, but not in phages passaged on the ancestral host. Overall, while adaptation to either host type (coevolving or ancestral) led to divergence in phage tail proteins and infectivity patterns, coevolution led to more rapid molecular changes that increased bacterial killing efficiency and had more predictable effects on infectivity range. Together, these results underscore the important role of hosts in driving viral evolution and in shaping the genotype-phenotype relationship.

Digest: Investigating reproductive isolation in test tubes.

Reproductive isolation can result from incompatibilities between mutations that arise in different individuals. Wang and Cooper examined this mechanism of postzygotic isolation in Escherichia coli experimentally evolved in either glucose or lactose. They formed recombinants from parents evolved in the same or different environments. Both same-environment and different-environment recombinants had lower fitness than the null expectation, but with important exceptions. These results indicate that the development of reproductive isolation is complex and results from incompatibilities that arise when populations are selected in either the same or different environment.