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Cybersecurity Monitoring/Mapping of USA Healthcare (All Hospitals) - Magnified Vulnerability due to Shared IT Infrastructure, Market Concentration, & Geographical Distribution
(2024) Yurcik, William; Schick, Andreas; North, Stephen; Gastner, Michael T.; FABIO ROBERTO DE MIRANDA; RODOLFO DA SILVA AVELINO; ANDRE FILIPE DE MORAES BATISTA; Pluta, Gregory; Brooks, Ian
In October 2024, there are two defining characteristics of a healthcare provider: (1) geographic location and services available at their physical structure and (2) Internet connectivity and services available via their virtual presence. For previous centuries we focused on the first defining characteristic and now we need to shift to understand and address issues that may arise from the new second defining characteristic.
In this paper we address issues related to Internet connectivity and virtual presence of USA healthcare providers, especially hospitals, when ransomware cyberattacks resulting in service outages occur. We show the cybersecurity posture of a large critical national infrastructure (USA healthcare) can be measured, mapped, and quantitatively baselined. Empirical results reveal systemic issues in USA healthcare presenting "magnified vulnerabilities" in that a single exploit can have an outsized impact on an entire nationwide infrastructure. As the initial step toward addressing this issue, we document for the first time the magnified cybersecurity vulnerability of USA healthcare to shared IT infrastructure, market concentration, and the geographical distribution of hospitals.
SNA-Based Reasoning for Multi-Agent Team Composition
(2015) ANDRE FILIPE DE MORAES BATISTA; Marietto, Maria das Graças Bruno
The social network analysis (SNA), branch of complex systems can be used in the construction of multi agent systems. This paper proposes a study of how social network analysis can assist in modeling multi agent systems, while addressing similarities and differences between the two theories. We built a prototype of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on the basis of the social network established between agents. Agents make use of performance indicators to assess when should change their social network to maximize the participation in teams.
Neonatal mortality prediction with routinely collected data: a machine learning approach
(2021) ANDRE FILIPE DE MORAES BATISTA; Diniz, Carmen S. G.; Bonilha, Eliana A.; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Background: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas.
Methods: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data.
Results: The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO’s five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records.
Conclusion: Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.
Predictors of tooth loss: A machine learning approach
(2021) Elani, Hawazin W.; ANDRE FILIPE DE MORAES BATISTA; W. Murray Thomson; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Introduction
Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.
Methods
We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values.
Results
The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone.
Conclusions
Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.