Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Robust Static Output-Feedback Control for MJLS with Non-Homogeneous Markov Chains: A Comparative Study Considering a Wireless Sensor Network with Time-Varying PER.

This paper deals with the problem of control through a semi-reliable communication channel, such as wireless sensor networks (WSN). Particularly, the case investigated is the one where the packet loss rate of the network is time-varying due to, for instance, variation in the distance between the nodes. Considering this practical motivation, the control system is modeled using a formulation based on discrete-time Markov jump linear systems (MJLS) with non-homogeneous Markov chains (time-varying transition probabilities). New control design conditions based on parameter-dependent linear matrix inequalities are proposed in order to solve this problem. The purpose is to demonstrate that this strategy is suitable to handle the networked control problem by comparing the temporal behavior of the closed-loop system with the Markovian controller and a standard proportional-integral-derivative (PID) controller. The case study presented in the paper considers the problem of the remote control of a Vertical Take-Off and Landing (VTOL) vehicle through a wireless communication channel. The network packet loss model employed in the case study is based on data collected on a wireless network workbench, which was previously developed and validated by the authors.

A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs.

This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network.

Prenatal treatment of ornithine transcarbamylase deficiency.

PURPOSE OF STUDY: Patients with neonatal urea cycle defects (UCDs) typically experience severe hyperammonemia during the first days of life, which results in serious neurological injury or death. Long-term prognosis despite optimal pharmacological and dietary therapy is still poor. The combination of intravenous sodium phenylacetate and sodium benzoate (Ammonul®) can eliminate nitrogen waste independent of the urea cycle. We report attempts to improve outcomes for males with severe ornithine transcarbamylase deficiency (OTCD), a severe X-linked condition, via prenatal intravenous administration of Ammonul and arginine to heterozygous carrier females of OTCD during labor. METHODS USED: Two heterozygote OTCD mothers carrying male fetuses with a prenatal diagnosis of OTCD received intravenous Ammonul, arginine and dextrose-containing fluids shortly before birth. Maintenance Ammonul and arginine infusions and high-caloric enteral nutrition were started immediately after birth. Ammonul metabolites were measured in umbilical cord blood and the blood of the newborn immediately after delivery. Serial ammonia and biochemical analyses were performed following delivery. SUMMARY OF RESULTS: Therapeutic concentrations of Ammonul metabolites were detected in umbilical cord and neonatal blood samples. Plasma ammonia and glutamine levels in the postnatal period were within the normal range. Peak ammonia levels in the first 24-48h were 53mcmol/l and 62mcmol/l respectively. The boys did not experience neurological sequelae secondary to hyperammonemia and received liver transplantation at ages 3months and 5months. The patients show normal development at ages 7 and 3years. CONCLUSION: Prenatal treatment of mothers who harbor severe OTCD mutations and carry affected male fetuses with intravenous Ammonul and arginine, followed by immediate institution of maintenance infusions after delivery, results in therapeutic levels of benzoate and phenylacetate in the newborn at delivery and, in conjunction with high-caloric enteral nutrition, prevents acute hyperammonemia and neurological decompensation. Following initial medical management, early liver transplantation may improve developmental outcome.

Protocol for Energy-Efficiency in Networked Control Systems Based on WSN.

This paper proposes a new communication protocol for output-feedback control through multi-hop Wireless Sensor Network (WSN). The protocol is based on a Hop-by-Hop transport scheme and is especially devised to simultaneously fulfill two conflicting criteria: the network energy consumption and the stability/performance (in terms of H∞ norm) of the closed-loop system. The proposed protocol can be implemented by means of three heuristics, basically using distinct rules to control the maximum number of retransmissions allowed in terms of the voltage level of the batteries of the network nodes. As another contribution, a Markov jump based representation is proposed to model the packet loss in the communication channel, giving rise to a systematic procedure to determine the transition probability matrix and the Markov chain operation modes of a network with multiple information sources. The synthesis of the output-feedback controller is made in two steps (observer filter plus a state-feedback controller) for the Markov model assuming partial availability of the operation modes. The efficiency and applicability of the communication protocol is illustrated by means of a numerical experiment, based on a physical model of a coupled tanks plant. The features of each heuristic of implementation of the proposed protocol are presented in the numerical comparisons.

