Robotic Process Automation Based Data Extraction from Handwritten Medical Forms.

This paper proposes to create an RPA(robotic process automation) based software robot that can digitalize and extract data from handwritten medical forms. The RPA robot uses a taxonomy that is specific for the medical form and associates the extracted data with the taxonomy. This is accomplished using UiPath studio to create the robot, Google Cloud Vision OCR(optical character recognition) to create the DOM (digital object model) file and UiPath machine learning (ML) API to extract the data from the medical form. Due to the fact that the medical form is in a non-standard format a data extraction template had to be applied. After the extraction process the data can be saved into databases or into a spreadsheets.

Developing Prediction Models for COVID-19 Outcomes: A Valuable Tool for Resource-Limited Hospitals.

Purpose: Coronavirus disease is a global pandemic with millions of confirmed cases and hundreds of thousands of deaths worldwide that continues to create a significant burden on the healthcare systems. The aim of this study was to determine the patient clinical and paraclinical profiles that associate with COVID-19 unfavourable outcome and generate a prediction model that could separate between high-risk and low-risk groups. Patients and Methods: The present study is a multivariate observational retrospective study. A total of 483 patients, residents of the municipality of Timișoara, the biggest city in the Western Region of Romania, were included in the study group that was further divided into 3 sub-groups in accordance with the disease severity form. Results: Increased age (cOR=1.09, 95% CI: 1.06-1.11, p<0.001), cardiovascular diseases (cOR=3.37, 95% CI: 1.96-6.08, p<0.001), renal disease (cOR=4.26, 95% CI: 2.13-8.52, p<0.001), and neurological disorder (cOR=5.46, 95% CI: 2.71-11.01, p<0.001) were all independently significantly correlated with an unfavourable outcome in the study group. The severe form increases the risk of an unfavourable outcome 19.59 times (95% CI: 11.57-34.10, p<0.001), while older age remains an independent risk factor even when disease severity is included in the statistical model. An unfavourable outcome was positively associated with increased values for the following paraclinical parameters: white blood count (WBC; cOR=1.10, 95% CI: 1.05-1.15, p<0.001), absolute neutrophil count (ANC; cOR=1.15, 95% CI: 1.09-1.21, p<0.001) and C-reactive protein (CRP; cOR=1.007, 95% CI: 1.004-1.009, p<0.001). The best prediction model including age, ANC and CRP achieved a receiver operating characteristic (ROC) curve with the area under the curve (AUC) = 0.845 (95% CI: 0.813-0.877, p<0.001); cut-off value = 0.12; sensitivity = 72.3%; specificity = 83.9%. Conclusion: This model and risk profiling may contribute to a more precise allocation of limited healthcare resources in a clinical setup and can guide the development of strategies for disease management.

The Consequence of Repetitive Heavy Object Lifting on the Normal Standing Posture of Factory Workers.

Low back pain is one of the most common physical symptom and is frequently related with an abnormal body posture. It may be caused by poor upper body and limb coordination; repetitive lifting of heavy objects or poor working are ergonomics. This study analysis the consequence of repetitive heavy lifting on the normal standing posture of factory workers. To asses the posture malformations the Microsoft Kinect sensor was used to obtain postural data from 88 factory workers. The study has shown that more than 90% of the study group has some sort of postural malformation and lower back pain.

Computer Assisted Oswestry Disability Questionnaire Evaluation Using Fuzzy Inference Systems.

This paper proposes the evaluation of the Oswestry Disability Index (ODI) using a fuzzy inference system. The ODI is used to evaluate the impact of the low back pain on the patient's quality of life. The patient grades from 0 to 5 a number of 10 questions regarding usual daily activities. At the end a mathematical formula is used to calculate the degree of the impact/disability. It has been observed that this method easily can generate false positive results due to the fact that patients purposely try to score a higher degree so that they can benefit from advantages offered by the medical system. To eliminate the false positive results a fuzzy inference system was developed which can identify the contradictory input data and warn the medical staff about a possible erroneous result.

Measuring the Negative Impact of Long Sitting Hours at High School Students Using the Microsoft Kinect.

This paper suggests the usage of the Microsoft Kinect to detect the onset of the scoliosis at high school students due to incorrect sitting positions. The measurement is done by measuring the overall posture in orthostatic position using the Microsoft Kinect. During the measuring process several key points of the human body are tracked like the hips and shoulders to form the postural data. The test was done on 30 high school students who spend 6 to 7 hours per day in the school benches. The postural data is statistically processed by IBM Watson's Analytics. From the statistical analysis we have obtained that a prolonged sitting position at such young ages affects in a negative way the spinal cord and facilitates the appearance of malicious postures like scoliosis and lordosis.

A Virtual Patient Representation in the Medical Rehabilitation Domain.

This paper proposes a virtual patient (VP) for the medical rehabilitation domain using the digital representation of the real life patient's matchstick skeleton. This virtual patient is used to analyze and track the recovery of the orthopedic patient with malicious posture problems. The creation of the digital patient was realized using a markerless depth camera, the Microsoft Kinect. The gathered data was saved in to a BVH type motion capture file. This file records not only the skeletal structure of the patient but its movements as well from witch the adduction, rotation and flexion angles of the joints can be analyzed. The data is stored in structured text format making it suitable to be used in telemedicine. The results confirm the utility and usability of the digital patient in clinical reasoning and in educational applications.