We report the design and implementation of an affordable bedside device, Neo, that is capable of acquiring vital data in real time by integrating with diverse devices connected to newborns in neonatal intensive care units (NICU). NICUs are equipped with multiple vital sign monitoring devices that are connected to the premature newborn and acquire a few gigabytes of data every day. The continuous vital data from these devices are manually documented every hour. This introduces errors and loses a large amount of high-resolution data. The intermittent documentation of physiological data also makes it difficult for clinicians to visualize and detect trends of diagnostic utility. Neo is built on an affordable IoT platform that aggregates and sends the real-time data to a cloud-based big data platform called iNICU [29,30]. Apart from minimizing documentation errors, the device enables data acquisition at sufficiently real-time rates as to indicate the current status of all patients at the NICU. Neo automates immediate vital sign status and past trends as graphs or charts that the doctor and nurses can view from anywhere through the Internet. Physiological signal and clinical parameters from Neo is used to score different diseases like sepsis, respiratory distress syndrome (RDS), necrotizing enterocolitis (NEC) and retinopathy of prematurity (ROP). This score predicts the physiological health of the newborn and aid clinicians in decision-making ensuring timely intervention.

Premature neonates hospitalized in the neonatal intensive care unit (NICU) have high nutritional needs to ensure optimal growth and development. Monitoring adherence to neonatal nutritional guidelines and providing personalized nutritional recommendations are essential for promoting their health. However, ensuring consistent adherence to these guidelines is challenging. To address this, we developed a clinical decision support system using an ontology and rule-based approach to offer personalized nutrition recommendations for preterm infants in the NICU. The Nutrition Recommendation Ontology (NRO) was developed, incorporating 121 classes, 366 axioms, and 157 semantic rules based on standard nutrition guidelines and retrospective data from 601 NICU patients, using a cumulative 8460 patient-days data collected from a single center between 2019-2021. While the ontology represented the conceptual knowledge, the rules encoded the procedural knowledge. The integrated NRO-based system serves as a reasoning engine, enabling the generation of patient-specific feeding recommendations and assisting in identifying deviations from established guidelines. To validate its efficacy, the NRO system was tested on 10 sample case studies and achieved 98% accuracy, as assessed by a panel of neonatologists. Our findings indicate that the NRO-based clinical decision support system can provide accurate personalized nutrition recommendations and assess guideline adherence in the NICU. With further real-world validation, we anticipate that this approach could significantly improve nutrition delivery, prevent malnutrition, and ultimately improve outcomes for preterm infants.

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.

Critical care units (CCUs) with extensive use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like clinical information system and laboratory information management system. These systems are proprietary, allow limited access to their database and, have the vendor-specific clinical implementation. In this study, we focus on developing an open-source web-based meta-data repository for CCU representing stay of the patient with relevant details. After developing the web-based open-source repository named data dictionary (DD), we analyzed prospective data from 2 sites for 4 months for data quality dimensions (completeness, timeliness, validity, accuracy, and consistency), morbidity, and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators. DD with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover the clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 88% completeness, 97% accuracy, 91% timeliness, and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (P < 0.05). This study documents DD for standardized data collection in CCU. DD provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.

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.