Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing

Objective

Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer – impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.

Materials and Methods

Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we train deep neural network models to predict the onset of 6 endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single-task (ST) models with naive multitask and SeqSNR in terms of discriminative performance and label efficiency.

Results

SeqSNR showed a modest yet statistically significant performance boost across 4 of 6 tasks compared with ST and naive multitasking. When the size of the training dataset was reduced for a given task (label efficiency), SeqSNR outperformed ST for all cases showing an average area under the precision-recall curve boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels, respectively.

Conclusions

The SeqSNR architecture shows superior label efficiency compared with ST and naive multitasking, suggesting utility in scenarios in which endpoint labels are difficult to ascertain.

A clinically applicable approach to continuous prediction of future acute kidney injury

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2-17 and using acute kidney injury-a common and potentially life-threatening condition18-as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.

Tweeting does not preclude the ability to communicate

I share the frustration of your correspondent (letters April 11) who deplores the use of mobile phones and social networking sites such as Twitter and Facebook in the workplace. I also agree that their use should be restricted in the clinical area.

"Like a trip to McDonalds": a grounded theory study of patient experiences of day surgery

BACKGROUND

The amount and complexity of (ambulatory) day surgery is rapidly expanding internationally. Nurses have a responsibility to provide quality care for day surgery patients. To do this they must understand all aspects of the patient experience. There is dearth of research into day surgery using a sociological frame of reference.

OBJECTIVE

The study investigated patients' experiences of day surgery using a sociological frame of reference.

DESIGN

A qualitative study using the grounded theory approach was used.

SETTING

The study was based in two day surgery units in two urban public hospitals in the United Kingdom.

PARTICIPANTS

145 patients aged 18-70 years and 100 carers were purposely selected from the orthopaedic, ear nose and throat and general surgical lists. They were all English speaking and were of varied socio-economic background.

METHODS

The data was collected from 2004 to 2006. Semi-structured interviews were conducted on three occasions: before surgery, 48 h following surgery and one month following discharge. Permission was received from the Local Research Ethics Committee. Analysis of the data involved line-by-line analysis, compilation of key words and phrases (codes) and constant comparison of the codes until categories emerged.

FINDINGS

Patients liked day surgery and placed it within the wider societal context of efficiency and speed. Time was a major issue for them. They wished surgery, like all other aspects of their life to be a speedy process. They likened it to a McDonald's experience with its emphasis on speed, predictability and control.

CONCLUSION

This study throws new light on patient experiences and offers an understanding of day surgery against a western culture which emphasises the importance of speed and efficiency. It is a popular choice for patients but at times it can be seen to be a mechanistic way of providing care. The implications for nurses to provide education and information to add to the quality of the patient experience are discussed.

The hospital revolution John Riddington Young The hospital revolution Metro £9.99 243 978184454595 [Formula: see text]

THIS BOOK appears to have been born out of the sense of frustration of its authors, who, as doctors, must cope with the current management ethos of the NHS.

Pre-registration student nurses' perceptions of the day surgery unit

An increasing number of complex surgical interventions are now taking place on a day case basis with some surgical specialities able to perform 80% of their elective surgery as day surgery. It is important that student nurses are exposed to clinical practice within the day surgery unit. Some students, for a variety of reasons, exhibit a reluctance to experience a day-surgery placement. The writer describes a programme of study which takes place before the students take up their placements to demonstrate that day surgery offers many opportunities for the delivery of highly skilled, specialised nursing care. Day Surgery Nursing is emerging as a speciality in its own right. The clinical skills of the nurse are required alongside the interpersonal, informational and psychological care-giving skills to ensure safety and comfort for the patient whilst in the unit and transfer home where responsibility for care, normally performed by nurses, now lies with the patients and their carers.