Weakly Supervised Learning for Categorization of Medical Inquiries for Customer Service Effectiveness.

With the growing unstructured data in healthcare and pharmaceutical, there has been a drastic adoption of natural language processing for generating actionable insights from text data sources. One of the key areas of our exploration is the Medical Information function within our organization. We receive a significant amount of medical information inquires in the form of unstructured text. An enterprise-level solution must deal with medical information interactions via multiple communication channels which are always nuanced with a variety of keywords and emotions that are unique to the pharmaceutical industry. There is a strong need for an effective solution to leverage the contextual knowledge of the medical information business along with digital tenants of natural language processing (NLP) and machine learning to build an automated and scalable process that generates real-time insights on conversation categories. The traditional supervised learning methods rely on a huge set of manually labeled training data and this dataset is difficult to attain due to high labeling costs. Thus, the solution is incomplete without its ability to self-learn and improve. This necessitates techniques to automatically build relevant training data using a weakly supervised approach from textual inquiries across consumers, healthcare professionals, sales, and service providers. The solution has two fundamental layers of NLP and machine learning. The first layer leverages heuristics and knowledgebase to identify the potential categories and build an annotated training data. The second layer, based on machine learning and deep learning, utilizes the training data generated using the heuristic approach for identifying categories and sub-categories associated with verbatim. Here, we present a novel approach harnessing the power of weakly supervised learning combined with multi-class classification for improved categorization of medical information inquiries.

HSV vector-delivery of GDNF in a rat model of PD: partial efficacy obscured by vector toxicity.

Herpes simplex virus (HSV)-derived vectors have been suggested for potential use in gene therapy for Parkinson's disease (PD). HSV naturally infects adult neuronal cells and possesses a large genome for the insertion of transgenes. In the present study, we have used two different HSV constructs to deliver glial cell line-derived neurotrophic factor (GDNF) to the striatum, and to assess the neuroprotective effects of the GDNF product in an intrastriatal 6-hydroxydopamine lesion model. One construct is blocked for IE gene expression whereas the other is deleted in the thymidine kinase gene. Both constructs induced a significant protection of the dopaminergic neurons in the substantia nigra from the lesions, whereas only one induced a transient behavioural recovery in amphetamine-induced rotation. Unexpectedly, the more deleted virus caused the greater toxicity. This was found to be due to the way the vector was purified. The issue of toxicity, which might account for the variable functional effects, needs resolving prior to therapeutic application of these vectors.