Changing Management of the Patent Ductus Arteriosus: Effect on Neonatal Outcomes and Resource Utilization.

Objective This historical cohort study investigated how a shift toward a more conservative approach of awaiting spontaneous closure of the patent ductus arteriosus (PDA) in preterm infants has affected neonatal outcomes and resource utilization. Methods We retrospectively studied very low birth weight infants diagnosed with a PDA by echocardiogram (ECHO) in 2006-2008 (era 1), when medical or surgical PDA management was emphasized, to those born in 2010-2012 (era 2) when conservative PDA management was encouraged. Multiple regression analyses adjusted for gestational age were performed to assess differences in clinical outcomes and resource utilization between eras. Results More infants in era 2 (35/89, 39%) compared with era 1 (22/120, 18%) had conservative PDA management (p < 0.01). Despite no difference in surgical ligation rate, infants in era 2 had ligation later (median 24 vs. 8 days, p < 0.0001). There was no difference in clinical outcomes between eras, while number of ECHOs per patient was the only resource measure that increased in era 2 (median 3 vs. 2 ECHOs, p = 0.003). Conclusion In an era of more conservative PDA management, no increase in adverse clinical outcomes or significant change in resource utilization was found. Conservative PDA management may be a safe alternative for preterm infants.

Cannabinoid modulation of alpha2 adrenergic receptor function in rodent medial prefrontal cortex.

Endocannabinoids acting at the cannabinoid type 1 receptor (CB1R) are known to regulate attention, cognition and mood. Previous studies have shown that, in the rat medial prefrontal cortex (mPFC), CB1R agonists increase norepinephrine release, an effect that may be attributed, in part, to CB1Rs localised to noradrenergic axon terminals. The present study was aimed at further characterising functional interactions between CB1R and adrenergic receptor (AR) systems in the mPFC using in vitro intracellular electrophysiology and high-resolution neuroanatomical techniques. Whole-cell patch-clamp recordings of layer V/VI cortical pyramidal neurons in rats revealed that both acute and chronic treatment with the synthetic CB1R agonist WIN 55,212-2 blocked elevations in cortical pyramidal cell excitability and increases in input resistance evoked by the α2-adrenergic receptor (α2-AR) agonist clonidine, suggesting a desensitisation of α2-ARs. These CB1R-α2-AR interactions were further shown to be both action potential- and gamma-aminobutyric acid-independent. To better define sites of cannabinoid-AR interactions, we localised α2A-adrenergic receptors (α2A-ARs) in a genetically modified mouse that expressed a hemoagglutinin (HA) tag downstream of the α2A-AR promoter. Light and electron microscopy indicated that HA-α2A-AR was distributed in axon terminals and somatodendritic processes especially in layer V of the mPFC. Triple-labeling immunocytochemistry revealed that α2A-AR and CB1R were localised to processes that contained dopamine-ß-hydroxylase, a marker of norepinephrine. Furthermore, HA-α2A-AR was localised to processes that were directly apposed to CB1R. These findings suggest multiple sites of interaction between cortical cannabinoid-adrenergic systems that may contribute to understanding the effect of cannabinoids on executive functions and mood.

Red blood cell transfusion is not associated with necrotizing enterocolitis: a review of consecutive transfusions in a tertiary neonatal intensive care unit.

OBJECTIVE: To explore the association between red blood cell transfusion and necrotizing enterocolitis (NEC) in a neonatal intensive care unit with liberal transfusion practices. STUDY DESIGN: A retrospective cohort study was conducted for all infants weighing <1500 g who received at least 1 packed red blood cell transfusion between January 2008 and June 2013 in a tertiary neonatal intensive care unit. The primary outcome was NEC, defined as Bell stage II or greater. The temporal association of NEC and transfusion was assessed using multivariate Poisson regression. RESULTS: The study sample included 414 very low birth weight infants who received 2889 consecutive red blood cell transfusions. Twenty-four infants (5.8%) developed NEC. Four cases of NEC occurred within 48 hours of a previous transfusion event. Using multivariate Poisson regression, we did not find evidence of a temporal association between NEC and transfusion (P = .32). CONCLUSION: There was no association between NEC and red blood cell transfusion. Our results differ from previous studies and suggest that the association between NEC and transfusion may be contextual.

