Document Type
Dissertation
Date of Award
8-31-2020
Degree Name
Doctor of Philosophy in Mathematical Sciences - (Ph.D.)
Department
Mathematical Sciences
First Advisor
Yi Chen
Second Advisor
Dantong Yu
Third Advisor
Dimitri Theodoratos
Fourth Advisor
Senjuti Basu Roy
Fifth Advisor
Vincent Oria
Abstract
Online Health communities (OHCs), Electronic Health Records (EHRs) and Claims data contain rich patient information. Accurate identification of patient's health conditions is critical for providing effective and safe health care service and for trustable knowledge discovery. This dissertation proposes several deep-learning based techniques on identifying patient health conditions and predicting adverse drug event (ADE) risks using various health data.
First, this dissertation presents a novel patient experience mining model to differentiate the data expressing user experiences from the one describing hearsays. Indeed, OHCs contain information with varying degrees of quality. Identifying information that describes patient health experience from OHCs is important for trustable knowledge discovery and effective recommendation. The proposed model holistically captures linguistic features of text, user information and context information to effectively classify patient experience in OHC.
The second problem discussed in the dissertation is to accurately identify patient medical conditions in an EHR system. This is the basis of precisely documenting patient status, coding for billing, and supporting data-driven clinical decision making. However, patient disease information is often not fully captured in structured EHR systems, but may be documented in unstructured clinical notes. Not all disease mentions in clinical notes actually refer to the patient's condition. The dissertation presents a two-step workflow for identifying patient diseases from clinical notes and a deep-learning model for disease mention classification.
Last, this dissertation presents proposed techniques for predicting a patient's risk of experiencing an ADE in the target ADE list if being given a target drug using medical claims data. Such risk assessment can help physicians to choose safer treatment among alternatives. A hierarchical neural network model that combines the characteristic of claim codes and encounter time relation is proposed.
Recommended Citation
Shi, Jinhe, "Accurate patient health condition identification and risk prediction from health data" (2020). Dissertations. 1909.
https://digitalcommons.njit.edu/dissertations/1909
