PiMS: A Pre-ML Labelling Tool

Machine Learning (ML) techniques in clinical decision support systems are scarce due to the limited availability of clinically validated and labelled training data sets. We present a framework to (1) enable quality controls at data submission toward ML appropriate data, (2) provide in-situ algorithm assessments, and (3) prepare dataframes for ML training and robust stochastic analysis. We developed and evaluated PiMS (Pandemic Intervention and Monitoring Systems): a remote monitoring solution for patients that are Covid-positive. The system was trialled at two hospitals in Melbourne, Australia (Alfred Health and Monash Health) involving 109 patients and 15 clinicians.

PiMS: A Pre-ML Labelling Tool

Machine Learning (ML) techniques in clinical decision support systems are scarce due to the limited availability of clinically validated and labelled training data sets. We present a framework to (1) enable quality controls at data submission toward ML appropriate data, (2) provide in-situ algorithm assessments, and (3) prepare dataframes for ML training and robust stochastic analysis. We developed and evaluated PiMS (Pandemic Intervention and Monitoring Systems): a remote monitoring solution for patients that are Covid-positive. The system was trialled at two hospitals in Melbourne, Australia (Alfred Health and Monash Health) involving 109 patients and 15 clinicians. © 2022 IEEE.

The feasibility and ongoing use of electronic decision support to strengthen the implementation of IMCI in KwaZulu-Natal, South Africa.

BACKGROUND: Continued efforts are required to reduce preventable child deaths. User-friendly Integrated Management of Childhood Illness (IMCI) implementation tools and supervision systems are needed to strengthen the quality of child health services in South Africa. A 2018 pilot implementation of electronic IMCI case management algorithms in KwaZulu-Natal demonstrated good uptake and acceptance at primary care clinics. We aimed to investigate whether ongoing electronic IMCI implementation is feasible within the existing Department of Health infrastructure and resources. METHODS: In a mixed methods descriptive study, the electronic IMCI (eIMCI) implementation was extended to 22 health facilities in uMgungundlovu district from November 2019 to February 2021. Training, mentoring, supervision and IT support were provided by a dedicated project team. Programme use was tracked, quarterly assessments of the service delivery platform were undertaken and in-depth interviews were conducted with facility managers. RESULTS: From December 2019 - January 2021, 9 684 eIMCI records were completed across 20 facilities, with a median uptake of 29 records per clinic per month and a mean (range) proportion of child consultations using eIMCI of 15% (1-46%). The local COVID-19-related movement restrictions and epidemic peaks coincided with declines in the monthly eIMCI uptake. Substantial inter- and intra-facility variations in use were observed, with the use being positively associated with the allocation of an eIMCI trained nurse (p < 0.001) and the clinician workload (p = 0.032). CONCLUSION: The ongoing eIMCI uptake was sporadic and the implementation undermined by barriers such as low post-training deployment of nurses; poor capacity in the DoH for IT support; and COVID-19-related disruptions in service delivery. Scaling eIMCI in South Africa would rely on resolving these challenges.

Minimization of the Drug and Gene Interactions in Polypharmacy Therapies Augmented with COVID-19 Medications.

Medications Dexamethasone, Remdesivir or Colchicine, used to treat COVID-19 patients, have significant interactions with other medications and the human genome. The study presented in this paper investigates how to use the Personalized Medicine Therapy Optimization Method (PM-TOM) to minimize these interactions in polypharmacy therapies of COVID-19 patients. We applied PM-TOM on the EMR database of Harvard Personal Genome Project (PGP), drug database DrugBank and Comprehensive Toxicogenomics Database (CTD) to analyze polypharmacy therapies augmented with these medications. The main finding is that these COVID-19 medications significantly increase the drug and gene interactions in partially optimized (or unoptimized) therapies, which is not the case in the fully optimized ones. For example, the test results show that in polypharmacy treatments for patients having between 3 and 8 conditions, the average number of drug and gene interactions in partially optimized therapies ranges from 3 to 18 after adding Remdesivir, 4.3 to 20 Colchicine, and 4.7 to 23 Dexamethasone. On the other hand, these interactions in fully optimized therapies range only 0.6 to 5.2, 1.2 to 7, and 2.7 to 11, respectively. These results suggest that polypharmacy therapies should be carefully examined before adding these medications. This recommendation applies to all other situations when polypharmacy patients may conduct new serious conditions, such as COVID-19, requiring additional medications with a high number of drug and gene interactions.