Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. patient independence while performing several activities of daily living such as walking SC 57461A grooming and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported. [3] used linear regression to predict discharge FIM motor scores for 131 first-stroke patients at an inpatient rehabilitation hospital. Features used for prediction included admission data included age days since onset of the stroke to admission admission FIM cognitive and motor scores and the reciprocal of the admission FIM motor score. The regression models yielded correlations of = 0.89 for a training group and = 0.93 for a validation group. Similar studies predicting FIM scores using only clinical SC 57461A predictors include Matsugi [4] Jeremic [5] Fujiwara [6] Tsuji [7] and Jeong [8]. Jeong and colleagues predicted discharge FIM to investigate the differences between two stroke groups: 4 311 patients admitted to acute hospitals ([9] investigated the predictive ability of admission FIM scores of patients with stroke Rabbit Polyclonal to ADAM10. (N = 286) to determine functional independence. Independence was classified as either completely dependent/requiring maximal assistance moderately dependent/requiring minimal assistance or completely independent/requiring supervision. The study concluded the motor and cognitive scores of the FIM are valid predictors of functional independence whereas the individual FIM tasks alone are not useful predictors. In addition to predicting clinical assessments scores such as the FIM a fair amount of research has been performed to predict individual patient length of stay [10]-[13]. Tan [10] considered motor function on admission and the effects of patients’ socioeconomic status and family structure on LOS for patients with stroke. Franchignoni [11] found individual FIM task scores on admission to be strong predictors of patients’ LOS with the tasks related to transfers having the highest predictive ability. Brosseau [12] discovered that age functional status at one week after admission perceptual status and balance status accounted for 43.6% of the total variance in the rehabilitation LOS for stroke patients. Furthermore functional status at admission rehabilitation program motor status communication problems and medical complications were SC 57461A indirect predictors of LOS. B. Technology-based Predictors In addition to utilizing clinical metrics several studies have investigated mapping technology-based measurements onto clinical assessment scores. Zariffa [14] considered the relationship between robot-collected kinematic data and the graded redefined assessment of strength stability and prehension action research arm test (ARAT) and spinal cord independence measure. Olesh [15] collected data from a Kinect sensor and mapped it to the Fugl-Meyer assessment (FMA) and ARAT. Similarly Wang [16] mapped accelerometer data from upper arm movements to the FMA for shoulder-elbow. Finally Simila [17] analyzed lower-back accelerometer data to estimate SC 57461A Berg balance scale scores for identifying subjects with high or low risk of falling. The aforementioned studies have primarily examined the relationships between technology-based metrics and associated clinical rating scales. These studies do not utilize collected data to project into the future and predict discharge assessment scores. On the other hand Mostafavi [18] predicted several clinical scores using metrics collected from the kinensiological instrument for normal and.