Anti-Ge3 causes late-onset hemolytic disease of the newborn: the fourth case in three Hispanic families.

BACKGROUND: The Gerbich (Ge) blood group system consists of 11 antigens carried on red blood cell (RBC) membrane glycophorins C and D; of these, Ge:3 antigen is of high prevalence, and the anti-Ge3 is found to be clinically significant. CASE REPORT: A 34-week neonate born to a Hispanic mother with anti-Ge3 developed late-onset hemolysis with hyperbilirubinemia and was successfully treated with transfusions from her mother. Relevant clinical findings and laboratory results for this case are summarized and compared to three other previously reported cases; all babies were born from a mother of Hispanic ethnicity. CONCLUSION: Hemolytic disease of the fetus and new born associated with anti-Ge3 is rare but should be considered when working up a broadly reactive RBC antibody screen in women of Hispanic ethnicity. Early identification of pregnant women with anti-Ge3 is recommended for prenatal transfusion planning and close monitoring of the newborn infant for evidence of late-onset anemia.

Using an Evidence-Based Approach to EMR Implementation to Optimize Outcomes and Avoid Unintended Consequences.

Implementation of an electronic medical record (EMR) with computerized physician order entry (CPOE) can provide an important foundation for preventing harm and improving outcomes. Incentivized by the recent economic stimulus initiative, healthcare systems are implementing vendor-based EMR systems at an unprecedented rate. Accumulating evidence suggests that local implementation decisions, rather than the specific EMR product or technology selected, are the primary drivers of the quality improvement performance of these systems. However, limited attention has been paid to effective approaches to EMR implementation. In this case report, we outline the evidence-based approach we used to make EMR implementation decisions in a pragmatic structure intended for replication at other sites.

Clinical decision support in the neonatal ICU.

Clinical Decision Support (CDS) tools help the healthcare team diagnose, monitor, and treat patients more efficiently and consistently by executing clinical practice guidelines and recommendations. As a result, CDS has a direct impact on the delivery and healthcare outcomes. This review covers the fundamental concepts, as well as the infrastructure needed to create a CDS tool and examples of its use in the neonatal setting. This article also serves as a primer on what to think about when proposing the development of a new CDS tool, or when upgrading an existing one. We also highlight important elements that influence CDS development, such as informatics methodologies, data and device interoperability, and regulation.

A Data-Driven Algorithm to Recommend Initial Clinical Workup for Outpatient Specialty Referral: Algorithm Development and Validation Using Electronic Health Record Data and Expert Surveys.

BACKGROUND: Millions of people have limited access to specialty care. The problem is exacerbated by ineffective specialty visits due to incomplete prereferral workup, leading to delays in diagnosis and treatment. Existing processes to guide prereferral diagnostic workup are labor-intensive (ie, building a consensus guideline between primary care doctors and specialists) and require the availability of the specialists (ie, electronic consultation). OBJECTIVE: Using pediatric endocrinology as an example, we develop a recommender algorithm to anticipate patients' initial workup needs at the time of specialty referral and compare it to a reference benchmark using the most common workup orders. We also evaluate the clinical appropriateness of the algorithm recommendations. METHODS: Electronic health record data were extracted from 3424 pediatric patients with new outpatient endocrinology referrals at an academic institution from 2015 to 2020. Using item co-occurrence statistics, we predicted the initial workup orders that would be entered by specialists and assessed the recommender's performance in a holdout data set based on what the specialists actually ordered. We surveyed endocrinologists to assess the clinical appropriateness of the predicted orders and to understand the initial workup process. RESULTS: Specialists (n=12) indicated that <50% of new patient referrals arrive with complete initial workup for common referral reasons. The algorithm achieved an area under the receiver operating characteristic curve of 0.95 (95% CI 0.95-0.96). Compared to a reference benchmark using the most common orders, precision and recall improved from 37% to 48% (P<.001) and from 27% to 39% (P<.001) for the top 4 recommendations, respectively. The top 4 recommendations generated for common referral conditions (abnormal thyroid studies, obesity, amenorrhea) were considered clinically appropriate the majority of the time by specialists surveyed and practice guidelines reviewed. CONCLUSIONS:  An item association-based recommender algorithm can predict appropriate specialists' workup orders with high discriminatory accuracy. This could support future clinical decision support tools to increase effectiveness and access to specialty referrals. Our study demonstrates important first steps toward a data-driven paradigm for outpatient specialty consultation with a tier of automated recommendations that proactively enable initial workup that would otherwise be delayed by awaiting an in-person visit.

