DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

2022 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2022 SDSU Data Science Symposium

2022 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2022 SDSU Data Science Symposium

2022 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1003/thumbnail.jp

The reading of handwriting: An evaluation of Chinese written by CFL learners.

This paper describes two experiments that first explore the potential role of Chinese character writing in character visual recognition, and then examine different evaluative responses towards the quality of pinyin and character handwriting. Taken together, the results suggest that drawing Chinese characters privileges them in memory in a way that facilitates their subsequent visual recognition. This is true even when the congruency of the recognition response and other potential confounds are controlled for. In terms of the writing quality, the reader’s empathy effect can be found for handwritten characters but not pinyin, since the handwritten characters tended to be rated more highly than pinyin. The experience of an evaluator also has an impact on the evaluation of writing quality. The pedagogical implications for Chinese as a foreign language (CFL) are highlighted at the end of the paper, in particular those relating to curriculum design and teacher training

The reading of handwriting: an evaluation of Chinese written by CFL learners

This paper describes two experiments that first explore the potential role of Chinese character writing in character visual recognition, and then examine different evaluative responses towards the quality of pinyin and character handwriting. Taken together, the results suggest that drawing Chinese characters privileges them in memory in a way that facilitates their subsequent visual recognition. This is true even when the congruency of the recognition response and other potential confounds are controlled for. In terms of the writing quality, the reader’s empathy effect can be found for handwritten characters but not pinyin, since the handwritten characters tended to be rated more highly than pinyin. The experience of an evaluator also has an impact on the evaluation of writing quality. The pedagogical implications for Chinese as a foreign language (CFL) are highlighted at the end of the paper, in particular those relating to curriculum design and teacher training