1,690 research outputs found
Drug prescription support in dental clinics through drug corpus mining
The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients
Drug prescription support in dental clinics through drug corpus mining
The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients
Investigating the prevalence and causes of prescribing errors in general practice : the PRACtICe Study
Report prepared by the University of Nottingham, University of Reading and University of Hertfordshir
Learning Prescriptive ReLU Networks
We study the problem of learning optimal policy from a set of discrete
treatment options using observational data. We propose a piecewise linear
neural network model that can balance strong prescriptive performance and
interpretability, which we refer to as the prescriptive ReLU network, or
P-ReLU. We show analytically that this model (i) partitions the input space
into disjoint polyhedra, where all instances that belong to the same partition
receive the same treatment, and (ii) can be converted into an equivalent
prescriptive tree with hyperplane splits for interpretability. We demonstrate
the flexibility of the P-ReLU network as constraints can be easily incorporated
with minor modifications to the architecture. Through experiments, we validate
the superior prescriptive accuracy of P-ReLU against competing benchmarks.
Lastly, we present examples of interpretable prescriptive trees extracted from
trained P-ReLUs using a real-world dataset, for both the unconstrained and
constrained scenarios.Comment: 17 pages, 6 figures, accepted at ICML 2
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