A new approach of H∞ filtering for combustion systems using optical instrumentation.

This paper proposes a new filtering scheme applied to a linearized model of a nonlinear representation for combustion systems, whose parameters are obtained by means of optical sensors. To ensure a robust representation regarding the chosen operation point and external disturbances variations, a linear parameter-varying (LPV) state-space representation is proposed in terms of noise disturbances and time-varying parameters affecting the plant (like the instrumentation noise and non-laminar air flow). Concerning the proposed filtering scheme, a new observer structure, which includes the incorporation of the control signal as an additional input of the filter, is proposed to assure improved stability margins and performance given in terms of the H∞ norm. The filter design method is based on a convex optimization technique and is capable to deal with unstable dynamics. A numerical experiment, whose data were obtained from an actual combustion plant, illustrates the flexibility and advantages of the method when compared with the maximum correntropy criterion based Kalman filter, the full-order filter and the standard Luenberger observer.

Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital. METHODS: The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a "membership model"; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes. RESULTS: The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data (p = 0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables (p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC = 0.71, p <0.0001) and the response distributions were significantly different (p <0.0001) for the two settings. CONCLUSION: This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.

Interactions between chemokine and mu-opioid receptors: anatomical findings and electrophysiological studies in the rat periaqueductal grey.

Opioids have immunomodulatory functions and may alter susceptibility to immune disorders. Behavioral studies also indicate that chemokines, molecules expressed by immune cells, block opioid-induced analgesia in the periaqueductal grey (PAG). Bi-directional heterologous desensitization of opioid and chemokine receptors has been described in cell systems. We report the anatomical and functional interactions of chemokine receptors with the mu-opioid receptor (MOR) in the rat brain. The chemokine receptors, CXCR4 and CX3CR1, as well as their chemokine substrates, CXCL12 and CX3CL1, are widely expressed in the central nervous system (CNS). Immunohistochemical techniques were utilized to investigate MOR-CXCR4 and MOR-CX3CR1 receptor colocalization in multiple brain areas. Our results demonstrate co-expression of these receptors on individual neurons in several regions including cingulate cortex, hippocampus, and PAG, suggesting functional receptor interactions. Whole-cell patch-clamp recordings of PAG neurons in a rat brain slice preparation were used to examine morphine or chemokine (CXCL12, CX3CL1) effects alone, or in combination on neuronal membrane properties. Morphine (10 µM) hyperpolarized and reduced input resistance of PAG neurons. CXCL12 and CX3CL1 (10 nM) had no impact on either parameter. In the presence of CXCL12, morphine's electrophysiological effects were blocked in all neurons examined, whereas with CX3CL1, morphine's effects were blocked in 57% of neurons studied. The data provide electrophysiological evidence for MOR-CXCR4 and MOR-CX3CR1 heterologous desensitization in the PAG at the single-cell level. These interactions may contribute to the limited utility of opioid analgesics for inflammatory pain treatment and supports chemokines as neuromodulators.

The effect of gp120 on morphine's antinociceptive and neurophysiological actions.

