Name
Title | ||
|---|---|---|
Iddsam: An Integrated Disease Diagnosis And Severity Assessment Model For Intensive Care Units |
Abstract | ||
|---|---|---|
People are admitted to intensive care units (ICUs) because they need complete support for failing organ systems, constant monitoring, routine nursing care, and treatment. A critical or intensive illness is different from conventional or chronic diseases that most people are likely to have previously encountered. Such an illness is often unexpected and without warnings and can suddenly strike the previously fit. High levels of treatment and support are generally required to prevent life-threatening complications for the patents. Two of the most noticeable actions during an ICU stay are disease diagnosis and severity assessment of the patients. Unlike the majority of previous approaches where diagnosis and severity assessment are studied separately, we treat these actions as two tasks in an integrated procedure that clinicians must be able to quickly and accurately conduct such that patients are given the best possible chance for therapeutic success. In this paper, we propose an integrated disease diagnosis and severity assessment model (IDDSAM) to diagnose and assess diseases. Moreover, accompanying the prediction, we also provide an evidence-based explanation. IDDSAM is a multisource multitask model that is based on an attention mechanism and utilizes shareable information from laboratory tests, bedside monitoring, and complications to support patients severity assessment and in-hospital disease diagnoses. We use 50,430 ICU cases consisting of 46,520 patients from 50 kinds of diseases over nine classifications to evaluate our proposed model. The experimental results demonstrated that our model outperforms the existing state-of-the-art mortality and diagnosis prediction framework by 3.79 on average in terms of accuracy for the mortality prediction tasks and by 14.51 on average for the diagnosis tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2967417 | IEEE ACCESS |
Keywords | DocType | Volume |
Healthcare, data mining, disease diagnosis, mortality prediction, multisource multitask learning | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenkun Shi | 1 | 12 | 3.50 |
| Wanli Zuo | 2 | 342 | 42.73 |
| Shining Liang | 3 | 3 | 2.05 |
| Xianglin Zuo | 4 | 0 | 1.69 |
| Yue Lin | 5 | 65 | 18.95 |
| Xue Li | 6 | 2196 | 186.96 |