OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data. MATERIALS AND METHODS: We model a disease source as an unobserved root node in the causal graph of observed EHR variables (laboratory test results, medication exposures, billing codes, and demographics), and a signature as the set of downstream effects that a given source has on those observed variables. We used probabilistic independence to infer 2000 sources and their signatures from 9195 variables in 630,000 cross-sectional training instances sampled at random times from 269,099 longitudinal patient records. We evaluated the learned sources by using them to infer and explain the causes of benign vs. malignant pulmonary nodules in 13,252 records, comparing the inferred causes to an external reference list and other medical literature. We compared models trained by three different algorithms and used corresponding models trained directly from the observed variables as baselines. RESULTS: The model recovered 92% of malignant and 30% of benign causes in the reference standard. Of the top 20 inferred causes of malignancy, 14 were not listed in the reference standard, but had supporting evidence in the literature, as did 11 of the top 20 inferred causes of benign nodules. The model decomposed listed malignant causes by an average factor of 5.5 and benign causes by 4.1, with most stratifying by disease course or treatment regimen. Predictive accuracy of causal predictive models trained on source expressions (Random Forest AUC 0.788) was similar to (p = 0.058) their associational baselines (0.738). DISCUSSION: Most of the unrecovered causes were due to the rarity of the condition or lack of sufficient detail in the input data. Surprisingly, the causal model found many patients with apparently undiagnosed cancer as the source of the malignant nodules. Causal model AUC also suggests that some sources remained undiscovered in this cohort. CONCLUSION: These promising results demonstrate the potential of using probabilistic independence to disentangle complex clinical signatures from noisy, asynchronous, and incomplete EHR data that represent the confluence of multiple simultaneous conditions, and to identify patient-specific causes that support precise treatment decisions.

We introduce a method to optimize on a patient-specific basis the kinematic design of the Continuum Reconfigurable Incisionless Surgical Parallel (CRISP) robot, a needle-diameter medical robot based on a parallel structure that is capable of performing minimally invasive procedures. Our objective is to maximize the ability of the robot's tip camera to view tissue surfaces in constrained spaces. The kinematic design of the CRISP robot, which greatly influences its ability to perform a task, includes parameters that are fixed before the procedure begins, such as entry points into the body and parallel structure connection points. We combine a global stochastic optimization algorithm, Adaptive Simulated Annealing (ASA), with a motion planner designed specifically for the CRISP robot. ASA facilitates exploration of the robot's design space while the motion planner enables evaluation of candidate designs based on their ability to successfully view target regions on a tissue surface. By leveraging motion planning, we ensure that the evaluation of a design only considers motions which do not collide with the patient's anatomy. We analytically show that the method asymptotically converges to a globally optimal solution and demonstrate our algorithm's ability to optimize kinematic designs of the CRISP robot on a patient-specific basis.

Continuum Reconfigurable Incisionless Surgical Parallel (CRISP) robots consist of multiple needle-diameter flexible instruments that are assembled into a parallel structure inside the human body. With a camera placed at the tip of one of the instruments, the CRISP robot can be used to inspect anatomical sites in constrained body cavities in a minimally invasive manner. We introduce a motion planner for CRISP robots that computes manipulations of the flexible instruments outside the body such that the camera can visually inspect a user-specified site of clinical interest inside the body. Our sampling-based motion planner ensures avoidance of collisions with anatomical obstacles inside the body, enforces remote-center-of-motion constraints on the instrument's entry points into the body, and efficiently handles the expensive computation of CRISP robot kinematics. We also extend the motion planner to estimate the set of points inside a body cavity that can be visually inspected by the camera of a CRISP robot for a given setup. We demonstrate our method in a simulated endoscopic medical procedure in the pleural space around a lung.

We propose a new class of robotic device for minimally-invasive surgery that lies at the intersection of continuum, parallel, and reconfigurable robotics. This Continuum Reconfigurable Incisionless Surgical Parallel (CRISP) paradigm involves the use of multiple needle-diameter devices inserted through the skin and assembled into parallel structures inside the body. The parallel structure can be reconfigured inside the patient's body to satisfy changing task requirements such as reaching initially inaccessible locations or modifying mechanical stiffness for manipulation or palpation. Another potential advantage of the CRISP concept is that many small (needle-sized) entry points into the patient may be preferable in terms of both patient healing and cosmesis to the single (or multiple) larger ports needed to admit current surgical robots. This paper presents a mechanics-based model for CRISP forward and inverse kinematics, along with experimental validation.