Recently, we have shown that morphine's analgesic activity can be attenuated by chemokines, specifically CCL5 and CXCL12. Because the HIV-1 coat protein, glycoprotein 120 (gp120), binds to the same receptors as do CCL5 and CXCL12, experiments were designed to investigate the effect of gp120 in the brain on antinociception induced by morphine in the cold-water (-3°C) tail-flick (CWT) and hot-plate (+54°C) tests. In addition, mu-opioid-receptor-mediated effects in brain periaqueductal grey (PAG) slices were examined with whole-cell patch-clamp recordings. The results showed that (1) pretreatment with gp120 itself (10, 25, 50, 100 or 133 ng, PAG) had no nociceptive effect in the CWT; (2) pretreatment with gp120 (25 or 100 ng) dose-dependently reduced antinociception induced by subcutaneous (sc) injection of morphine (3 or 6 mg/kg) or PAG injection of morphine (100 ng) in the CWT; (3) a PAG injection of gp120 (133 ng), given 30 min before sc injection of morphine (6 mg/kg), similarly reduced morphine antinociception in the hot-plate test; (4) the inhibitory effect of gp120 on morphine-induced antinociception in the CWT was reversed by AMD3100, an antagonist of CXCR4; (5) pretreatment of slices with gp120 (200 pM) prevented morphine (10 µM)-induced hyperpolarization and reduction of input resistance in PAG neurons. Electrophysiology studies paralleled gp120-induced desensitization of a mu-opioid-receptor-mediated response in PAG neurons at the single-cell level. These studies are the first to demonstrate that the analgesic activity of morphine can be reduced by the presence of gp120 in the PAG and that pretreatment with AMD3100 is able to restore the analgesic effects of morphine.

Development and validation of high definition phenotype-based mortality prediction in critical care units.

OBJECTIVES: The objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings. MATERIALS AND METHODS: A total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long-short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II. RESULTS: A HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM. CONCLUSIONS AND RELEVANCE: The HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP-based clinical decision tools to detect the early onset of neonatal morbidities.

Designing a bed-side system for predicting length of stay in a neonatal intensive care unit.

Increased length of stay (LOS) in intensive care units is directly associated with the financial burden, anxiety, and increased mortality risks. In the current study, we have incorporated the association of day-to-day nutrition and medication data of the patient during its stay in hospital with its predicted LOS. To demonstrate the same, we developed a model to predict the LOS using risk factors (a) perinatal and antenatal details, (b) deviation of nutrition and medication dosage from guidelines, and (c) clinical diagnoses encountered during NICU stay. Data of 836 patient records (12 months) from two NICU sites were used and validated on 211 patient records (4 months). A bedside user interface integrated with EMR has been designed to display the model performance results on the validation dataset. The study shows that each gestation age group of patients has unique and independent risk factors associated with the LOS. The gestation is a significant risk factor for neonates < 34 weeks, nutrition deviation for < 32 weeks, and clinical diagnosis (sepsis) for ≥ 32 weeks. Patients on medications had considerable extra LOS for ≥ 32 weeks' gestation. The presented LOS model is tailored for each patient, and deviations from the recommended nutrition and medication guidelines were significantly associated with the predicted LOS.

Axonal Conduction Velocity in CA1 Area of Hippocampus is Reduced in Mouse Models of Alzheimer's Disease.

The timing of action potentials arrival at synaptic terminals partially determines integration of synaptic inputs and is important for information processing in the CNS. Therefore, axonal conduction velocity (VC) is a salient parameter, influencing the timing of synaptic inputs. Even small changes in VC may disrupt information coding in networks requiring accurate timing. We recorded compound action potentials in hippocampal slices to measure VC in three mouse models of Alzheimer's disease. We report an age-dependent reduction in VC in area CA1 in two amyloid-ß precursor protein transgenic mouse models, line 41 and APP/PS1, and in a tauopathy model, rTg4510.

Early experiences with combined fellowship training in clinical informatics.

Given the ubiquitous nature of information systems in modern health care, interest in the pursuit of formal training in clinical informatics is increasing. This interest is not restricted to generalists-informatics training is increasingly being sought by future subspecialists. The traditional structure of Accreditation Council on Graduate Medical Education subspecialty training requires completion of both clinical and clinical informatics fellowship programs, and understandably lacks appeal due to the time commitment required. One approach to encourage clinical informatics training is to integrate it with clinical fellowships in order to confer dual-board eligibility. In this perspective, we describe 3 successful petitions for combined training in clinical informatics in order to support other programs and the American Board of Preventive Medicine in establishing pathways for training subspecialists in clinical informatics.